DEV Community: Oluseye Jeremiah

How to Measure RAG System Performance

Oluseye Jeremiah — Sat, 28 Mar 2026 10:17:27 +0000

Your RAG demo passed every test. The dashboard showed green across the board, with answers that clearly cite source documents. A key metric called "Faithfulness" scored 0.89. Then you shipped to production. Within two weeks, 35% of users reported wrong answers. The metrics hadn't changed. The failures were real.

What happened? Test queries looked formal, "What is the enterprise pricing structure?" while production queries were casual, "How much does this thing cost?" Faithfulness, which checks whether answers rely on retrieved documents, caught the hallucinations but missed tone problems, missing context, and the dozens of ways RAG systems fail when real users show up.

Most teams add more metrics, build bigger dashboards, and measure everything, but in the end, they predict nothing. Weights & Biases found that a simple zero-shot evaluation prompt outperformed complex reasoning frameworks at 100% accuracy versus 82-90%, adding sophistication made results worse, not better. The problem isn't quantity, it's choosing the right measurements.

Engineers know evaluation is hard, and most aren't doing it well. Neptune.ai research found that many RAG product initiatives stall after the proof-of-concept stage because teams underestimate the complexity of evaluation. This article walks through selecting three to five metrics that actually predict failures: which metrics catch which problems, what each costs, and how to build monitoring that scales.

TL;DR

Most teams measure retrieval and generation but miss end-to-end user success. Systems score 0.89 on Faithfulness while 35% of users report failures because metrics don't catch tone or context mismatches. Neptune.ai found that many RAG initiatives stall after the proof-of-concept stage because teams underestimate the evaluation complexity.
Simple beats complex: Weights & Biases found zero-shot prompts hit 100% accuracy versus 82-90% for complex frameworks. Adding sophistication made results worse, not better.
Ground truth costs $50-200 per Q&A pair. Building 1,000 pairs requires $50,000-200,000. Reference-free metrics cost $0.01-0.04 per check and scale to production.
Production queries break test sets. Derive 50% from production logs, refresh quarterly, weight edge cases (5% of traffic, 40% of complaints).
Start with three metrics: Context Relevance + Faithfulness + Answer Relevance at $0.02-0.04 per query. Expand only when you hit concrete limits.

Why Generic RAG Evaluation Metrics Fail

Most RAG dashboards look convincing. Precision stays high, Faithfulness remains above 0.85, and Answer Relevance seems stable. But while the metrics show no problems, production tells a different story.

Users report incomplete answers, responses miss intent, and queries fail even though no hallucination occurs. Engineers re-run the evaluation and see the same strong numbers. The issue isn't a missing metric, it's a missing layer.

The three-layer problem

Every RAG system operates across three layers, but most evaluation pipelines cover only two.

Layer 1 (Retrieval) measures whether the system retrieved the right documents using Precision, Recall, and Mean Reciprocal Rank. These metrics assess ranking quality and coverage — if Recall drops, the system fails to surface necessary context, and if Precision drops, irrelevant documents pollute results. Retrieval metrics matter, but they don't explain why users still complain.

Layer 2 (Generation) measures whether the model used retrieved documents correctly. Faithfulness checks whether claims appear in the retrieved context, while Answer Relevance checks whether the response addresses the query. These metrics reduce hallucinations and detect context misuse, but they still miss many production failures.

Layer 3 (End-to-end user success) measures whether the answer actually helped the user. This layer covers tone, clarity, and whether the system actually completes the user's task. Automated metrics rarely capture this layer.

A system might report a Faithfulness score of 0.89 and context relevance of 0.91, yet 30-35% of production queries still fail. The model grounds its answers, retrieval works as expected, and there are no clear hallucinations. The failure stems from a query mismatch.

Most teams measure the retrieval and generation layers, but not the full end-to-end alignment. Understanding the three layers narrows the problem. The next question is which you can actually monitor in production without ground truth?

Reference-Based vs. Reference-Free

Once you recognize the three-layer structure, the question emerges, "Do you have ground truth Answers?" This limitation affects which metrics you can use, how much evaluation will cost, and whether you can monitor continuously.

Reference-based metrics compare system output against known correct answers. Context Recall, Context Precision, and Answer Correctness require labeled datasets. Their strength is stability for regression testing; they let you benchmark precisely and spot problems as models change.

However, creating high-quality ground truth typically costs $50-200 per Q&A pair for expert annotation and quality assurance, particularly for specialized domains. At this rate, a 1,000-query test set costs $50,000–200,000, so reference-based evaluation doesn't scale to continuous production monitoring.

Reference-free metrics don't require labeled answers. Faithfulness, Answer Relevance, and Context Relevance estimate correctness by comparing outputs to retrieved context. Their main advantage is that they scale easily, making them practical for ongoing production monitoring.

Most production systems need both types. Use reference-based metrics to set baselines, and reference-free metrics to monitor daily performance.

With this foundation in place, let's look at the specific metrics you'll use, what they measure, when they might fail, and which problems they help catch.

Core Metrics Explained

Most teams use whatever metrics their framework provides. The issue isn't that these metrics are wrong, but that they're often used without a clear understanding of what they measure or where they might fail. Retrieval determines which information the model receives. If retrieval fails, the generation step can't fix it.

Context Precision

Measures how many retrieved documents are relevant. If your retriever returns five documents and only two contain useful information, precision drops to 0.4.

Real failure example: an "enterprise pricing" query returns a blog post first, while the actual pricing page is ranked fifth, so the user sees incorrect information upfront. This is why Precision should be used when evaluating ranking quality, as it directly impacts the accuracy of the answers.

Context Recall

Requires you to know in advance which documents the system should retrieve for each query. This means maintaining a labeled test set where you've manually tagged, "For this question, these three documents are the correct answers."

This makes Recall valuable for regression testing: "Did our update break Retrieval?" It doesn't work for production monitoring; you can't manually label thousands of daily queries.

Context Relevance

Relies on embedding similarity to measure how close retrieved documents are to the query in the vector space. This works well for drift detection if average similarity drops over time, embeddings or indexing may be degrading. However, similarity doesn't guarantee usefulness. Treat context relevance as a monitoring signal, not a correctness guarantee.

Mean Reciprocal Rank (MRR)

Measures how high the first relevant document appears. If the first relevant result appears at position one, MRR equals 1.0. At position three, MRR equals 0.33.

> Formula: MRR = 1 / rank_of_first_relevant_result

Research suggests relevance in the top three positions predicts answer performance better than top-ten coverage.

Faithfulness

Evaluates whether the claims in a response are supported by the retrieved context. Most approaches break the answer into individual statements and verify them against the source documents. These checks typically cost between $0.01 and $0.04 apiece.

Real failure example: the system claims "coverage includes international shipping," even though the documentation only mentions domestic. Faithfulness is one of the most reliable ways to detect hallucinations, but it doesn't measure usefulness. A response can be fully grounded in the source material and still fail to help the user.

Answer Relevance

Measures whether a response actually addresses the user's question. Many implementations approach this indirectly by asking an LLM to infer the likely question from the answer, then comparing it to the original query.

The RAGAS (Retrieval-Augmented Generation Assessment Suite) paper notes that Answer Relevance often diverges from human scoring in conversational cases.

Real failure example: a user asks how to reset a password, but the system responds with an explanation of the account creation process.

Answer Correctness

Compares the model's output to a gold reference answer. It provides strong regression guarantees, but requires curated ground truth, typically costing $50 to $200 per Q&A pair. Use it when precision matters more than scale.

BLEU and ROUGE

BLEU (Bilingual Evaluation Understudy) and ROUGE (Recall-Oriented Understudy for Gisting Evaluation) were designed for machine translation and measure word overlap between generated text and reference answers. They work well for translation, but break down for RAG. Two answers can convey the same meaning with different wording and still score poorly, while a hallucinated answer that mirrors the reference phrasing may score highly. Treat these metrics as rough development signals only, not as a substitute for real evaluation in production.

Metric comparison

Cost estimates reflect approximate LLM API charges for automated evaluation calls. Metrics listed as "Free" use deterministic computation with no API dependency.

Metric	Requires ground truth?	Cost per eval	Production-ready?	Best use case
Context Precision	Document labels	$0.001-0.01	Yes	High-volume monitoring
Context Recall	Document labels	$0.01-0.02	No	Regression testing
Context Relevance	No	$0.001-0.01	Yes	Continuous monitoring
MRR	Document labels	Free	Yes	FAQ systems, search ranking
Faithfulness	No	$0.01-0.04	Yes	Hallucination detection
Answer Relevance	No	$0.01-0.02	Yes	Query-answer matching
Answer Correctness	Reference answers	$50-200	No	Benchmark testing
BLEU/ROUGE	Reference answers	Free	No	Development proxy only

Table 1: Comparison of RAG evaluation metrics by cost, ground truth requirements, and production readiness.

It's important to note that these metrics don't require gold-standard reference answers. However, they do rely on relevance labels for retrieved documents, which must be manually annotated. Only Context Relevance, Faithfulness, and Answer Relevance are truly reference-free.

LLM-as-a-Judge

At some point, most teams reach the same conclusion: "If automated metrics miss tone and alignment, why not let another LLM evaluate the output?"

This approach, known as LLM-as-a-judge, has become popular for evaluating RAG systems. It offers flexibility, requires no ground truth, and can capture nuanced reasoning. In practice, this method comes with trade-offs.

LLM-as-a-judge uses a large model like GPT-4 or Claude to evaluate another model's output. You provide criteria directly in the prompt: "Does the context support the answer"? "Does it address the user's question"? "Is the tone appropriate"?

The model returns a score or classification. This works well for nuanced checks and avoids the cost of creating labeled datasets. How reliable it is depends completely on how you design the prompts and how the model behaves.

The surprising finding

Weights & Biases evaluated multiple LLM-based approaches. A simple zero-shot prompt achieved 100% accuracy. More complex frameworks using reasoning chains scored 82-90%.

The simpler prompt outperformed the "smarter" ones. Complex reasoning chains introduced over-analysis. The judge inferred errors that didn't exist. It penalized acceptable variations and produced inconsistent results.

Making evaluations more complex doesn't always improve them. Sometimes, it actually makes them worse.

Known limitations include version dependency (GPT-4 and GPT-4o may produce different judgments), prompt sensitivity (small wording changes can shift scores by 10-15 points), and context length constraints (LLM-based evaluations struggles with long contexts).

Cost reality

Assume GPT-4o costs $0.015 per evaluation

1,000-case evaluation: $15 per metric
Five metrics: $75
Ten tuning rounds: $750
Monthly regression testing: $250/month, or $3,000 annually

For high-traffic systems, continuous evaluation can be expensive. LLM-as-a-judge doesn't remove the cost; it just moves it from labeling to inference.

LLM-as-a-judge works best for development iteration, qualitative validation, sample-based production review (10-20% traffic), and early-stage systems without ground truth. Avoid relying on it for compliance documentation, high-volume per-query evaluation, or benchmark comparisons across model versions.

Once you understand these basics, the real question becomes: Which metrics should you actually use? The answer depends on your specific use case and constraints.

Building Your Strategy

Which three to five metrics will predict failures in your system? There's no one-size-fits-all answer. Begin by identifying the type of failure you absolutely can't accept.

