DEV Community: Drishti Jain

RAG Is a Data Engineering Problem Disguised as AI

Drishti Jain — Tue, 27 Jan 2026 14:00:41 +0000

Retrieval-Augmented Generation (RAG) is usually introduced as a clever AI pattern: take an LLM, bolt on a vector database, retrieve relevant documents, and voilà—your model is now “grounded” in private data. This framing is seductive because it makes RAG feel like an inference-time concern. Pick a good embedding model, tune top_k, write a better prompt, and the system improves.

In production, this mental model collapses almost immediately.

What actually determines whether a RAG system works over time has very little to do with prompt engineering or model choice. The dominant failure modes are mundane, unglamorous, and painfully familiar to anyone who has built large-scale data systems: stale data, broken pipelines, schema drift, inconsistent backfills, and the absence of contracts between producers and consumers.

RAG does not fail because LLMs hallucinate.

RAG fails because data systems drift.

Once you accept this, the architecture of a “good” RAG system changes completely.

From Toy RAG to Production Reality

Let’s start with a simplified RAG pipeline that appears in most tutorials:

Load documents
Split them into chunks
Generate embeddings
Store them in a vector database
Retrieve top-k chunks at query time
Send them to an LLM

This pipeline assumes something critical but rarely stated: that documents are static.

In real systems, documents change. Policies are updated. Knowledge bases are corrected retroactively. Records are deleted for compliance reasons. Meanings shift even when text does not. If your embedding store does not reflect these changes, retrieval quality degrades silently. Worse, it degrades confidently.

The LLM is not aware that its context is stale. It will happily synthesize an authoritative answer from outdated information.
This is the first sign that RAG is not an inference problem. It is a derived data problem.

Embeddings Are a Materialized View

A useful reframing is to think of embeddings as a materialized view over raw data.

They are:

Derived from source data
Expensive to compute
Immutable once written
Queried at high frequency
Assumed to be correct by downstream consumers

This should immediately trigger familiar data-engineering questions:

What is the source of truth?
How do changes propagate?
How do we handle deletes?
How do we backfill safely?
How do we know the data is fresh?

Most RAG systems answer none of these explicitly.

Data Freshness and Embedding Invalidation

Consider a simple example: a policy document stored in S3 that is updated weekly. A naïve RAG pipeline embeds the document once and stores the vectors in OpenSearch. A week later, the policy changes, but the embeddings remain untouched.

Your system is now guaranteed to return incorrect answers.

The dangerous part is that nothing breaks. Queries still work. Latency looks fine. Retrieval scores look reasonable. There is no exception to catch.

To prevent this, embedding invalidation must be explicit.

At minimum, each embedding must be associated with:

A stable source identifier
A source version or checksum
A timestamp For example, a simple metadata schema might look like this:

{
  "document_id": "policy_123",
  "document_version": "2024-11-18",
  "chunk_id": 7,
  "embedding_model": "text-embedding-3-large",
  "created_at": "2024-11-18T10:42:00Z"
}

At query time, retrieval should filter embeddings based on freshness constraints, not blindly trust the vector store.
This already moves RAG closer to a data system: freshness is now a first-class concept.

Change Data Capture → Incremental Re-Embedding

The next failure point appears at scale. Once you have thousands or millions of documents, re-embedding everything on every change becomes infeasible. Cost explodes, pipelines miss SLAs, and backfills become terrifying.

This is where Change Data Capture (CDC) becomes essential.
Instead of treating embeddings as batch artifacts, treat them as incrementally updated derived data.

A Practical AWS Pattern

Assume your source data lives in Aurora PostgreSQL and is periodically updated.

Enable CDC using AWS DMS or logical replication.
Stream changes into an S3 landing zone.
Trigger re-embedding only for changed records.

A simplified Lambda-based embedding consumer might look like this:

import json
import boto3
from openai import OpenAI
from psycopg2 import connect

client = OpenAI()
opensearch = boto3.client("opensearch")

def handler(event, context):
    for record in event["Records"]:
        change = json.loads(record["body"])

        if change["op"] == "DELETE":
            delete_embeddings(change["document_id"])
            continue

        text_chunks = chunk_document(change["content"])

        embeddings = client.embeddings.create(
            model="text-embedding-3-large",
            input=text_chunks
        )

        for i, vector in enumerate(embeddings.data):
            index_vector(
                document_id=change["document_id"],
                version=change["version"],
                chunk_id=i,
                vector=vector.embedding
            )

This code is not interesting from an ML perspective. It is interesting from a data perspective because it makes embeddings reactive to change.
Now embeddings behave like any other downstream table in a CDC-driven architecture.

Schema Evolution in “Unstructured” Data

The phrase “unstructured data” is one of the most damaging ideas in modern data systems. PDFs, tickets, chats, and documents are not unstructured—they have implicit schemas.