For Q&A chatbots facing hallucinations and intent mismatch risks, use Faithfulness (catches hallucinations), Answer Relevance (ensures query addressed), and Context Precision (reduces noise). Skip Context Recall since coverage is less important than accuracy. Add latency P95 and token cost.

For document search where ranking quality matters most, use MRR (position of first relevant result), Context Precision (clean ranking), and Context Relevance (embedding quality). Skip generation metrics since this is about search, not generating answers. Add result diversity. Qdrant research shows that top-three ranking quality correlates more strongly with outcome than broader retrieval depth.

For long-form generation facing drift in framing or emphasis, use Faithfulness (grounding check), Answer Correctness (if ground truth exists), and Context Coverage (percentage of retrieved context used in answer). Add coherence checks and regular human reviews since automated metrics can't guarantee the narrative makes sense.

For compliance/legal systems where omission is the dominant risk, use ALL retrieval metrics (complete coverage required), Faithfulness (no deviation), and Answer Correctness (requires ground truth). Add human validation and an audit trail. Reference-based evaluation and logging are essential for operations.

After identifying the failure mode, constraints become the second filter. Whether you have ground truth data changes everything.

The amount of traffic also matters. If your system handles hundreds of queries a day, you can evaluate each one with LLM-as-a-judge, but if you have tens of thousands, you'll need to use sampling. Budget is another factor. LLM-as-a-judge seems cheap per evaluation, but costs add up quickly when you use it for many metrics and rounds.

Most production RAG systems operate effectively with three core signals. Start with Context Relevance (cheap, continuous retrieval monitoring), Faithfulness (catches hallucinations), and Answer Relevance (ensures query addressed). Add operational metrics like Latency P95/P99 and token cost per query. Evaluation metric overhead should add no more than 10-20% to your base retrieval-plus-generation latency. Cost: $0.02-0.04 per evaluation.

Expand only after these stabilize: Have ground truth? Add Context Recall and Answer Correctness. Need compliance? Add human validation. Ranking matters? Add MRR. Avoid the temptation to measure everything — having too many metrics creates noise, which can obscure important changes.

Production Monitoring

Evaluation looks controlled in development. You curate test queries, control context, and metrics that behave predictably. Production removes those guarantees.

Real users introduce typos, vague phrasing, and inconsistent terminology while query distribution shifts and edge cases surface. In development, most queries look like your test set, but in production, most may not.

Three forces reshape performance: Query distribution shifts (users ask shorter, more casual questions and expect the system to infer intent), data evolves (knowledge bases update, new documents enter the index, embedding distributions change), and user expectations increase (people are less forgiving of slow responses or wrong tone than of small factual errors).

Continuous strategy

Evaluating in production needs a layered approach to monitoring.

Always On (Per-Query)

Context Relevance (low-cost drift detection)
Latency P95/P99 (infrastructure pressure)
Token cost per query (prompt creep)

Batch/Sampling

Faithfulness (nightly batch on query subset)
LLM-as-a-judge (10-20% traffic sample)
Human review (50-100 queries weekly)

Your evaluation process must adapt as traffic grows. If your system handles 500 queries a day, you can check them all. If it handles 50,000, that's not possible.

Setting alert thresholds

Set your thresholds before any incidents happen:

Context Relevance < 0.7: Retrieval drift likely
Faithfulness < 0.8: Hallucination risk increased
P95 latency > 2 seconds: Infrastructure constraints
User feedback < 4.0/5.0: Tone or completeness issues

def monitor_rag_health(query_results):
    """Production monitoring with threshold alerts"""
    # calculate_metrics expects: {'query': str, 'contexts': List[str], 'answer': str}
    # Returns: {'context_relevance': float, 'faithfulness': float, 'latency_p95': float, 'user_feedback': float}
    metrics = calculate_metrics(query_results)

    alerts = []

    if metrics['context_relevance'] < 0.7:
        alerts.append("Retrieval degrading")

    if metrics['faithfulness'] < 0.8:
        alerts.append("Hallucination risk")

    if metrics['latency_p95'] > 2.0:
        alerts.append("Infrastructure issue")

    if metrics['user_feedback'] < 4.0:
        alerts.append("UX problem")

    return alerts

Evaluation costs should grow more slowly than your traffic does. Sample 5-10% of queries for expensive metrics, cache embeddings, batch LLM evaluations overnight, and use smaller models for screening.

Framework Selection

Most teams shouldn't build an evaluation from scratch. Frameworks exist because evaluation becomes brittle quickly. Choose based on lifecycle stage, not feature count.

RAGAS

RAGAS (Retrieval-Augmented Generation Assessment Suite) introduced a structured, reference-free RAG evaluation. It formalized Faithfulness, Answer Relevance, and Context Relevance in a reusable format.

Strengths

Research-backed methodology
Native support for reference-free metrics
Clean integration with LangChain

Limitations
Limited explainability for metric failures
Sensitive to LLM version differences

Setup: 1-2 hours | Cost: Free + LLM API | Best for: Early-stage RAG validating retrieval and grounding quality

from ragas import evaluate
from ragas.metrics import faithfulness, answer_relevance
from datasets import Dataset

# Prepare evaluation data
data = {
    "question": ["What is the capital of France?"],
    "answer": ["Paris is the capital of France"],
    "contexts": [["France is a country in Western Europe with Paris as its capital"]]
}
dataset = Dataset.from_dict(data)

# Run evaluation
results = evaluate(
    dataset=dataset,
    metrics=[faithfulness, answer_relevance]
)

print(results)
# Output: {'faithfulness': 0.95, 'answer_relevance': 0.88}

RAGAS is a good choice if your main goal is structural correctness, rather than production monitoring. You can find full documentation on GitHub.

DeepEval

DeepEval approaches evaluation like test engineering. It supports CI/CD integration and automated regression testing.

Strengths

Broad metric library (50+ metrics)
Better failure inspection
Designed for automated pipelines

Limitations

Higher configuration overhead
More complex onboarding

Setup takes about 2-3 hours. It's open source, with optional paid tiers. It's best for teams that want to include evaluation in their release workflows.

TruLens

TruLens focuses on simplicity. It tracks groundedness, Context Relevance, and Answer Relevance without heavy configuration.

Strengths

Quick to deploy (under 1 hour setup)
Minimal configuration
Clear mental model

Limitations

Smaller ecosystem
Less extensible for advanced workflows
Slowed development pace following the Snowflake acquisition with ecosystem growth stalled

Arize Phoenix

Phoenix emphasizes production observability over development-only evaluation.

Strengths

OpenTelemetry integration
Trace-based debugging
Real-time monitoring

Limitations

Requires infrastructure integration
Heavier operational footprint
Best for mature systems that need large-scale drift detection

LangSmith

LangSmith integrates tightly with LangChain environments. It combines tracing with evaluation.

Strengths

Native LangChain support
Experiment tracking
Production trace inspection

Limitations

Ecosystem dependency
Less framework-agnostic

Best for teams using LangChain who are moving toward structured monitoring.

Framework comparison

Framework	Best for	Strengths	Limitations	Cost	Setup Time
RAGAS	Pure RAG evaluation	Reference-free, LangChain integration	Limited explainability	Free + LLM API	1-2 hours
DeepEval	Engineering teams	50+ metrics, CI/CD integration	Learning curve	Free + optional $49-299/mo	2-3 hours
TruLens	Getting started	3 core metrics, simple	Limited traction	Free	30 min
Arize Phoenix	Production debugging	OpenTelemetry compatible	Enterprise complexity	Usage-based	3-4 hours
LangSmith	LangChain users	Native integration	Vendor lock-in	Usage-based	1-2 hours

Table 2: Comparison of RAG evaluation frameworks by use case, features, and operational requirements.

Choose by phase

POC: RAGAS or TruLens
CI/CD integration: DeepEval
Production monitoring: Phoenix or similar observability tools
Enterprise governance: Commercial platforms with audit features

A good framework integrates smoothly, gives stable results across LLM versions, keeps costs predictable, and makes failures easy to spot.

Even with the right framework, teams often make the same mistakes. Spotting these patterns early can save you months of extra work.

Common Pitfalls

Most RAG evaluation failures follow predictable patterns.

Over-indexing on automated metrics

This happens when automated scores look healthy but users complain. A system reports Faithfulness at 0.92, but user feedback indicates responses feel robotic or miss conversational nuance. Automated metrics measure grounding but don't measure tone.

Fix: Allocate 10-20% of the evaluation budget to human review. Sample high-risk queries weekly. Use findings to adjust prompts or refine automated thresholds.

Test-production mismatch

This occurs when tests pass, but production fails at 40%. Test datasets contain formal queries: "What is the enterprise pricing structure?" Production users ask: "How much does this cost?" The distribution mismatch creates a silent evaluation failure.

Fix: Derive 50% of your test set from production logs. Refresh quarterly. Query patterns evolve faster than curated datasets.

Ignoring edge cases

Common queries work but rare queries fail 80% of the time. Edge cases represent 5% of traffic but generate 40% of complaints. Test sets skew toward frequent queries.

Fix: Ensure equal representation of query types in evaluation. Weight infrequent but high-impact scenarios appropriately.

Actian VectorAI DB Advantages

Most RAG evaluation pipelines expose queries and documents to external APIs. Embeddings travel to OpenAI, faithfulness checks route through Claude, and each evaluation step introduces data movement. For teams with compliance requirements, this setup doesn't work.

Actian VectorAI DB addresses this gap by allowing you to run all evaluation workloads on-premises. Queries remain local, documents never leave controlled infrastructure, and LLM-based evaluation executes using locally hosted models. This eliminates external API dependencies entirely.

Teams working with HIPAA-regulated data, financial records, or proprietary research can evaluate RAG systems on real production data without creating audit risk. Cloud evaluation costs scale with query volume and token count. Actian uses flat licensing with no per-query charges, making costs predictable as evaluation scales.

Development environments often use mocked dependencies and synthetic data. Actian allows testing with the same database engine production uses, ensuring retrieval latency, index behavior, and evaluation results accurately predict production performance.

Final Thoughts

More metrics don't guarantee better results. Automated scoring and human review form a more reliable system than either alone. Production queries provide better test coverage than curated datasets. Monitor continuously, not episodically.

The Weights & Biases benchmark confirmed that simple evaluation, done consistently, outperforms complex evaluation done occasionally. Build your strategy on that principle. The goal isn't choosing the trendiest framework or the most complex dashboard, it's building infrastructure that remains accurate, scalable, and cost-effective as query volume grows.

For teams building production RAG systems, start with three core metrics. Expand when you hit concrete limits, not hypothetical ones.

If you need on-premises evaluation without exposing sensitive data to external APIs, Actian VectorAI DB lets you run all evaluation workloads locally within your own infrastructure.

Why GraphQL Adoption Keeps Growing: Benefits and Limitations

Oluseye Jeremiah — Fri, 03 Oct 2025 13:55:56 +0000

REST has been the default method for designing APIs for years. It's predictable, resource-oriented, and simple enough that nearly every engineering team has used it. However, as applications grew more complex, their shortcomings became increasingly difficult to overlook. Mobile clients wanted lighter payloads. Single-page apps needed flexible queries. Teams found themselves battling over-fetching, under-fetching, and endless endpoint versions to keep features moving.
GraphQL emerged as a direct response. Instead of hard-coded endpoints, it lets clients declare exactly what data they need. That shift may sound small, but it changes the relationship between frontend and backend teams, reduces wasted network calls, and makes APIs easier to evolve.
This isn’t theoretical. Companies like GitHub, Shopify, and Netflix rely on GraphQL in production to simplify API use and scale effectively. Adoption continues to grow because GraphQL addresses recurring problems in distributed systems.
In this post, we'll explore the challenges that REST left unsolved, compare GraphQL with REST, explain how GraphQL works, and examine the benefits and limitations that drive its adoption.

The API Landscape Before GraphQL

Before GraphQL, REST was the dominant API design approach. Its resource-based model was simple: define endpoints, return JSON, and let clients assemble the data. This worked when applications were smaller and client needs were predictable.
As systems scaled, cracks appeared:

Over-fetching: Endpoints returned more data than required. A mobile app that requires only a user's name and avatar might receive the entire user object.
Under-fetching: Clients made multiple round-trips to gather related data. A dashboard fetching customers, orders, and invoices often requires three or four requests.
Versioning headaches: New features led to /v2 and /v3 endpoints, leaving teams juggling multiple versions in production.
One-size-fits-all models: REST assumed the same data served every client. Mobile, web, and IoT clients often require different shapes, which resulted in bloated responses or fragile workarounds.

These problems weren’t abstract. They appeared in every production system at scale. REST remains useful for many APIs, but teams needed a more flexible model that addressed over-fetching, under-fetching, and version churn without rewriting every client. This set the stage for GraphQL.

What GraphQL Brings to the Table

GraphQL, introduced by Facebook in 2015, directly addresses the weaknesses of REST. Instead of rigid endpoints, the client specifies the shape of the data it wants, and the server responds with that shape.
Key features include:

Strongly typed schema: Defines objects, fields, and relationships. It acts as a contract between frontend and backend, reducing guesswork and enabling evolution without breaking clients.
Single endpoint: Consolidates APIs into one entry point. Instead of /users, /orders, and /products, a single endpoint accepts declarative queries.
Declarative data fetching: Eliminates over-fetching and under-fetching. A mobile app can request only an ID, name, and avatar, while a web dashboard can query orders and invoices in a single call.
Introspection and tooling: The schema can be queried for documentation. Tools like GraphiQL and Apollo Studio make APIs self-discoverable, easing onboarding and debugging.
Frontend alignment: Frameworks like Apollo Client and Relay integrate queries into component lifecycles, fitting naturally with how teams build SPAs and mobile apps.

These qualities make GraphQL particularly effective for modern API design, where speed, flexibility, and cross-team collaboration are most crucial.

GraphQL vs REST: Why Developers Prefer It

The benefits of GraphQL adoption are practical and immediate:
Fewer requests, faster apps: By returning exactly the requested data, GraphQL reduces bandwidth use and round trips, especially valuable for mobile clients.

Faster iteration cycles: Frontend teams don't need to wait on new endpoints. If a field exists in the schema, they can query it directly.
Better developer experience: Introspection, type safety, and ecosystem support make APIs easier to explore and debug. GraphiQL offers interactive queries, while Apollo Client integrates seamlessly with React for enhanced data handling.
Strong typing for safety: IDEs offer better autocomplete, reducing runtime surprises and simplifying refactors.

Adoption by leading platforms validates these advantages. GitHub migrated large parts of its API to GraphQL to simplify complex queries. Shopify utilizes it to power storefront APIs, enabling partners to build more sophisticated apps. Netflix has written about consolidating multiple data sources under a single GraphQL schema. These examples demonstrate GraphQL in production at scale.
For developers, the appeal is clear: GraphQL reduces friction, speeds up development, and provides a more reliable contract between client and server.

GraphQL Benefits and Limitations

No technology is without tradeoffs. While GraphQL adoption grows, it brings challenges:

Complexity on the server: Resolvers must handle dynamic queries, nested relationships, and performance tuning. Poor design can lead to slow queries or denial-of-service risks.
Caching difficulties: REST benefits from simple HTTP caching by URL. GraphQL queries are unique, which complicates cache invalidation. Teams often rely on Apollo or Relay, or build custom caching layers.
Learning curve: Teams must learn schemas, resolvers, and query planning. Backends need monitoring and query cost analysis. Adoption slows if cultural and technical shifts aren’t managed.
Not always necessary: For small internal APIs with a handful of endpoints, REST is still simpler and easier to maintain. Using GraphQL where it isn’t needed adds overhead without clear benefits.
The key takeaway is that GraphQL shifts complexity. It solves recurring client-side problems but introduces new concerns on the server side. Successful adoption requires investment in schema design, performance safeguards, and developer education.

Why GraphQL Adoption Continues to Grow

Despite its trade-offs, GraphQL adoption continues to rise because it aligns with modern engineering practices.
Mature ecosystem: Servers like Apollo, Hasura, and GraphQL Helix make advanced features like schema stitching, subscriptions, and federation more accessible.

Front-end-first model: With React, Vue, and Next.js, GraphQL enables teams to colocate queries with UI components, improving maintainability.
Standardization: As more companies adopt GraphQL internally, developers recognize familiar patterns across organizations. This shared experience boosts confidence for new adopters.

In short, GraphQL isn’t perfect, but it consistently addresses over-fetching, under-fetching, and version churn while aligning with today’s API needs.

Conclusion

GraphQL’s growth isn’t about replacing REST but filling its gaps. Over-fetching, under-fetching, and rigid versioning made it difficult to deliver efficient experiences across clients. GraphQL solves these challenges by allowing clients to declare precisely what they need.
The tradeoffs—server complexity, caching, and the learning curve—are real, but so are the benefits. With robust tooling, an active ecosystem, and years of production use, GraphQL has proven its ability to handle the needs of large-scale systems.
For senior engineers and architects, the conclusion is straightforward: GraphQL isn’t a silver bullet, but when applied to the right problems, it enables APIs that are easier to evolve, more efficient for clients, and better suited to modern application development.

Building an Effective and User-Friendly Medical Chatbot with OpenAI and CometLLM: A Step-by-Step Guide

Oluseye Jeremiah — Sat, 30 Mar 2024 00:24:28 +0000

The application of artificial intelligence (AI) is transforming patient involvement and information sharing in the quickly changing field of healthcare technology.
This article walks you through building a cutting-edge Doctor Chatbot as it explores the fascinating field of conversational AI.
We will explore step-by-step directions to create an intelligent yet friendly chatbot designed for medical interactions, utilizing the potent powers of OpenAI and CometLLM.

Learn how CometLLM, a dynamic platform for machine learning experimentation, and OpenAI, a trailblazing force in AI research, are collaborating to transform the healthcare experience. This post offers a thorough road map for developers and healthcare professionals alike, covering everything from comprehending the nuances of OpenAI's cutting-edge models to building a seamless chatbot architecture.

About CometLLM

Comet's LLMOps toolbox provides customers with access to state-of-the-art prompt management innovations, including quicker iterations, better performance bottleneck diagnosis, and a visual tour of Comet's ecosystem's internal prompt chain operations.
Comet excels in accelerating progress in the following critical areas with its LLMOps tools:

Prompt History Mastery:

Keeping accurate records of prompts, responses, and chains is critical in the world of machine-learning products powered by big language models. Comet's LLMOps tool offers a very user-friendly interface for thorough, fast history tracking, and analysis.
Users can learn much about how their prompts and answers have changed over time.

#### Prompt Playground Adventure

One of the most creative features in the LLMOps toolbox is the Prompt Playground, a dynamic environment where Prompt Engineers can conduct quick explorations. This allows them to quickly test out different prompt templates and see how they affect different scenarios. During the iterative process, users are empowered to make well-informed decisions thanks to their increased experimentation agility.

#### Prompt Usage Surveillance Using paid APIs may be necessary to navigate the world of huge language models. Precise usage tracking is available at the project and experiment levels using Comet's LLMOps tool. Users may better understand API use with the help of this meticulously thorough tracking system, which makes resource allocation and optimization easier.

In summary, Comet's LLMOps toolset is an essential tool for engineers, developers, and academics who are delving into the intricacies of huge language models. Their approach is not only streamlined, but it also provides increased transparency and efficiency, which makes it easier to design and refine ML-driven apps.

Building a Doc-Bot OpenAI and CometLLM

Before delving into the intricacies of code, it's crucial to grasp the foundational components and key features of the chatbot we're about to build-DocBot. Tasked with the role of a virtual health assistant, DocBot is designed to cater to a spectrum of user needs within the realm of healthcare.

Main Components and Features:

General Health Inquiries: DocBot serves as a reliable source for users seeking information on general health and wellness. Users can ask about maintaining a healthy lifestyle, dietary recommendations, and other holistic health practices.
Advice on Common Ailments With DocBot, users can seek guidance on common health issues such as colds, headaches, and stress management. The chatbot provides practical advice, suggesting remedies and lifestyle adjustments to alleviate common ailments.
Specialized Health Tips: DocBot extends its capabilities to offer specialized advice for users with chronic conditions, mental health concerns, and those navigating the various facets of healthy aging. This personalized guidance ensures a tailored approach to individual health needs.
Emergency Situations Guidance: In critical situations, DocBot steps up as a virtual first responder, providing users with essential first-aid information for emergencies. From burns to CPR guidelines, the chatbot imparts crucial knowledge to users in times of urgency.

Guiding Principles:
Empathy: DocBot is designed to engage with users empathetically, understanding the importance of human touch even in virtual interactions. The chatbot responds with compassion, acknowledging the sensitivity of health-related queries.
Informativeness: In each interaction, DocBot aims to be informative and educational. Whether offering advice on healthy living or guiding users through emergency procedures, the chatbot prioritizes the dissemination of accurate and valuable information.
User-Centric Approach: DocBot places users at the center of its functionality. By addressing a spectrum of health-related inquiries, the chatbot ensures a user-centric experience, tailoring responses to meet individual needs.
Safety and Responsibility: Recognizing the critical nature of health advice, DocBot operates with a commitment to safety and responsibility. The chatbot encourages users to consult healthcare professionals for personalized guidance in specific situations.
Step 1: Install all dependencies

%pip install "comet_llm>=1.4.1" "openai>1.0.0"
import os
from openai import OpenAI
import comet_llm
from IPython.display import display
import ipywidgets as widgets
import time
import comet_llm

comet_llm.init(project="Doc_bot_openai")
from openai import OpenAI

client = OpenAI()

Step 2: Defining The Role of the Bot
Developing a user-friendly chatbot experience with a focus on empathy and informativeness in DoctorBot's responses is the main goal, encouraging interaction and engagement.
The code below attempts to improve user comprehension by classifying health information, making it a useful and trustworthy resource for health-related questions.
The final objective is to encourage users to seek expert medical counsel when needed and to make informed health decisions.

# Customize your medical advice list if necessary.
advice_list = '''
# Medical Advice List

## General Health:

- Healthy Diet  
  - Tips: Include a variety of fruits and vegetables in your diet. Limit processed foods.

- Regular Exercise  
  - Tips: Aim for at least 30 minutes of moderate exercise most days of the week.