A policy document might look like prose, but it encodes structure:

Definitions
Scope
Exceptions
Effective dates

When these structures change, retrieval quality changes too. Chunking strategies that worked before may now split semantically related sections. Old embeddings may no longer align with new meanings.
This is why schema evolution must be modeled explicitly, even for text.

A practical approach is to version:

Chunking logic
Section detection
Metadata extraction

For example:

def chunk_document_v2(document):
    sections = extract_sections(document)
    for section in sections:
        yield {
            "text": section.text,
            "section_type": section.type,
            "schema_version": "v2"
        }

By tagging embeddings with a schema_version, you gain the ability to:

Compare retrieval quality across versions
Backfill selectively
Roll back safely This is standard practice in feature stores. RAG systems should be no different.

Data Contracts for LLM Inputs

In mature data platforms, producers and consumers agree on contracts. LLMs are consumers too, even if they speak natural language.
Without contracts, retrieval layers return “whatever is close enough,” and prompts are expected to fix the rest. This is backwards.
A data contract for RAG might specify:

Required metadata fields
Maximum document age
Allowed document types
Minimum chunk completeness

Enforcement belongs in the retrieval layer, not the prompt.

def retrieve_context(query_embedding):
    results = vector_search(
        embedding=query_embedding,
        filters={
            "document_type": "policy",
            "document_version": ">=2024-10-01"
        }
    )
    return results

The LLM should never see context that violates these guarantees. If no context satisfies the contract, the system should abstain or escalate.
This is how you prevent hallucinations systemically, not cosmetically.

Backfills: The Moment of Truth

Eventually, you will need to:

Change embedding models
Fix broken chunking
Correct historical data

This requires backfills, and backfills expose architectural weaknesses brutally.

A robust backfill strategy on AWS typically involves:

Writing new embeddings to a versioned index
Validating retrieval quality offline
Atomically switching traffic

Step Functions are ideal for this:

{
  "StartAt": "BatchDocuments",
  "States": {
    "BatchDocuments": {
      "Type": "Map",
      "ItemsPath": "$.documents",
      "Iterator": {
        "StartAt": "EmbedBatch",
        "States": {
          "EmbedBatch": {
            "Type": "Task",
            "Resource": "arn:aws:lambda:embed",
            "End": true
          }
        }
      },
      "End": true
    }
  }
}

If backfills are terrifying, your system is not production-ready.

The LLM Is the Least Interesting Part

Once you view RAG through a data-engineering lens, something surprising happens: the LLM becomes interchangeable.
You can swap models. You can change prompts. You can even replace RAG with fine-tuning in some cases.
What you cannot replace easily is:

Data lineage
Freshness guarantees
Versioned embeddings
Deterministic retrieval

These are the real assets of a production RAG system.

Conclusion: Build RAG Like a Data Platform

RAG systems do not fail because LLMs are probabilistic.
They fail because data systems are treated casually.

If you build RAG like:

a batch job,
a demo pipeline,
or a prompt experiment,

it will collapse under real-world change.

If you build it like:

a CDC-driven system,
with contracts, versioning, and backfills,
using boring, well-understood data engineering principles,

it will scale—and more importantly, it will stay correct.

RAG is a data engineering problem disguised as AI.
Treat it that way, and the AI part becomes easy.

Generative AI Meets Edge: Deploying Foundation Models with AWS IoT Greengrass

Drishti Jain — Sun, 29 Jun 2025 19:14:19 +0000

In the past few years, Generative AI has captured the imagination of the tech world, enabling breakthroughs from natural language processing to computer vision. Foundation models like GPT, Stable Diffusion, and proprietary large models from Anthropic and Cohere have reshaped industries. Yet, most deployments have remained cloud-centric due to the computational heft and data centralization traditionally required.

However, a new paradigm is emerging: bringing Generative AI to the edge. This shift promises faster inference, enhanced privacy, lower bandwidth usage, and real-time decision-making. AWS IoT Greengrass, Amazon's edge runtime and management system, provides a robust, scalable framework to deploy and manage these advanced AI models at the edge.

In this blog, we'll explore how AWS IoT Greengrass enables the deployment of foundation models to edge devices, discuss architectural considerations, practical steps, limitations, and real-world scenarios where this approach shines.

Why Bring Generative AI to the Edge?

Before diving into architecture, it's important to understand the why of edge-based Generative AI.

Latency and Real-time Processing

Cloud-based GenAI models introduce unavoidable round-trip latencies that can hinder use-cases like real-time language translation, predictive maintenance alerts, or immediate anomaly detection in video streams. Edge deployment reduces response time to milliseconds.

Bandwidth and Cost Savings

Streaming large amounts of sensor or video data to the cloud for inference can be prohibitively expensive and bandwidth-intensive. Processing and filtering data locally cuts down cloud transfer costs dramatically.