- Adequate Sleep  
  - Tips: Ensure you get 7-9 hours of sleep per night for overall well-being.

## Common Ailments:
- Cold and Flu Remedies  
  - Tips: Stay hydrated, get plenty of rest, and consider over-the-counter cold remedies.

- Headache Relief  
  - Tips: Drink water, rest in a quiet room, and consider over-the-counter pain relievers.

- Stress Management  
  - Tips: Practice deep breathing, meditation, or engage in activities you enjoy.

## Common Symptoms and Solutions:
- Fever  
  - Tips: Stay hydrated, rest, and consider over-the-counter fever reducers.

- Cough  
  - Tips: Stay hydrated, use cough drops, and consider over-the-counter cough medicine.

- Sore Throat  
  - Tips: Gargle with warm saltwater, stay hydrated, and rest your voice.

- Fatigue  
  - Tips: Ensure you get enough sleep, maintain a balanced diet, and consider stress-reducing activities.

## Specialized Advice:
- Chronic Conditions  
  - Tips: Follow your prescribed treatment plan and attend regular check-ups.

- Mental Health Support  
  - Tips: Reach out to a mental health professional if you're struggling emotionally.

- Healthy Aging  
  - Tips: Stay socially active, exercise regularly, and attend routine health check-ups.

## Emergency Situations:
- First Aid for Burns  
  - Tips: Run cold water over the burn, cover with a clean cloth, and seek medical attention.

- CPR Guidelines  
  - Tips: Call for help, start chest compressions, and follow emergency protocols.

'''

context_doctor = [{'role': 'system',
                   'content': f"""
You are DoctorBot, an AI assistant providing medical advice and information.

Your role is to assist users with general health inquiries, provide advice on common ailments, offer specialized health tips, and guide users in emergency situations.

Be empathetic and informative in your interactions.

We offer a variety of medical advice across categories such as General Health, Common Ailments, Common Symptoms and Solutions, Specialized Advice, and Emergency Situations.

The Current Medical Advice List is as follows:

{advice_list}

Encourage users to ask questions about their health, provide relevant advice, and remind them to consult with a healthcare professional for personalized guidance.
"""}]
Step 3: Creating the Chatbot
After you configure your environment and define your advise_list, you can create your DocBot chatbot. The get_completion_from_messages function sends messages to the OpenAI GPT-3.5 Turbo model and retrieves responses from the model.

# Create a Chatbot
def get_completion_from_messages(messages, model="gpt-3.5-turbo"):
    client = OpenAI(
        api_key="OPEN_AI_KEY",
    )

    chat_completion = client.chat.completions.create(
        messages=messages,
        model=model,
    )
    return chat_completion.choices[0].message.content

Step 4: Interacting with Patients
To interact with patients, use a simple user interface with text entry for Patient messages and a button to start a conversation. Thecollect_messages function processes user input, updates conversation context, and displays chat history.

def collect_messages(_):
    user_input = inp.value
    inp.value = ''

    context_doctor.append({'role':'user', 'content':f"{user_input}"})

    # Record the start time
    start_time = time.time()  

    response = get_completion_from_messages(context_doctor) 

    # Record the end time
    end_time = time.time()  

    # Calculate the duration
    duration = end_time - start_time

Step 5: Log records into Comet
The next step involves using comet_llm to keep track of what patients ask, how the bot responds, and how long each interaction takes. The information is logged on the Comet website.
This helps in improving the model for future training. You can learn more about experiment tracking with Comet LLM.

 # Log to comet_llm
    comet_llm.log_prompt(
        prompt=user_input,
        output=response,
        duration=duration,
        metadata={
            "role": context_doctor[-1]['role'],
            "content": context_doctor[-1]['content'],
            "context": context_doctor,
            "advice_list": advice_list
        },
    )

    context_doctor.append({'role': 'assistant', 'content': f"{response}"})

    user_pane = widgets.Output()
    with user_pane:
        display(widgets.HTML(f"<b>User:</b> {user_input}"))

    assistant_pane = widgets.Output()
    with assistant_pane:
        display(widgets.HTML(f"<b>Assistant:</b> {response}"))

    display(widgets.VBox([user_pane, assistant_pane]))

inp = widgets.Text(value="Hi", placeholder='Enter text here…')
button_conversation = widgets.Button(description="Chat!")
button_conversation.on_click(collect_messages)

dashboard = widgets.VBox([inp, button_conversation])

display(dashboard)

The prompts have been logged on the Comet website. By analyzing these logs, you can use various strategies to make responses quicker, improve accuracy, enhance customer satisfaction, and eliminate unnecessary steps in your medical operations.
More training is required for a DocBot chatbot that is more sophisticated.
Comet LLM is a useful tool for logging and viewing messages and threads, which streamlines the process of developing chatbot language models and enhances workflow. It offers insights for effective model building and optimization, simplifies problem-solving, assures workflow reproducibility, and aids in identifying successful methods.

Conclusion

In conclusion, this comprehensive article explains how to utilize OpenAI and CometLLM to build a strong and approachable medical chatbot. Through the utilization of CometLLM's machine learning experimentation capabilities and OpenAI's complex language models, developers and medical professionals can acquire valuable insights into developing a conversational AI that is specifically designed for medical interactions.In order to ensure that the chatbot, DocBot, competently helps users with general health inquiries, common ailments, specialist guidance, and emergency circumstances, the guide highlights the significance of user-centric design. The resulting chatbot, which is dedicated to empathy and informativeness, encourages users to seek expert help when necessary in addition to offering useful health information. This guide offers a preview of the future of intuitive and efficient digital health aides and demonstrates how cutting-edge technologies can revolutionize healthcare communication.
You can check the full code here.

Building A HealthBot Using Chainlit And OpenAI

Oluseye Jeremiah — Fri, 29 Mar 2024 16:23:21 +0000

In this tutorial, we’ll delve into the world of Chainlit, an open-source Python package designed to expedite the development of Chat GPT-like applications by seamlessly integrating your unique business logic and data. We’ll explore how to harness the power of Chainlit to build intelligent and customized HealthBot applications, leveraging its capabilities to create a responsive and context-aware conversational experience. Combined with OpenAI, this tutorial will guide you through the process of constructing a HealthBot that not only understands health-related queries but also incorporates your specific business requirements. Let’s embark on the journey of building an innovative HealthBot using Chainlit and OpenAI.

Before we get started take a look at the end product

Prerequisites
To assure the project’s effective execution, developing an application that resembles ChatGPT utilizing Chainlit and OpenAI demands a particular level of technical expertise.

These are the primary fields of competence that are required:

Python Programming
Principles of Artificial Intelligence and Machine Learning
API Integration and OpenAI API Key Access

Understanding Chainlit

Chainlit is an open-source Python module that has been painstakingly designed to facilitate the rapid building of Chat-like applications by allowing you to easily integrate your unique business logic and data. Specifically designed to build ChatGPT-like apps, it offers a quick and efficient way to integrate into existing code bases or start projects from scratch. With features like data permanence, quick iteration tools, and quick build times, Chainlit is a versatile tool that works with all Python applications and modules. It finds great use in a wide range of AI and machine learning endeavors, particularly those based around conversational AI, thanks to integrations for prominent frameworks and libraries. It provides a ChatGPT-like frontend for instant use, but it also gives customers the ability to customize their frontend using Chainlit as a reliable backend.

Key Features

Fast and Easy Development: Chainlit provides a step-based approach to building LLM applications, making it quick and efficient to get your bot up and running.
Customizable UI: You can create a custom user interface for your Chainlit application, ensuring it seamlessly integrates with your brand and user experience.
Integrations: Chainlit integrates with various tools and libraries, including OpenAI, Haystack, and Llama Index, allowing you to leverage their functionalities within your application.
Robust Features: Chainlit offers features like authentication, monitoring, data streaming, and multi-user support, making your application secure, scalable, and reliable. Overall, Chainlit is a powerful and versatile tool for building chatbot applications.

Building The HealthBot

After setting up the python environment the next step will be to install all necessary dependencies.

pip install chainlit
pip install --upgrade openai

Step 2: Create the main application using Chainlit

import chainlit as cl
from src.llm import ask_doctor, messages


@cl.on_message
async def main(message: cl.Message):
    # Your custom logic goes here...
    messages.append({"role": "user", "content": message.content})
    response = ask_doctor(messages)
    messages.append({"role": "assistant", "content": response})

    # Send a response back to the user
    await cl.Message(
        content =response,
    ).send()

The above code is the app.py version of the entire application. This code segment, utilizing Chainlit, serves as a key part of a chatbot implementation. It intercepts and processes user messages, appending them to a message list. Subsequently, it calls the ask_doctor function, incorporating the accumulated messages to generate a response from a doctor-like entity. The assistant’s reply is then appended to the message list. Finally, the response is sent back to the user, maintaining a conversational flow. The messages list retains the entire conversation, offering a record of interactions for future analysis or reference.

Step 3: Build the llm.py section`

from openai import OpenAI
from src.prompt import health_prompts

client = OpenAI()

messages = [
{"role": "system", "content": health_prompts}
]

def ask_doctor(messages, model="gpt-3.5-turbo",temperature=0):
response = client.chat.completions.create(
model= model,
messages= messages,
temperature= temperature
)
return response.choices[0].message.content
`

This Python code uses the OpenAI library to create a health chatbot powered by the GPT-3.5-turbo model. The chatbot receives user messages, appends them to a list of messages, and utilizes the ask_doctor function to obtain responses from the language model. The code includes predefined health prompts for the chatbot to start the conversation. It sets up a communication loop where the user’s messages trigger model responses, and the assistant’s replies are sent back to the user. The temperature parameter in the ask_doctor function controls the randomness of the model’s responses, offering a dynamic interaction experience.

Step 4:The next step will be to build the prompt which the application runs on

`
Customize your health-related prompts and information if necessary.
health_prompts = '''
Health Bot Information

General Health:

What are the benefits of regular exercise?
- Exercise helps improve cardiovascular health, boost mood, and maintain a healthy weight.
How many hours of sleep are recommended for adults?
- Adults should aim for 7-9 hours of sleep per night for optimal health.
What are some healthy eating tips?
- Include a variety of fruits, vegetables, whole grains, and lean proteins in your diet.

Mental Health:

How to manage stress effectively?
- Practice relaxation techniques, exercise, and prioritize self-care.
Tips for better mental well-being?
- Connect with others, practice gratitude, and seek professional help if needed. Nutrition:
What are some superfoods for a balanced diet?
- Include foods like berries, leafy greens, nuts, and fatty fish in your diet.
How to stay hydrated throughout the day?
- Drink at least 8 glasses of water daily and consume hydrating foods.

Fitness:

Recommended daily physical activity for adults?
- Aim for at least 150 minutes of moderate-intensity exercise per week.
Effective home workouts for beginners?
- Try bodyweight exercises, yoga, or brisk walking.

'''

context_health = [{'role': 'system',
'content': f"""
You are HealthBot, an AI assistant for health-related inquiries.

Your role is to provide information on general health, mental well-being, nutrition, and fitness.

Feel free to answer health-related questions, share tips, and encourage users to adopt a healthy lifestyle.

Below are some health-related prompts:

`{health_prompts}`

Make the health-related interactions informative and encourage users to ask about any health concerns or seek advice.
"""}]

This code defines a set of health-related prompts and information for a chatbot called HealthBot. The prompts cover topics such as general health, mental well-being, nutrition, and fitness. Each prompt includes a question and a brief, informative answer. The code sets up the context for the HealthBot, describing its role as an AI assistant for health-related inquiries. The assistant is encouraged to provide information, answer questions, and promote a healthy lifestyle. The system context includes the predefined health prompts, ready for the HealthBot to interact with users, offering valuable health-related advice and tips.

So before we run the application let’s add a welcome note to the front page that tells users what the bot is all about

`
Doctor Klaus - Your Health Companion

Greetings! I'm Doctor Klaus, your dedicated health companion on this wellness journey. 🌟

In my virtual clinic, I'm here to provide you with valuable health insights, answer your health-related queries, and offer guidance on leading a healthier lifestyle. Let me tell you a bit about myself:

👨‍⚕️ About Doctor Klaus:

I'm an AI-powered health assistant designed to assist you with a wide range of health inquiries.
My knowledge spans various health topics, including exercise, nutrition, mental well-being, and general health guidelines.
Your well-being is my priority, and I'm here to make your health journey more accessible, informative, and tailored to your needs.

💡 How I Can Assist You:

Answering your general health questions.
Providing tips for mental well-being and stress management.
Sharing information on nutrition and healthy eating habits.
Recommending personalized fitness routines and exercises.

🌐 How to Interact with Me:

Simply ask me your health-related questions, and I'll provide you with accurate and relevant information.
Whether you're curious about specific health topics or seeking advice on wellness practices, I'm here for you.

Embark on this wellness adventure with me, Doctor Klaus! Together, we'll explore the path to a healthier, happier you. For any health-related queries, type your questions below, and let's kickstart your journey to well-being. 🚀💚
` `

Run the Application
To start your Chainlit app, open a terminal and navigate to the directory containing app.py. Then run the following command:

chainlit run app.py -w
`
The -w flag tells Chainlit to enable auto-reloading, so you don’t need to restart the server every time you make changes to your application. Your chatbot UI should now be accessible at

We can see that the app is working you can then go ahead and ask any health related questions.

Conclusion

Building a health bot is more than just code and AI. It’s about empowering users with reliable information and promoting informed choices about their well-being. This journey, while ambitious, becomes surprisingly accessible thanks to the incredible capabilities of Chainlit.

Chainlit acts as your user-friendly architect, crafting a seamless interface where your health bot shines. You don’t need to be a coding wizard — Chainlit’s features and intuitive structure let you build a beautiful and interactive platform for your bot to engage with users.

But Chainlit’s magic extends beyond aesthetics. It acts as the communication bridge, translating complex AI responses into clear and user-friendly language. Think of it as your health bot’s personal translator, ensuring every interaction is informative and engaging.

Building A Heart Disease Prediction Model Using Machine Learning

Oluseye Jeremiah — Wed, 24 Jan 2024 03:33:35 +0000

In the dynamic world of healthcare, we’re witnessing a groundbreaking shift towards using technology to better understand and tackle life-threatening conditions. One such leap forward is the integration of machine learning (ML) in predicting heart disease, a pervasive threat that claims lives globally. In this article, we embark on a journey to explore the development of an innovative ML model, aiming to redefine how we approach and safeguard cardiovascular health.

Heart disease, with its various complexities affecting the heart and blood vessels, calls for a proactive approach to healthcare. While traditional risk assessments have been helpful, the rise of ML holds the promise of heightened precision and accuracy. This article dives into the process of creating a predictive model that can identify potential heart issues before symptoms emerge, leveraging algorithms and vast datasets.

Come along as we unravel the intricate steps involved in building a machine-learning model for heart disease prediction. We’ll explore the pivotal roles of data collection, feature engineering, model selection, and validation strategies. This article not only sheds light on the technical side of ML but also emphasizes the profound impact these innovations can have on reshaping the landscape of preventive healthcare.

Picture a future where data-driven insights empower healthcare professionals to intervene early, potentially saving lives and fostering a healthier society. As we delve into the world of predictive analytics for heart disease, let’s envision a human-centric approach that prioritizes well-being and brings us one step closer to a healthier tomorrow.

For our text editor, we’ll be using DeepNote

Deepnote is a cloud-based collaborative workspace for data science and analytics teams. Think of it as a supercharged Jupyter notebook with built-in features for:

Seamless collaboration: Multiple users can work on notebooks together in real time, see each other’s changes, and even chat within the platform.
Powerful data analysis: It combines code blocks, SQL queries, and visualization tools, enabling teams to explore and analyze data efficiently.
Easy sharing and documentation: Notebooks can be easily shared with colleagues and stakeholders, and version control ensures everyone’s on the same page.
Beautiful dashboards and reports: Create interactive dashboards and reports to present findings clearly and compellingly.
Integrated tools and extensions: Connect to popular data sources, libraries, and cloud platforms directly within Deepnote.
Overall, Deepnote streamlines data science workflows, fosters collaboration, and empowers teams to turn data into actionable insights. It’s a popular choice for organizations looking to boost their data science productivity and impact.

Getting Started

The first step involves installing all dependencies

Step 2: This involves loading the dataset to begin data preprocessing

Step 3: To better understand the dataset, we view the first 10 rows of the dataset

we also use the .describe() feature to get insights from the dataset

Step 4: This code groups the diabetes dataset by the ‘Outcome’ column and then calculates the mean of each group. The ‘Outcome’ column usually represents the categories or classes, in this case, it could be ‘0’ for non-diabetic and ‘1’ for diabetic. By using the mean() function, it computes the average values for each feature or column for each outcome. Therefore, the resulting output would include the average of all the columns of the dataset for each outcome (0 and 1).

Step 5: This code is used to separate the features and target from the ‘diabetes_dataset’ data frame. The drop() function is used to remove the ‘Outcome’ column from the data frame. The resultant data frame, which contains all columns other than 'Outcome', is assigned to ‘X’, which will serve as a feature matrix for the machine learning model. ‘Y’ is assigned the ‘Outcome’ column from the 'diabetes_dataset', which acts as a target variable. This will be used to train the machine-learning model. The ‘Outcome’ column typically contains the label or result that the model will attempt to predict.

Step 6: This line of code splits the dataset into a training set and a testing set using the function ‘train_test_split()’ from the sklearn library. The ‘test_size’ parameter is set to 0.2 which means 20% of the data will be used for testing and the rest 80% will be used for training the model. The ‘stratify’ parameter is set to Y which means the train-test split will be made in such a way that the proportion of values in the sample produced will be the same as the proportion of values provided in the ‘Outcome’ column. The ‘random_state’ parameter is set to 2, which ensures that the splits you generate are reproducible and affects the randomness of the training and testing indices produced.

From the above table, we can see the pregnacies by age and the effect it has on heart disease.

Step 7: This line of code is printing the shape of three different data frames: ‘X’, ‘X_train’, and ‘X_test’. The shape of a data frame is a tuple that contains the number of rows and columns in the data frame. ‘X’ is the data frame that contains the entire feature set; ‘X_train’ contains the features for the training set; and ‘X_test’ contains the features for the test set. The output would be three tuples, each representing the number of rows and columns for the respective data frame.

Step 8: The next step involves initiating a Support Vector Machine (SVM) classifier from the'svm' function found in the'sklearn' Python library. The SVM classifier’s kernel is set to ‘linear’. In essence, this piece of code creates a linear SVM model that can be trained using the ‘fit’ function on a labeled dataset to be able to classify new, previously unseen data into pre-defined categories. The performance of the trained classifier can be evaluated using different metrics applicable to classification problems.

Moving on, the piece of code below is responsible for training the Support Vector Machine (SVM) classifier on the training data. The 'classifier.fit(X_train, Y_train)' method is called, where ‘X_train’ is the set of input features for the training data and ‘Y_train’ is the output label for those input features. The model learns from this data, and this learned model can further be used to make predictions on unseen data.

Step 9: We then calculate the accuracy of the predictive model on the training data. The ‘classifier.predict(X_train)’ function generates predictions for the training data based on the trained model, and the results are stored in ‘X_train_prediction’. The ‘accuracy_score()’ function from the sklearn library is then utilized to compare these predictions with the actual labels (‘Y_train’) to compute the accuracy of the model. The calculated accuracy score is stored in ‘training_data_accuracy’.

We can see that the accuracy of our training data is manageable. We can then go on to build the prediction model

We can see that our prediction model works, and the patient here is diabetic.

Conclusion

In our pursuit to predict heart disease through machine learning, the constructed model stands as a beacon of hope, promising a transformative approach to preventive healthcare. The model’s accuracy, validated through meticulous calibration and training, reflects its efficacy in discerning subtle patterns for early detection. Precision, recall, and F1 scores affirm its reliability in identifying potential cardiovascular issues. However, acknowledging the dynamic nature of healthcare data, ongoing refinement is crucial for the model’s adaptability. Beyond numerical accuracy, the model symbolizes our collective commitment to a future where predictive analytics becomes a transformative force, guiding us toward early intervention and shaping a healthier tomorrow.

Image Segmentation Techniques in Computer Vision

Oluseye Jeremiah — Mon, 11 Dec 2023 22:48:32 +0000

Have you ever played Tetris? Remember how you had to fit different shapes together to form complete lines and score points?

Well, image segmentation in computer vision is a bit like playing a high-tech version of Tetris! Instead of fitting shapes together, we’re trying to segment an image into different regions or shapes based on color, texture, edges, and other visual features. It’s a challenging but exciting task, with many applications in fields such as autonomous driving, medical imaging, and augmented reality.

So get ready to flex your Tetris skills and dive into the fascinating world of image segmentation in computer vision!

Before we dive into the techniques, let’s talk briefly about image segmentation.

Image Segmentation

Image segmentation is a fundamental task in computer vision that involves dividing an image into distinct regions or segments based on certain criteria, such as color, texture, or edges.

Image segmentation is essential in many computer vision applications, including object recognition, scene understanding, and image manipulation. Expertise in image segmentation requires knowledge of various techniques, ranging from traditional methods, such as thresholding and edge-based segmentation, to more advanced techniques, like deep learning-based segmentation.

Understanding the strengths and limitations of different segmentation techniques is critical to selecting the most appropriate approach for a specific application.

With the rapid advancement of computer vision technology and the growing demand for high-quality image analysis, expertise in image segmentation has become increasingly important for researchers, engineers, and practitioners in the field.

Image Segmentation Techniques

Thresholding: Thresholding is one of the simplest and most popular image segmentation techniques. It involves setting a threshold value and dividing the image into two segments: one containing pixels with values above the threshold and the other containing pixels with values below the threshold. Thresholding is often used for binary image segmentation, where the goal is to separate foreground objects from the background.
Edge-based Segmentation: Edge-based segmentation is another popular technique that involves detecting edges in the image and using them to separate different regions. Edges are the boundaries between different regions in the image, and they can be detected using various edge detection algorithms, such as the Canny edge detector. Once the edges are detected, they can be used to segment the image by grouping the pixels on either side of the edges into separate regions.
Region-based Segmentation: Region-based segmentation is a technique that involves grouping pixels in the image based on their similarity in color, texture, or other visual features. Region-based segmentation can be performed using clustering algorithms, such as k-means clustering or mean-shift clustering, which group similar pixels into clusters. The resulting clusters can then be used to segment the image into different regions.