Data Privacy and Compliance

For applications in healthcare, industrial control, and customer personalization, sending sensitive data to the cloud may be legally restricted. Processing locally with models on-device or on-premises preserves data privacy and compliance with regulations like HIPAA or GDPR.

Improved Reliability

Edge inference continues to work even when connectivity to the cloud is intermittent or temporarily lost, providing resiliency crucial for mission-critical environments.

AWS IoT Greengrass: The Edge AI Enabler

AWS IoT Greengrass is a service that extends AWS capabilities to edge devices so they can act locally on the data they generate, while still using the cloud for management, analytics, and storage. Version 2 of Greengrass provides a modular, component-based architecture allowing developers to:

Build and deploy Lambda functions, native binaries, containerized applications, or Python scripts to devices.

Manage device fleet updates, configuration, and monitoring from AWS IoT Core.

Integrate seamlessly with other AWS services like SageMaker Edge Manager, CloudWatch, and IoT Device Defender.

Critically, Greengrass supports machine learning inference locally through its ML inference component, which pairs well with AWS SageMaker Neo (optimized model compilation for edge hardware).

Example Python Greengrass Component Code

Below is a minimal working Python script snippet you can package in your Greengrass component to load a compiled PyTorch model and serve local inference:

This script uses Flask (packaged in your component) to create a local HTTP endpoint for inference.

Architecture Overview: Deploying Foundation Models with Greengrass

Let’s map out a typical workflow for deploying a foundation model with AWS IoT Greengrass.

Model Preparation and Optimization

Most large generative models are initially too big for constrained edge hardware. The first step is to distill or quantize the model using frameworks such as:

AWS SageMaker Neo: Compiles models to optimized binaries for specific edge hardware accelerators (e.g., NVIDIA Jetson, Intel OpenVINO devices, ARM cores).

ONNX Runtime: Converts models to ONNX format for efficient cross-platform inference.

Third-party libraries: Such as Hugging Face Optimum, TensorRT for LLM quantization/pruning.

Example: Take a distilled GPT-2 model from Hugging Face, convert to TorchScript or ONNX, and then compile using SageMaker Neo targeting your device architecture.

Create Greengrass Component

Greengrass components package your code, dependencies, and resources (such as ML models). A component recipe (JSON/YAML manifest) describes component lifecycle phases (install, run, shutdown) and parameters.

You can package your optimized model alongside a Python script that loads the model and serves inference requests over a local REST API or IPC interface.

Deploy Component to Edge Devices

Through the AWS IoT Greengrass console or CLI, deploy your component to target device groups or individual devices. You can set rollout policies and observe deployment status in real-time.

Greengrass handles pulling components to devices, setting up runtime environments, and managing version updates seamlessly.

Connect Local Applications

Other applications on the device (e.g., sensor data pipelines, camera feeds) can interact with the component over IPC or HTTP to send prompts and receive generated outputs. Greengrass also allows secure communication between components and integration with AWS IoT Core messaging.

Monitor and Update

Use AWS IoT Device Management and CloudWatch to monitor performance, log errors, and trigger OTA (over-the-air) updates to your models or code as needed.

Example Use Case: Generative Vision Model for Industrial Inspection

Imagine a factory floor using high-speed cameras to inspect products. Sending video streams to the cloud for inference would incur huge bandwidth costs and latency issues.

Instead, you can:

Train and fine-tune a generative defect detection model in AWS SageMaker.

Optimize the model with SageMaker Neo or TensorRT for deployment on NVIDIA Jetson edge devices.

Package the model with inference scripts in a Greengrass component.

Deploy to all edge inspection devices.

Run inference locally, generating real-time alerts and defect metadata. Optionally send only summary reports or exceptions to the cloud.

This reduces data transfer by orders of magnitude, speeds response time, and keeps sensitive production data on-premises.

Challenges and Considerations

While the architecture above is powerful, practical edge GenAI deployments come with challenges:

Resource constraints: Even optimized models can require gigabytes of memory and compute. Careful model selection, quantization, or even hybrid cloud-edge inference strategies are often needed.

Model updates: Foundation models evolve quickly; managing frequent updates across potentially thousands of devices can become operationally complex.

Security: Edge devices can be physically accessed or compromised. Ensuring secure model storage, encrypted communication, and device hardening is crucial.

Explainability: Generative models are often black boxes. Providing operators with transparent outputs or confidence metrics is important, especially in regulated industries.

Future Directions: TinyML, Multi-Agent Orchestration, and Federated Learning

The convergence of GenAI and edge computing is just beginning. Exciting areas of research and development include:

TinyML GenAI: Compressing language and vision models further to fit microcontroller-class devices with kilobytes of RAM.

Multi-agent edge orchestration: Using Greengrass to coordinate multiple specialized AI agents on the same device or across clusters of devices.