4.Watershed Segmentation: Watershed segmentation is a particularly useful technique for segmenting images with multiple objects or regions that touch or overlap. Watershed segmentation treats the image as a topographic map, where the pixel values represent the height of the terrain. The algorithm starts by flooding the image from the highest points and gradually filling the basins between the objects. The resulting basins correspond to the different objects in the image and can be used to segment the image.

5.Deep Learning-based segmentation: Deep learning-based segmentation is a more recent technique that has gained popularity in recent years, particularly with the advent of convolutional neural networks (CNNs). CNNs can learn to segment images by training on large datasets of labeled images. The network is trained to predict a segmentation mask for each input image, where each pixel is assigned a label corresponding to its region. Deep learning-based segmentation can achieve state-of-the-art performance on many image segmentation tasks.

Conclusion

In conclusion, image segmentation is an important problem in computer vision that has many applications in various fields. Several different image segmentation techniques are available, each with its strengths and weaknesses. The best technique depends on the application and the characteristics of the images being segmented. By understanding the different image segmentation techniques, computer vision practitioners can choose the best approach for their specific task and achieve better results.

A Step-by-Step Guide: Efficiently Managing TensorFlow/Keras Model Development with Comet

Oluseye Jeremiah — Mon, 11 Dec 2023 22:32:00 +0000

Introduction

Welcome to the step-by-step guide on efficiently managing TensorFlow/Keras model development with Comet. TensorFlow and Keras have emerged as powerful frameworks for building and training deep learning models. However, as your model development process becomes more complex and involves numerous experiments and iterations, keeping track of your progress, managing experiments, and collaborating effectively with team members becomes increasingly challenging.

This is where Comet comes to the rescue. Comet is a comprehensive experiment tracking and collaboration platform for machine learning projects. It empowers data scientists and machine learning practitioners to streamline their model development workflow, maintain a structured record of experiments, and foster seamless collaboration among team members.

In this guide, we will walk you through the process of efficiently managing TensorFlow/Keras model development using Comet. We will explore the essential features of Comet that enable you to track experiments, log hyperparameters and metrics, visualize model performance, optimize hyperparameter configurations, and facilitate collaboration within your team. Following our step-by-step instructions and incorporating Comet into your workflow can enhance productivity, maintain experiment reproducibility, and derive valuable insights from your model development process.

Whether you are an experienced machine learning practitioner or just starting your journey in deep learning, this article will provide practical strategies and tips to leverage Comet effectively. Let's dive in and discover how you can take control of your TensorFlow/Keras model development with Comet.

Introducing MLOps

Machine learning (ML) is an essential tool for businesses of all sizes. However, deploying ML models in production can be complex and challenging. This is where MLOps comes in.

MLOps is a set of principles and practices that combine software engineering, data science, and DevOps to ensure that ML models are deployed and managed effectively in production. MLOps encompasses the entire ML lifecycle, from data preparation to model deployment and monitoring.

Why Is MLOps Important?

There are several reasons why MLOps is essential. First, ML models are becoming increasingly complex and require a lot of data to train. This means it is necessary to have a scalable and efficient way to deploy and manage ML models in production.

Second, ML models are constantly evolving. This means that it is vital to have a way to monitor and update ML models as new data becomes available. MLOps provides a framework for doing this.

Finally, ML models need to be secure. They can make important decisions, such as approving loans or predicting customer behavior. MLOps provides a framework for securing ML models.

How Does MLOps Work?

MLOps typically involves the following steps:

Data Preparation:
The first step is preparing the data that will be used to train the ML model. This includes cleaning the data, removing outliers, and transforming the data into a format that the ML model can use.
Model Training: The next step is training the ML model. This involves using the prepared data to train the model. The training process can be iterative, and trying different models and hyperparameters may be necessary to find the best model.
Model Deployment: Once the ML model is trained, it must be deployed in production. This means making the model available to users so they can use it to make predictions.
Model Monitoring: Once the ML model is deployed, it must be monitored to ensure it performs as expected. This involves tracking the model's accuracy, latency, and other metrics.
Model Maintenance: As new data becomes available, the ML model may need to be updated. This is known as model maintenance. Model maintenance involves retraining the model with the latest data and deploying the updated model in production.

Keeping Track of Your ML Experiments

Accurate experiment tracking simplifies comparing metrics and parameters across different data versions, evaluating experiment results, and identifying the best or worst predictions on test or validation sets. Additionally, it allows for in-depth analysis of hardware consumption during model training.

The following explanations will guide you in efficiently tracking your experiments and generating insightful charts. By implementing these strategies, you can enhance your experiment management and visualization capabilities, allowing you to derive valuable insights from your data.

Project Requirements

To ensure adequate tracking and management of your TensorFlow model development, it is crucial to establish a performance metric as a project goal. For instance, you may set the F1-score as the metric to optimize your model's performance.

The initial deployment phase should focus on building a simple model while prioritizing the development of a robust machine-learning pipeline for prediction. This approach allows for the swift delivery of value and prevents excessive time spent pursuing the elusive perfect model.

As your organization embarks on new machine learning projects, the number of experiment runs can quickly multiply, ranging from tens to hundreds or even thousands. Without proper tracking, your workflow can become convoluted and challenging to navigate.

That's why tracking tools like Comet have become standard in machine learning projects. Comet enables you to log essential information such as data, model architecture, hyperparameters, confusion matrices, graphs, etc. Integrating a tool like Comet into your workflow or code is relatively simple compared to the complications that arise when you neglect proper tracking.

To illustrate the tracking approach, let's consider an example where we train a text classification model using TensorFlow and Long Short-Term Memory (LSTM) networks. Following the steps in this guide will provide insights into effectively utilizing tracking tools and seamlessly managing your TensorFlow model development process.

Achieve a Well-Organized Model Development Process with Comet

Install Dependencies For This Project

We'll be using Comet in Google Colab, so we need to install Comet on our machine. Follow the commands below to do this.

%pip install comet_ml tensorflow numpy
  !pip3 install comet_ml
   import comet_ml
   from comet_ml import Experiment
   import logging

logging.basicConfig(level=logging.INFO)
LOGGER = logging.getLogger("comet_ml")

Now that we've installed the necessary dependencies let's import them.

import comet_ml
from comet_ml import Experiment
import logging
import pandas as pd
import tensorflow as tfl
import numpy as np
import csv
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow import keras
from tensorflow.keras import layers
import re
import nltk

nltk.download('stopwords')
from nltk.corpus import stopwords

Connect your project to the Comet platform. If you're new to the platform, read the guide.

# Create an experiment
experiment = comet_ml.Experiment(
    project_name="Tensorflow_Classification",
    workspace="olujerry",
      api_key="YOUR API-KEYS",
    log_code=True,

   auto_metric_logging=True,
    auto_param_logging=True,
    auto_histogram_weight_logging=True,
    auto_histogram_gradient_logging=True,
    auto_histogram_activation_logging=True,

It's important to connect your project to the Comet platform at the beginning of your project so every single parameter and metric can be logged.

Save the Hyperparameters (For Each Iteration)

params={
                    'embed_dims': 64,
                    'vocab_size': 5200,
                    'max_len': 200,
                    'padding_type': 'post',
                    'trunc_type': 'post',
                    'oov_tok': '<OOV>',
                    'training_portion': 0.75
    }

experiment.log_parameters(params)

About The Dataset

The dataset we're using is BBC news article data for classification. It consists of 2225 documents from the BBC News website corresponding to stories in five topical areas from 2004–2005.

Class Labels: 5 (business, entertainment, politics, sport, tech)
Download the data here.
In the below section, I've created a list called labels and text, which will help us store the labels of the news article and the actual text associated with it. We're also removing the stopwords using nltk.

labels = []
texts = []

with open('dataset.csv', 'r') as file:
    data = csv.reader(file, delimiter=',')
    next(data)
    for row in data:
        labels.append(row[0])
        text = row[1]
        for word in stopwords_list:  # Iterate over the stop words list
            token = ' ' + word + ' '
            text = text.replace(token, ' ')
            text = text.replace(' ', ' ')
        texts.append(text)

print(len(labels))
print(len(texts))

Let's split the data into training and validation sets. If you look at the above parameters, we're using 80% for training and 20% for validating the model we've built for this use case.

training_portion = 0.8  # Assigning a value of 0.8 for an 80% training portion
train_size = int(len(texts) * training_portion)

train_text = texts[0:train_size]
train_labels = labels[0:train_size]

validation_text = texts[train_size:]
validation_labels = labels[train_size:]

To tokenize the sentences into subword tokens, we will consider the top five thousand most common words. We will use the "oov_token" placeholder when encountering unseen special values. For words not found in the "word_index," we will use "<00V>". The "fit_on_texts" method will update the internal vocabulary utilizing a list of texts. This approach allows us to create a vocabulary index based on word frequency.

vocab_size = 10000  # Assigning a value for the vocabulary size
 oov_tok = '<OOV>'  # Assigning a value for the out-of-vocabulary token
tokenizer = Tokenizer(num_words = vocab_size, oov_token=oov_tok)
tokenizer.fit_on_texts(train_text)
word_index = tokenizer.word_index
dict(list(word_index.items())[0:8])

Observing the provided output, we notice that "" is the most frequently occurring token in the corpus, followed by other words.

With the vocabulary index constructed based on frequency, our next step is converting these tokens into sequence lists. The "text_to_sequence" function accomplishes this task by transforming the text into a sequence of integers. It maps the words in the text to their corresponding integer values according to the word_index dictionary.

train_sequences = tokenizer.texts_to_sequences(train_text)
 print(train_sequences[16])
max_length = 100  # Assigning a value for the maximum sequence length

train_sequences = tokenizer.texts_to_sequences(train_text)
train_padded = pad_sequences(train_sequences, maxlen=max_length, truncating='post', padding='post')

When training neural networks for downstream natural language processing (NLP) tasks, ensuring that the input sequences are the same size is important. We can use the max_len parameter to add padding to the sequences to achieve this. In our case, we initially set max_len to 200, and we applied padding using padding_sequences.

For sequences with lengths smaller or greater than max_len, we truncate or pad them to the specified length of 200. For example, if a sequence has a length of 186, we add 14 zeros at the end to pad it to 200. Typically, we fit the data once but perform sequence conversion multiple times, so we have separate training and validation sets instead of combining them.

padding_type = 'post'  # Assigning a value for the padding type ('post' or 'pre')


trunc_type = 'post'  # Assigning a value for the truncation type ('post' or 'pre')


valdn_padded = pad_sequences(valdn_sequences, maxlen=max_len, padding=padding_type, truncating=trunc_type)


vmax_len = 100  # Assigning a value for the maximum sequence length

valdn_sequences = tokenizer.texts_to_sequences(validation_text)
valdn_padded = pad_sequences(valdn_sequences, maxlen=max_len, padding=padding_type, truncating=trunc_type)

print(len(valdn_sequences))
print(valdn_padded.shape)

Next, let's examine the labels for our dataset. To work with the labels effectively, we need to tokenize them. Additionally, all training labels are expected to be in the form of a NumPy array. We can use the following code snippet to convert our labels into a NumPy array.

label_tokenizer = Tokenizer()
label_tokenizer.fit_on_texts(labels)

Before we proceed with the modeling task, let's examine how the texts appear after padding and tokenization. It is important to note that some words may be represented as "<oov>" (out of vocabulary) because they are not included in the vocabulary size specified at the beginning of our code. This is a common occurrence when dealing with limited vocabulary sizes.

word_index_reverse = {index: word for word, index in word_index.items()}

# %% In [41]:
def decode_article(text):
    return ' '.join([word_index_reverse.get(i, '?') for i in text])
print(decode_article(train_padded[24]))
print('**********')
print(train_text[24])

To train our TensorFlow model, we will use the tfl.keras.Sequential class that allows us to group a linear stack of layers into a TensorFlow Keras model. The first layer in our model is the embedding layer, which stores a vector representation for each word. It converts sequences of words into sequences of vectors. Word embeddings are commonly used in NLP to ensure that words with similar meanings have similar vector representations.