Federated fine-tuning: Devices could locally fine-tune models on unique data and periodically send updates to the cloud to improve a shared global model — combining edge privacy with cloud learning scale.

Generative + Predictive hybrids: Using generative models alongside traditional predictive models for richer local decision-making and diagnostics.

Deploying foundation models to the edge with AWS IoT Greengrass unlocks new opportunities for low-latency, private, and cost-effective AI-powered applications. While challenges remain, the AWS ecosystem provides powerful tools for model optimization, deployment, and fleet management at scale.

As generative AI continues its meteoric rise, expect the edge to become a major frontier — not just for inference, but also for creative and autonomous decision-making. Building today with Greengrass and AWS's AI stack positions you to harness this wave of innovation tomorrow.

Thank you for reading. If you have reached so far, please like the article.

Do follow me on Twitter and LinkedIn ! Also, my YouTube Channel has some great tech content, podcasts and much more!

Mastering Serverless and Event-Driven Architectures with AWS: Innovations in Lambda, EventBridge, and Beyond

Drishti Jain — Sun, 24 Nov 2024 02:02:24 +0000

In today’s fast-paced world, organizations are embracing serverless and event-driven architectures to achieve scalability, agility, and cost efficiency. AWS leads this domain with innovations in
services like AWS Lambda, Amazon EventBridge, and supporting solutions that empower developers to build resilient, real-time applications. This blog explores how you can leverage these technologies, the patterns and practices involved, and code examples to kick-start your journey into the world of serverless computing.

Why Serverless and Event-Driven Architectures?

Serverless architectures eliminate the need to manage infrastructure. With AWS handling scaling, fault tolerance, and maintenance, developers focus on writing code that delivers value. Similarly, event-driven architectures enable applications to respond to real-time events, such as database updates or user actions, enhancing responsiveness and user experience.

Benefits include:

Cost efficiency: Pay only for what you use.
Scalability: Automatic scaling to handle varying workloads.
Resilience: Built-in fault tolerance and high availability.
Developer agility: Faster development cycles and deployment.

Key AWS Serverless Services

1. AWS Lambda

AWS Lambda lets you run code without provisioning servers. It automatically scales based on the volume of incoming events. Common use cases include:

Real-time data processing
Serverless APIs
Automated workflows

Features:

Event-driven execution
Support for multiple runtimes (Node.js, Python, Java, etc.)
Integration with over 200 AWS services and custom applications

Code: Basic Lambda Function
Here’s a simple Node.js Lambda function that logs incoming events:

2. Amazon EventBridge

EventBridge is a serverless event bus that connects AWS services, SaaS applications, and custom applications. It allows you to create loosely coupled, event-driven architectures.

Key Features:

Centralized event routing
Support for custom events and SaaS integrations
Schema discovery for event validation

Use Case: Automatically process new files uploaded to an S3 bucket.

Set Up an Event Rule: Create a rule in EventBridge to trigger a Lambda function when an object is uploaded to an S3 bucket.
Lambda Code to Process S3 Events:
EventBridge Rule Configuration: Use the EventBridge console or AWS CLI to define a rule that filters for s3:ObjectCreated:* events.

Other Supporting Services

Amazon DynamoDB Streams:

Automatically trigger events when data changes in a DynamoDB table.

Amazon SQS and SNS:

Message queuing and notification services for decoupling applications.

AWS Step Functions:

Orchestrates serverless workflows with visual interfaces.

Patterns in Serverless Architectures

1. Microservices

Serverless fits naturally with microservices. Each service can be independently deployed and scaled, communicating through event buses or APIs.

2. Data Processing Pipelines

With AWS Lambda, EventBridge, and Kinesis, you can create real-time or batch data processing pipelines.

Code: Streaming Data Pipeline

Configure Lambda to trigger on data streams from Amazon Kinesis.

3. API Gateways

Combine Amazon API Gateway with Lambda to create serverless APIs. API Gateway handles request routing, authentication, and rate limiting.

Best Practices for Serverless Architectures

Optimize Cold Starts:

Use provisioned concurrency for critical Lambda functions.
Select runtimes with faster startup times (e.g., Node.js, Python).

Design for Scalability:

Use SQS or SNS for handling high-throughput events.
Employ throttling and retry logic to handle API limits.

Monitor and Debug:

Use AWS CloudWatch for logging and monitoring.
Leverage AWS X-Ray for distributed tracing.

Security First:

Apply the principle of least privilege to IAM roles.
Encrypt sensitive data in transit and at rest.

Real-World Applications

E-Commerce:

Implement order processing using Lambda and DynamoDB Streams.
Trigger personalized offers via EventBridge.

IoT Systems:

Use EventBridge to route telemetry data from IoT devices to analytics pipelines.

Gaming:

Real-time player match-making using Lambda and SQS.

Challenges and Solutions

Cold Starts

Lambda functions may take longer to execute when idle for long periods. Mitigate this with provisioned concurrency or keep-alive patterns.