We then use the tfl.keras.layers.Bidirectional wrapper to create a bidirectional LSTM layer. This layer helps propagate inputs forward and backward through the LSTM layers, enabling the network to learn long-term dependencies more effectively. After that, we form it into a dense neural network for classification.

Our model uses the 'relu' activation function, which returns the input value for positive values and 0 for negative values. The embed_dims variable represents the dimensionality of the embedding vectors and can be adjusted based on your specific needs.

The final layer in our model is a dense layer with six units, followed by the 'softmax' activation function. The 'softmax' function normalizes the network's output, producing a probability distribution over the predicted output classes.

Here's the code for the model:

embed_dims = 100  # Placeholder value, adjust it based on your needs

model = tfl.keras.Sequential([
    tfl.keras.layers.Embedding(vocab_size, embed_dims),
    tfl.keras.layers.Bidirectional(tfl.keras.layers.LSTM(embed_dims)),
    tfl.keras.layers.Dense(embed_dims, activation='relu'),
    tfl.keras.layers.Dense(6, activation='softmax')
])
model.summary()

From the model summary above, we can observe that our model consists of an embedding layer and a bidirectional LSTM layer. The output size from the bidirectional layer is twice the size we specified for the LSTM layer, as it considers both forward and backward information.

We used the 'categorical_crossentropy' loss function for this multi-class classification task. This loss function is commonly used in tasks where we have multiple classes and want to quantify the difference between the predicted probability distribution and the true distribution.

The optimizer we have chosen is 'adam,' a variant of gradient descent. 'Adam' is known for its adaptive learning rate and performs well in many scenarios.

Our model is designed to learn word embeddings through the embedding layer, capture long-term dependencies with the bidirectional LSTM layer, and produce predictions using the softmax activation function in the final dense layer.

model.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['accuracy'])

ML Model Development Organized Using Comet

epochs_count = 10

history = model.fit(train_padded, training_label_seq,
                    epochs=epochs_count,
                    validation_data=(valdn_padded, validation_labels_seq),
                    verbose=2)

The accuracy of the experiment was logged:

We can also see the loss of the experiment:

We can also monitor RAM and CPU usage as part of model training. The information can be found in the System Metrics section of the experiments.

Viewing Your Experiment On The Comet Platform

To view all your logged experiments, you need to end the experiment using the code below:

experiment.end()

After running the code, you will get a link to the Comet platform and a summary of everything logged.

[https://youtu.be/e_JnMaGFfGQ]

Conclusion

If the above model shows signs of overfitting after 6 epochs, it is recommended to adjust the number of epochs and retrain the model. By experimenting with different numbers of epochs, you can find the optimal point where the model achieves good performance without overfitting.

Debugging and analyzing the model's performance during development iteratively is crucial. Error analysis helps identify areas where the model may be failing and provides insights for improvement. Tracking how the model's performance scales as training data increases is also essential. This can help determine if collecting more data will lead to better results.

Model-specific optimization techniques can be applied when addressing underfitting, characterized by high bias and low variance. This includes performing error analysis, increasing model capacity, tuning hyperparameters, and adding new features to capture more patterns in the data.

On the other hand, when dealing with overfitting, which is characterized by low bias and high variance, it is recommended to consider the following approaches:

Adding more training data: Increasing the training data can help the model generalize better and reduce overfitting.
Regularization: Techniques like L1 or L2 regularization, dropout, or early stopping can prevent the model from over-relying on specific features or reducing complex interactions between neurons.
Error Analysis: Analyzing the model's errors in training and validation data can provide insights into specific patterns or classes that the model struggles with. This information can guide further improvements.
Hyperparameter Tuning: Adjusting hyperparameters like learning rate, batch size, or optimizer settings can help find a better balance between underfitting and overfitting.
Reducing Model Size: If the model is too complex, it may have a higher tendency to overfit. Consider reducing the model's size by decreasing the number of layers or reducing the number of units in each layer.
It is also valuable to consult existing literature and seek guidance from domain experts or colleagues who have experience with similar problems. Their insights can provide helpful directions for addressing overfitting effectively.

Remember that model development is an iterative process that may require multiple iterations of adjustments and experimentation to achieve the best performance for your specific problem.

Here is a link to my notebook on Google Colab, as well as the original notebook by Aravind CR.

How to Effectively Search Large Datasets in Python

Oluseye Jeremiah — Fri, 08 Dec 2023 21:10:34 +0000

Imagine you're trying to find a needle in a haystack, but the haystack is the size of a mountain. That's what it can feel like to search for specific items in a massive dataset using Python.

But fear not! With the right techniques, you can efficiently search and lookup information in large datasets without feeling like you're climbing Everest.

In this article, I'll show you how to take the pain out of search operations in Python. We'll explore a range of techniques, from using the built-in bisect module to performing a binary search, and we'll even throw in some fun with sets and dictionaries.

So buckle up and get ready to optimize your search operations on large datasets. Let's go!

Method 1: Linear Search in Python

The simplest way to search for an item in a list is to perform a linear search. This involves iterating through the list one element at a time until the desired item is found. Here is an example of a linear search:

def linear_search(arr, x):
    for i in range(len(arr)):
        if arr[i] == x:
            return i
    return -1

In the code above, we define the function linear search, which accepts two inputs: a list arr and a single item x. The function loops through the list, iterating through each element and comparing it to the desired item x. The function returns the item's index in the list if a match is found. In the absence of a match, the method returns -1.

Linear search has an O(n) time complexity, where n is the list length. This indicates that the time needed to conduct a linear search will increase proportionally as the size of the list grows.

Method 2: Binary Search in Python

If the list is sorted, we can perform a binary search to find the target item more efficiently. Binary search works by repeatedly dividing the search interval in half until the target item is found. Here is an example of a binary search:

def binary_search(arr, x):
    low = 0
    high = len(arr) - 1
    while low <= high:
        mid = (low + high) // 2
        if arr[mid] < x:
            low = mid + 1
        elif arr[mid] > x:
            high = mid - 1
        else:
            return mid
    return -1

In the code above, we define the function binary search, which accepts as inputs a sorted list arr and a target item x. The low and high indices are used by the function to maintain a search interval.

A comparison between the target item x and the middle element of the search interval is performed by the function on each iteration of the loop.

The modified search interval omits the bottom half of the list if the middle element is less than x. The search interval is modified to omit the top half of the list if the middle element is greater than x. The function provides the item's index in the list if the middle element equals x.

If the desired item cannot be located, the function returns -1. Binary search has an O(log n) time complexity, where n is the list length. This means that, especially for big lists, binary search is substantially more effective than linear search.

Method 3: Search Using Sets in Python

If the order of the list is not important, we can convert the list to a set and use the in operator to check whether an item is present in the set. Here is an example:

my_list = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
my_set = set(my_list)
if 5 in my_set:
    print("5 is in the list")
else:
    print("5 is not in the list")

In the above code, we define a list my_list and convert it to a set my_set. We then use the in operator to check whether item 5 is present in the set. If the item is present, we print a message indicating that it is in the list. If the item is not present, we print a message indicating that it is not in the list.

Using sets for search operations can be very efficient for large lists, especially if you need to perform multiple lookups, as sets have an average time complexity of O(1) for the in operator. But sets do not preserve the order of the elements, and converting a list to a set incurs an additional cost.

Method 4: Search Using Dictionaries in Python

If you need to associate each item in the list with a value or some other piece of information, you can use a dictionary to store the data. Dictionaries provide a fast way to look up a value based on a key. Here is an example:

students = {
    "John": 85,
    "Lisa": 90,
    "Mike": 76,
    "Sara": 92,
    "David": 87
}
if "Lisa" in students:
    print(f"Lisa's grade is {students['Lisa']}")
else:
    print("Lisa is not in the class")

In the above code, we define a dictionary students that associates the name of each student with their grade. We then use the in operator to check whether the name "Lisa" is in the dictionary, and if so, we print her grade.

Dictionaries provide an average time complexity of O(1) for lookups based on the key, which makes them very efficient for large datasets. But, dictionaries do not preserve the order of the items, and there is an additional cost associated with creating the dictionary.

Conclusion

Searching and looking up info in large datasets can be a daunting task, but with the right tools and techniques, it doesn't have to be. By applying the methods we've covered in this article, you can efficiently navigate massive datasets with ease and precision.

From the built-in bisect module to the powerful capabilities of sets and dictionaries, Python offers a range of efficient and versatile options for finding and retrieving data. By combining these techniques with smart programming practices and optimization strategies, you can create lightning-fast search operations that can handle even the largest datasets.

So don't let big data intimidate you. With a little bit of creativity, a lot of perseverance, and the techniques we've explored in this article, you can conquer any search challenge and emerge victorious. Happy searching!

Dealing With Missing Values In A Dataset. A Comprehensive Guide To Handling Missing Values In Machine Learning.

Oluseye Jeremiah — Tue, 31 May 2022 13:59:40 +0000

INTRODUCTION

One of the most common issues that Developers in the Data industry have had to deal with over the years is the issue of missing data. Data scientists, analysts, data engineers, and machine learning engineers all face the same issue, and the primary cause of missing values is data collection (i.e. the fact that data is usually collected from many different sources).

There have been a lot of solutions provided to fix the problem of missing data but none of it has been a permanent solution as each solution has its flaws. So each solution depends on the type of dataset and what the data will be used for. For instance, when building a machine model for predicting the prices of houses there are certain statistics that are needed and cannot just be replaced by any value, and the removal of such important value can reduce the accuracy of our model, giving us a biased model.

So it's important to use the method that works best for the dataset. To determine the method you first have to note down the size of the dataset, the percentage of data that is missing, and what the dataset is to be used for, all these statistics help determine which of the methods to use to find the missing value.

In this article, I will briefly explain and list some methods that can be used to deal with missing data with some hands-on examples.

Basic Steps Involved

1)The use of central tendencies for imputing values

Mean
Median
Mode

2) Dropping the column with the missing data.

3) Filling the column with new values.