Event Duplication

In event-driven systems, duplicate events can occur. Handle this by implementing idempotent logic in Lambda functions.

Complex Workflows

For multi-step processes, use AWS Step Functions to visualize and manage workflows.

Serverless and event-driven architectures on AWS are revolutionizing how applications are built and scaled. By leveraging AWS Lambda, Amazon EventBridge, and other supporting services, developers can create systems that are cost-efficient, scalable, and resilient. Whether you’re building APIs, processing real-time data, or creating complex workflows, AWS provides the tools and patterns to succeed.

Building Custom Generative Models with AWS: A Comprehensive Tutorial

Drishti Jain — Thu, 04 Jul 2024 21:41:58 +0000

Generative AI models have revolutionized the fields of natural language processing, image generation, and more. Building and fine-tuning these models can seem daunting, but AWS offers a suite of tools and services to streamline the process. In this blog, we will walk through the steps to develop and fine-tune a custom generative model using AWS services.

I’ll cover data preprocessing, model training, and deployment.

Prerequisites

Before we begin, ensure you have the following:

An AWS account
Basic knowledge of Python and machine learning
AWS CLI installed and configured

Step 1: Setting Up Your AWS Environment

1.1. Creating an S3 Bucket

Amazon S3 (Simple Storage Service) is essential for storing the datasets and model artifacts. Let’s create an S3 bucket.

Log in to the AWS Management Console.
Navigate to the S3 service.
Click on “Create bucket.”
Provide a unique name for your bucket and select a region.
Click “Create bucket.”

1.2. Setting Up IAM Roles

IAM (Identity and Access Management) roles allow AWS services to interact securely. Create a role for your SageMaker and EC2 instances.

Navigate to the IAM service.
Click on “Roles” and then “Create role.”
Select “SageMaker” and then “SageMaker — FullAccess.”
Name your role and click “Create role.”

Step 2: Preparing Your Data

Data is the cornerstone of any AI model. For this tutorial, I’ll use a text dataset to build a text generation model. The data preprocessing steps involve cleaning and organizing the data for training.

2.1. Uploading Data to S3

Navigate to your S3 bucket.
Click “Upload” and select your dataset file.
Click “Upload.”

2.2. Data Preprocessing with AWS Glue

AWS Glue is a managed ETL (Extract, Transform, Load) service that can help preprocess your data.

Navigate to the AWS Glue service.
Create a new Glue job.
Write a Python script to clean and preprocess your data. For example:
Run the Glue job and ensure the cleaned dataset is uploaded back to S3.

Step 3: Training Your Generative Model with SageMaker

Amazon SageMaker is a fully managed service that provides every developer and data scientist with the ability to build, train, and deploy machine learning models quickly.

3.1. Setting Up a SageMaker Notebook Instance

Navigate to the SageMaker service.
Click “Notebook instances” and then “Create notebook instance.”
Choose an instance type (e.g., ml.t2.medium for testing purposes).
Attach the IAM role you created earlier.
Click “Create notebook instance.”

3.2. Preparing the Training Script

Next, prepare a training script. For this tutorial, we’ll use a simple RNN model using PyTorch.

3.3. Training the Model

Open your SageMaker notebook instance.
Upload the training script.
Run the script to train the model. Ensure the training data is loaded from S3.

Step 4: Fine-Tuning Your Model

Fine-tuning involves adjusting hyperparameters or further training the model on a more specific dataset to improve its performance.

4.1. Hyperparameter Tuning with SageMaker

Navigate to the SageMaker service.
Click on “Hyperparameter tuning jobs” and then “Create hyperparameter tuning job.”
Specify the training job details and the hyperparameters to tune, such as learning rate and batch size.
Start the tuning job and review the results to select the best model configuration.

4.2. Transfer Learning

Transfer learning can be employed by initializing your model with pre-trained weights and further training it on your specific dataset.

Step 5: Deploying Your Model

Once your model is trained and fine-tuned, it’s time to deploy it for inference.

5.1. Creating a SageMaker Endpoint

Navigate to the SageMaker service.
Click on “Endpoints” and then “Create endpoint.”
Specify the model details and instance type.
Deploy the endpoint.

5.2. Inference with the Deployed Model

Use the deployed endpoint to make predictions.

Building custom generative models with AWS is a powerful way to leverage the scalability and flexibility of the cloud. By using services like S3, Glue, SageMaker, and IAM, you can streamline the process from data preprocessing to model training and deployment. Whether you’re generating text, images, or other forms of content, AWS provides the tools you need to create and fine-tune your generative models efficiently.

Happy modeling!

Thank you for reading. If you have reached so far, please like the article.

Do follow me on Twitter and LinkedIn ! Also, my YouTube Channel has some great tech content, podcasts and much more!

Unveiling the Magic of Serverless Computing with AWS

Drishti Jain — Tue, 21 Nov 2023 21:46:25 +0000

Welcome to the fascinating world of serverless computing, where the cloud does the heavy lifting, and you can focus on what truly matters—building innovative applications. In this journey, I'll take a deep dive into AWS Lambda and explore how it, alongside services like API Gateway and DynamoDB, empowers developers to create scalable and cost-efficient serverless applications.

The Serverless Revolution

Serverless computing isn't about eliminating servers altogether; it's about abstracting server m``anagement away from developers. AWS Lambda, a cornerstone in the serverless ecosystem, allows you to run code without provisioning or managing servers. Sounds like magic? It kind of is!

Imagine writing a piece of code, uploading it to the cloud, and having it automatically executed in response to events—whether it's an HTTP request, changes to data in a database, or the upload of a new file to storage. That's the essence of serverless, and AWS Lambda makes it all possible

AWS Lambda: The Heart of Serverless

At the core of AWS serverless architecture is Lambda, a compute service that runs your code in response to events and automatically manages the computing resources required. The brilliance of Lambda lies in its simplicity and scalability.

Getting Started with AWS Lambda

Getting started with AWS Lambda is a breeze. You write your code, package it up, and upload it to Lambda. The service takes care of the rest—scaling your application in response to incoming traffic, managing compute resources, and ensuring high availability.

Here's a taste of what Lambda supports:

_Programming Languages
_
Lambda supports a variety of programming languages, including Node.js, Python, Java, Go, and .NET Core. This flexibility allows you to choose the language that best suits your application.

Event Sources

Lambda can be triggered by various events, such as changes to data in an Amazon S3 bucket, updates to a DynamoDB table, or HTTP requests through API Gateway. This event-driven architecture ensures that your code executes precisely when needed.

Use Cases for AWS Lambda

The versatility of AWS Lambda extends across a spectrum of use cases, making it a go-to solution for many developers. Here are a few scenarios where Lambda shines:

1. Real-time File Processing with S3 and Lambda:

Imagine you have a bucket in Amazon S3 where users upload images. With Lambda, you can automatically resize or compress these images as soon as they are uploaded, ensuring your application always serves optimized content.

2. RESTful APIs with API Gateway:

AWS Lambda seamlessly integrates with API Gateway, allowing you to build scalable and secure APIs without the need for traditional server management. Define your API, connect it to Lambda functions, and let API Gateway handle the rest, including authentication and request throttling.

3. Scheduled Tasks and Cron Jobs:

Need to run a task at regular intervals? Lambda can be scheduled to execute code at specific times, making it perfect for cron jobs or routine maintenance tasks.

4. Real-time Image Recognition with AWS Rekognition:

Combine the power of Lambda with AWS Rekognition to build an image recognition system that identifies objects, people, text, scenes, and activities in images.

5. IoT Applications:

Lambda plays a pivotal role in IoT applications, processing data from connected devices in real-time and triggering actions based on the incoming data.

The Glue: AWS API Gateway

While Lambda takes care of executing your code, AWS API Gateway acts as the glue, enabling you to create, publish, and manage APIs at any scale. Let's take a closer look at how API Gateway complements AWS Lambda.

Creating APIs without the Overhead
With API Gateway, you can create RESTful APIs without the need to provision servers or manage infrastructure. Define your API endpoints, connect them to Lambda functions, and you have a scalable API ready to handle requests.

Authentication and Authorization
Security is paramount, and API Gateway simplifies it. You can set up authentication mechanisms, control access with API keys, and define fine-grained permissions to secure your APIs.

Rate Limiting and Throttling
Worried about abuse or unexpected spikes in traffic? API Gateway allows you to set rate limits and throttle requests, ensuring your serverless architecture remains robust and responsive.

Monitoring and Logging
Gain insights into your API's performance with built-in monitoring and logging features. API Gateway provides metrics, logs, and tracing, allowing you to identify and troubleshoot issues effectively.

DynamoDB: A NoSQL Powerhouse

Serverless applications often require a database that seamlessly scales with the rest of the infrastructure. Enter Amazon DynamoDB, a fully managed NoSQL database service that integrates seamlessly with AWS Lambda.

Serverless at the Data Layer
DynamoDB is designed for seamless scalability, automatically adjusting its capacity to handle the demands of your application. This aligns perfectly with the serverless paradigm, where resources scale dynamically based on actual usage.

Seamless Integration with Lambda
Lambda can easily interact with DynamoDB to read and write data. This tight integration allows you to build applications where compute and data layers work in harmony, responding to events and delivering results in real-time.

Low-latency, High-throughput
DynamoDB excels in providing low-latency access to data, making it an ideal choice for applications where responsiveness is critical. Whether you're building a gaming leaderboard, a real-time chat application, or an e-commerce platform, DynamoDB delivers the performance you need.