Example

We will start the hands-on example by first importing all the libraries that are needed in this tutorial.

import pandas as pd
import matplotlib as plt
import seaborn as sns

We then load the required dataset from Kaggle

dataset = pd. read_csv('/Highest Holywood Grossing Movies.csv')

Afterward, we start some exploratory data analysis

dataset.info()

dataset.head()

This is to actually familiarize ourselves with the dataset and also note the location of the missing data.

dataset.tail()

Then we use the dataset. shape function to know the number of rows and columns present in our data.

dataset.shape

The dataset. isnull() function returns false if the values in the dataset are not missing and returns true if it is missing.

dataset.isnull().head()

The dataset. isnull().head() function only gives us the details on the first 5 columns but to know the exact column and how many values are missing we use the dataset. isnull().sum() function that tells us which column has missing values and the number of missing values it has.


dataset.isnull().sum()

The use of central tendencies like the mean, median, and mode

Mean: This is the average of the total of the numbers
Median: This is the middle number when all numbers are arranged in an alphabetical or ascending order.
Mode: This is the number with the most occurring frequencies.

dataset['License'].fillna(dataset['License'].median(),inplace=True)

The above code shows how to fill our empty dataset with the median value. it also applies to the mean and mode just have to make a few changes to the code.


dataset['License'].fillna(dataset['License'].mean(),inplace=True)

How to fill the empty dataset using the mode value.

dataset['License'].fillna(dataset['License'].mode(),inplace=True)

it is important to note though that the use of central tendencies may not always apply to all kinds of datasets, mostly datasets involving numbers.

2) Dropping the column with the missing data.

To drop the column or row with the missing value is quite straightforward as it requires just a line of code but so as not to mess us the entire data there are features you need to include when dropping the column.

dataset.dropna(how='any').shape

The above line of code drops a row if any of its values are missing and as we can see we lost a lot of rows.

dataset.dropna(how='all').shape

The above code is quite similar to the previous one it drops the row if all of its values are missing and we can see that all our rows remain intact because it is not empty

dataset.dropna(subset=['License' , 'Release Date'], how='any').shape

The above method scans through the dataset and checks for the row where values are missing in the License and Release Date column.

Similarly, we can specify which row to remove using the "thresh hold " parameter and what this does is that it keeps any row that has just one missing value. You can always increase the thresh depending on how many rows you want to keep

dataset.dropna(thresh=1).shape

It is important to note that if the data missing in our dataset is above 60% it is advisable to discard such dataset.

3) Fill in the missing values.

The last method involves filling the missing values with either the previous value or any other value of our choice. Also, this is also not the best method as it can cause inaccuracy in our data, and also it is not always realistic.

The first step might involve filling all empty spaces with zero this might work for some datasets but not all. For instance, our dataset has the Release Date column empty filling the date column with zero is not helping our dataset in any way.

dataset.fillna(0)

Another method to fill the missing row is by creating a dictionary and what this does is that it specifies the exact column you want to fill with a new value.

dataset.fillna({
'Release Date':July 6, 2014,
'Liencse':PG-13,
})

The above code fills all the empty spaces in the 'Release Date' column with July 6, 2014 so we have about 118 rows filled with the same value and this can cause some serious alterations in the model. Conversely filling the 'License' column with 'PG-13' has some level of accuracy because every other row is filled with the same thing.

Also, we can use the forward fill('ffill') method that fills the missing value with the previous value and also the backward fill that fills the value with the next value or preceding value.

dataset.fillna(method=''ffill'')

dataset.fillna(method=''bfill'')

So also in a situation where you don't want to fill the entire column with the previous or preceding value you can set a limit.


dataset.fillna(method=''ffill'', limit=2)

The above code only fills the next two empty columns with the previous value.

Conclusion

Thank you for completing this article. I hope you learned something new or perhaps you found it helpful. You can reach out to me on Twitter

data-science

data-analysis

machine-learning

codenewbies

python

Explaining The Differences Between SAAS, PAAS And IAAS.

Oluseye Jeremiah — Mon, 30 May 2022 10:52:04 +0000

The cloud is becoming a significant topic as many of our day-to-day activities are performed thanks to the Cloud. The cloud has been used to save important documents, pictures, videos, and other contents. But the cloud has moved from just being a means of storing pictures and other documents to providing services to businesses both small businesses and big businesses. A lot of companies are gradually transitioning from on-premise infrastructure to the cloud.

So in this article, we will be considering the various cloud services and explaining the differences, advantages, disadvantages, and features of each.
3 major cloud services are widely accepted and it is important that understand each of these services before moving your business to any cloud platform.

IAAS( Infrastructure As A Service)
PAAS( Platform As A Service)
SAAS( Software As A Service)

IAAS: Infrastructure As A Service

Infrastructure as a service (IaaS) is a pay-as-you-go cloud computing service that provides basic computation, storage, and networking resources on demand. In IAAS the cloud provider is responsible for maintaining and overseeing the infrastructure while the client is in charge of the Platform that is ( Operating System, Runtime, and Middleware) and Software ( Application and Data).

IAAS provides you with the resources needed in creating a small cloud-based application to large and fully functional apps and is managed by a System Admin. IaaS provides the same technologies and capabilities as a traditional data center without requiring physical maintenance or management.

Users of IaaS can still access their computing infrastructure directly, but it's all done through a cloud-based "virtualized environment," which usually contains additional resources such as a virtual-machine disk-image library, raw (block) and file-based storage, firewalls, load balancers, IP addresses, virtual local area networks (VLANs), and software bundles.

Transferring your infrastructure to an IaaS service helps to minimize on-premises data center maintenance, save money on hardware, and access real-time business analytics. IaaS solutions allow you to scale your IT resources up and down in response to demand. They also aid in the rapid deployment of new apps and the enhancement of the reliability of your underlying infrastructure. IAAS allows you to avoid the costs and hassles of purchasing and operating real servers and data center infrastructure. Each resource is available as its service package, and you only pay for what you use.

Advantages Of Using IAAS

1) Compared to PAAS clients have complete control over the infrastructure.

2) IAAS is arguable the most flexible cloud computing model.

3) IaaS reduces the cost of setting up and administering a physical data center, making it a cost-effective option for cloud migration. IaaS providers' pay-as-you-go subscription models, decrease hardware costs and upkeep, allowing your Administration employees to concentrate on core business.

4) Performance, precision, reliability, and adaptability have all improved.

5) Because the data is not stored on the client's premise, it improves business continuity and disaster recovery.

6) IaaS allows for immediate outage recovery.

7) High scalability allows for quick scaling up and down.

Disadvantages Of Using IAAS.

1) Vendor Lock-In: Transitioning from one IAAs provider to another one is quite difficult.

2) IAAs tend to be prone to security challenges either internally or externally.

3) Iaas can be limited if the client does not have a stable internet connection.

4) Since it operates on a pay for what you use model there can be an increase in cost which can be a result of a spike in usage.

Examples Of IAAS Providers.

Popular IaaS providers include:

Microsoft Azure,
Amazon Web Services
Rackspace
Google Compute Engine.

PAAS: Platform As A Service

PaaS is a cloud computing service that utilizes virtualization technology to provide developers and organizations with an application development platform. The use of virtualization technology enables resources to be scaled up or down depending on how complex the client's business gets.

PAAS is a situation where the cloud provider manages the infrastructure (Storage, Networking, Servers, and Virtualization) and also Platform ( Operating System, Middleware, Runtime) In PAAS the client is only responsible for the Software that is the data and applications.
In PAAS the client(Developer) is responsible for maintaining, and managing applications and services developed. PAAS supports the building, testing, deployment, and management of cloud-based applications. PAAS is used mainly by developers as it provides them with the ability to work simultaneously with multiple developers herby enabling them to work on the same projects.
And also since developers prefer to focus on writing code rather than building and operating infrastructure. That is why PaaS (platform as a service) has become so attractive. PaaS users have traditionally been able to access a software development platform via a web browser via a cloud service provider like(Azure, AWS, GCP, and many other) hosted infrastructures. Programmers easily program, and organizations can quickly deploy new apps, thanks to easy access to a spectrum of development tools that can also be used to create a customized application.

Advantages Of Using PAAS

1) It is quite easy to create and delete as it supports short time usage

2) It is cost-effective as it operates on operational expenditure so you only pay for the services you use.

3) It saves you the stress of having to employ numerous System Admins.

4) It is easy to use and faster since the only aspect the client focuses on is uploading and updating the application.

5) PAAS is typically used when developing cloud-based applications.

6) Using PAAS gives you guaranteed access from any location with which you can assess your cloud-based application.

7) Using PAAS gives you the access to scale horizontally during peak demand and scale vertically as the demand reduces.

8) PAAS helps avoid purchasing and administering software that requires regular updates.
9) PaaS isn't just for developing and testing apps in a cloud platform. Businesses can also use PaaS technologies to analyze data, access BPM platforms, add communication capabilities to apps, and maintain databases.

Disadvantages Of PAAS.

1) Vendor lock-in: This happens to be one of the common challenges as it is quite difficult to migrate your applications and business in general from one cloud provider to another without affecting the business, that is why business owners must take the time to study each cloud provider and weigh their options.

2) PAAS depends solely on the vendor that is the cloud provider and the success or failure is dependent on how reliable the provider is.

3) Businesses are responsible for the security of the apps they design, whereas Vendors safeguard the infrastructure and platform.

4) PAAS clients have little or no control over the infrastructure and platform.

Examples of PAAS Providers

The following are examples of major PAAS providers.

AWS Elastic Beanstalk
Microsoft Azure App Services
Google App Engine,
IBM Cloud,
Red Hat OpenShift.

SAAS: Software As A Service.

Software as a service happens to be the most commonly used cloud service, especially for businesses. SAAs uses the internet to deliver applications and allows users to connect and use cloud-based applications over the internet and this is managed by a third-party vendor.

In SAAS the cloud vendor manages everything ranging from the infrastructure, the platform, and the software.

SaaS provides enterprises with various benefits, including flexibility and cost savings. Employees may focus on other priorities when SaaS vendors handle the tiresome chores of installing, managing and updating software.

One of the main advantages of SAAS is that most of its applications are online and they operate directly through your browser. And also the fact that a large percentage of people use SAAS applications daily as they are common applications.
SAAS operates on a subscription-based model rather than a one-time license fee.

Advantages Of Using SAAs

1) Due to the fact that it uses a web delivery model, it reduces the amount needed in buying infrastructure.

2) SAAs provides increased securities as the security of the business solely depends on the cloud provider.

3) IT personnel gets to focus on more important duties as there is no need to install, manage or update the software.

4) Users can access their data stored in the cloud from any computer or mobile device with an Internet connection. If a user's PC or device crashes, no data is lost.

5)The ability to operate through an internet browser from any device at any time.

Disadvantages Of Using SAAs

1) Due to the fact that it runs on a web-based model constant internet connection is needed.

2) The Client usually doesn't have control over the infrastructure, platform, and software.

3) The majority of SaaS applications provide very little in the form of vendor customization.

Examples of SAAS

Popular software as a service example include ;

Microsoft Office 365,
Google G Suite (Apps),
Dropbox
Salesforce,
Youtube
Zoom.

SASS VS PAAS VS IAAS

The image below shows the infrastructure, the platform, and the software and it reveals what the vendor manages and what the client manages.

Conclusion.

Understanding the difference, advantages, and disadvantages of each of the cloud service models is crucial before moving to any cloud platform.
I hope you have found this article helpful in understanding the different cloud services. Thanks for reading! Please like and share if it is helpful in any way! Cheers!