Building a Serverless Application: A Step-by-Step Guide

Now that we've explored the key components of AWS serverless computing, let's walk through the process of building a serverless application. In this example, we'll create a serverless API for a todo list using AWS Lambda, API Gateway, and DynamoDB.

Step 1: Set Up Your Environment

Before diving into code, ensure you have an AWS account. Once that's sorted, set up your AWS CLI and credentials.

Step 2: Create a DynamoDB Table

In the AWS Management Console, navigate to DynamoDB and create a table to store your todo list items. Define a primary key and configure the settings based on your application's needs.

Step 3: Write Your Lambda Functions

Write Lambda functions to perform CRUD (Create, Read, Update, Delete) operations on your todo list items. Use the AWS SDKs for your chosen programming language to interact with DynamoDB.

Step 4: Create an API with API Gateway

Head to API Gateway in the AWS Management Console, and create a new API. Define resources, methods, and integrate them with your Lambda functions. Set up any necessary authentication and deploy your API.

Step 5: Test Your Serverless API

Once your API is deployed, use the provided endpoint URLs to test your serverless application. You can use tools like cURL or Postman to send requests and verify that your todo list API is functioning as expected.

The Future of Serverless

The serverless journey doesn't end here. As AWS continues to innovate and expand its suite of serverless services, the possibilities for what you can achieve with serverless computing are boundless. Whether you're a seasoned developer or just starting, embrace the serverless revolution, experiment with AWS services, and discover new ways to build scalable and efficient applications.

So, what will you build with serverless? The adventure awaits!

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Exploring the Fascinating Intersection of Autism, AI, and Visual Thinking: Insights from Prof Maithilee Kunda’s Research Seminar

Drishti Jain — Sat, 02 Sep 2023 14:30:51 +0000

Have you ever wondered what goes on in the mind when we tackle complex reasoning tasks? How do different individuals approach these challenges, and can artificial intelligence (AI) learn to reason in ways similar to humans? These intriguing questions formed the crux of the enlightening research seminar I recently attended, titled “Reasoning with Visual Imagery: Research at the Intersection of Autism, AI, and Visual Thinking,” presented by the esteemed Prof. Maithilee Kunda.

In a world where AI has made remarkable strides, the realm of high-level reasoning continues to remain a complex puzzle. Prof. Maithilee Kunda delved into the nuances of this topic during her seminar, shedding light on the extensive research conducted in her lab. The core premise she introduced was the quest for creating AI agents capable of reasoning without the need for specialized algorithms or rigorous training procedures.

Prof. Kunda’s research takes an innovative approach, emphasizing the role of visual imagery in reasoning processes. Her team’s work explores how AI can leverage visual cognition to tackle intelligence tests. Moreover, the seminar delved into the fascinating world of neurodiversity, particularly focusing on individuals with autism. The insights garnered from this research provide invaluable perspectives into how different individuals, including those with neurodivergent conditions, approach reasoning tasks successfully.

A highlight of the seminar was Prof. Kunda’s exploration of AI’s interaction with visual imagery. She unveiled her lab’s groundbreaking work, showcasing AI agents’ ability to learn domain knowledge and problem-solving strategies through search and experience. This departure from manually designed components signifies a leap towards AI systems that learn and adapt more autonomously.

One impressive feat discussed was the lab’s participation in the Abstraction & Reasoning Corpus (ARC) ARCathon challenge. The seminar revealed their exceptional results, underscoring the potential of AI to handle intricate tasks like visual abstraction and reasoning. This new paradigm in AI development holds the promise of applications in a plethora of domains, from education to industry.

The seminar did not merely revolve around AI’s capabilities. Prof. Kunda also emphasized the implications of her research for understanding cognitive strategy differences among individuals. These insights have far-reaching applications, particularly in the realm of neurodiversity and employment opportunities for people with unique cognitive approaches, such as individuals with autism.

The seminar also delved into captivating topics like Gestalt principles and approaching through image inpainting. These concepts offer a deep dive into the intricacies of visual thinking and reasoning. One particularly engaging experiment discussed during the seminar was the block design experiment. This experiment demonstrated how the seminar’s overarching theme of reasoning with visual imagery can be practically applied, shedding light on the profound implications of such research.

Prof. Maithilee Kunda’s research seminar was a captivating journey into the realms of high-level reasoning, AI, and visual thinking. Her pioneering work in using visual imagery for intelligence tests and her AI agents’ autonomous learning capabilities are paving the way for a future where AI can reason more naturally and human-like. Furthermore, her insights into cognitive strategy differences and their implications for neurodiversity are commendable steps towards a more inclusive society.

As I reflect on the seminar, I am excited about the future possibilities that this research could unlock. The potential for AI to not only mimic but understand and adapt to human reasoning processes opens the door to transformative applications across industries and domains. Prof. Kunda’s work exemplifies the dynamic interplay between AI and human cognition, ushering in a new era of possibilities that blend cutting-edge technology with the intricacies of our own minds.

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The magic of Generative AI with AWS!

Drishti Jain — Sat, 12 Aug 2023 11:05:09 +0000

Building, training, and deploying machine learning models at scale is now possible for developers and data scientists thanks to Amazon SageMaker, a potent and completely managed service provided by Amazon Web Services (AWS).

SageMaker is an effective and practical framework for developing, training, and deploying Generative Adversarial Networks (GANs) or Variational Autoencoders (VAEs) for a variety of generative applications, including producing music, art, and other generative works.

Key Features of AWS SageMaker:

Scalability
Built-in Algorithms
Custom Model Deployment
Hyperparameter Optimization
Model Versioning
Cost Optimization

Let's look into each of these features before building our very own Generative AI model with AWS.

Scalability: Generative AI models, especially GANs and VAEs, can be computationally intensive and require significant computational resources. With SageMaker, you can easily scale your training and inference tasks to utilize high-performance GPU instances, allowing you to process large datasets and train complex models efficiently.
Built-in Algorithms: SageMaker offers built-in algorithms for Generative AI tasks, including GANs and VAEs. This eliminates the need for manually implementing complex algorithms, saving time and effort for researchers and developers.
Custom Model Deployment: Once you have trained your Generative AI model, SageMaker allows you to deploy it as a real-time endpoint or as a batch transform job. This enables you to use your model for generating new content on-demand or in a batch mode for large-scale processing.
Hyperparameter Optimization: SageMaker provides tools for hyperparameter tuning, enabling automatic search and optimization of hyperparameters for better model performance. This is crucial for tuning the parameters of complex Generative AI models like GANs and VAEs.
Model Versioning: Version control for models is essential for iterative improvements and tracking changes. SageMaker allows you to version your trained models and manage the deployment of different versions.
Cost Optimization: With SageMaker, you can optimize costs by using spot instances for training and deploying your Generative AI models. Spot instances offer significant cost savings, making large-scale experimentation more affordable.

Implementing generative AI algorithms like GANs and VAEs is made simple and comprehensive by AWS SageMaker. Data scientists, researchers, and developers can use Generative AI to create art, produce realistic visuals, and address other imaginative and useful problems since it accelerates the development, training, and deployment processes.

In order to build a GAN the steps involved are:

Import required libraries
Build the Generator Model
Build a Discriminator Model
Preprocess the dataset
Initialize GAN and the Generator and Discriminator Models created
Define the Training Loop.

Let's look at the code implementation

You can also fork the repo - https://github.com/DrishtiJ/GenerativeAI-GAN-AWS and use the code as a boilerplate to build on it.
It is advised to leverage AWS SageMaker's GPU instances for faster training as GAN training can be time-consuming and resource-intensive.

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How to build a data pipeline on AWS

Drishti Jain — Tue, 21 Feb 2023 12:08:02 +0000

AWS is a powerful tool and a great application is to use it to create data pipeline to collect, process, and analyze large amounts of data in real-time. Using a combination of AWS services, we can create a data pipeline that can handle a number of use cases, including data analytics, real-time processing of IoT data, and logging and monitoring.

The key components to build a data pipeline are:

Amazon Kinesis
Amazon Glue
Amazon S3

Developing a data pipeline on AWS might seem complex, but through this blog I aim to help you understand it and help you build a data pipeline on your own.
Let's look at a guided step-by-step process of creating an AWS data pipeline.

Amazon Kinesis

Amazon Kinesis enables to create a stream of data that can be read and processed in real-time. It is a fully managed service that makes it easy to collect, process, and analyze streaming data.
Code to create a new stream and put data into a Kinesis stream using the AWS SDK for Python

AWS Glue

AWS Glue is a fully managed extract, transform, and load (ETL) service that helps to prepare and load data for analytics.
Using Glue, we can create a job to read data from Kinesis stream and write it to an S3 bucket.

Code to create a Glue job using the AWS SDK for Python

AWS S3

AWS S3 is a fully managed object storage service to store and retrieve any amount of data, at any time, from anywhere on the web.

S3 is designed for 99.999999999% (11 9's) of durability, and stores data redundantly across multiple devices in multiple facilities. This makes it an ideal solution for use cases such as data archiving, backup and recovery, and disaster recovery.
Code to create an S3 bucket using the AWS SDK for Python

Additionally Amazon Redshift can be used to run complex queries on your data, and generate insights and reports in near real-time.
AWS ETL Glue can be used for data cleaning, filtering, and transformation tasks on your data, and load it into a target data store, such as Redshift.

Building a data pipeline on AWS is a powerful way to move data efficiently, and with the right tools and techniques, it can be done quickly and easily. With the AWS services you can build robust, scalable, and cost-effective data pipelines that can handle a wide variety of use cases.

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