DEV Community: Dhanvina N

Machine Learning Roadmap

Dhanvina N — Tue, 02 Dec 2025 16:46:58 +0000

A Complete Foundation for Becoming a Strong ML Engineer

Machine Learning stands on three fundamental pillars:

1. Mathematics

2. Statistics

3. Programming

Without these foundations, ML becomes just black-box code.

With them, you understand how models work — and how to optimize them.

The Math You Actually Need

1. Linear Algebra — Matrix Manipulation

vectors
matrices
dot products
eigenvalues
PCA & SVD

2. Calculus — Optimization & Gradients

derivatives
partial derivatives
chain rule
gradient descent

3. Probability — Modelling Uncertainty

distributions
random variables
Bayes theorem

4. Statistics — Understanding Data

mean, variance
hypothesis testing
confidence intervals
correlation

Data Manipulation Skills

NumPy

vectorization
broadcasting
matrix ops

Pandas

cleaning data
merging
grouping
time-series ops

Matplotlib

histograms
2D/3D plots

Seaborn

heatmaps
pairplots
correlations

Core Machine Learning Branches

1. Supervised Learning

Regression, classification, neural networks.

2. Unsupervised Learning

Clustering, dimensionality reduction, anomaly detection.

3. Reinforcement Learning

Agents, robotics, decision-making.

How to Learn ML the Right Way

1. Project-Based Learning

Build small projects:

classifiers
clustering visualizations
NLP pipelines
recommender systems

2. Read Latest Hugging Face Research

Stay updated with:

new models
tutorials
research summaries
benchmarks

🏁 Final Thoughts

Machine Learning is built on math, statistics, programming, data skills, and real-world projects.

Master the foundations and you become a strong ML engineer.

AI Engineering Roadmap

Dhanvina N — Tue, 02 Dec 2025 16:16:34 +0000

A complete, practical, industry-level roadmap for becoming an AI engineer who can build real products using LLMs, RAG, agents, fine-tuning, and cloud deployment.

AI Engineering is evolving faster than any other technical field — and the role today is very different from the classic “data scientist” or “ML researcher.”

In 2025, AI Engineers are builders.

They take powerful pretrained models and turn them into real, production-ready AI systems.

If you’re starting your journey, this roadmap breaks down exactly what you need to learn, what you don’t, and how to build a portfolio that gets you hired.

No fluff. Only actionable skills.

🔥 What AI Engineering Really Is

AI Engineering is not about training huge models from scratch.

You do not need deep mathematical knowledge, GPU clusters, or research-level ML.

Instead, AI Engineering focuses on:

1. Adapting Pretrained Models

Modern AI models (GPT, Llama, Mistral, CLIP, SAM, Whisper, etc.) are already incredibly powerful.

Your job is to integrate them, adapt them, and make them useful for real-world problems.

2. Prompt Engineering

Knowing how to write precise, structured, reproducible prompts is a core engineering skill — not a trick.

3. Retrieval-Augmented Generation (RAG)

Connecting LLMs to external data sources to produce reliable, up-to-date answers.

4. Fine-Tuning & LoRA

Lightweight, efficient ways of customizing a model for a specific domain without retraining everything.

5. AI Agents & Orchestration

Agents that reason, plan, take actions, call tools, and work with other agents.

🧠 Core Skills Every AI Engineer Must Have

1. Programming (Python — Production Level)

You must write clean, modular, scalable code.

Understand:

OOP fundamentals
Async programming
Dependency management
Testing (pytest)
Code quality & engineering patterns

2. Version Control (Git)

Not optional.

Branches, PRs, merge strategies, semantic commits.

3. APIs

Both:

Using external APIs (OpenAI, HuggingFace, Replicate, Gemini, etc.)
Creating your own REST APIs using FastAPI or Flask

4. Machine Learning Basics

Enough to understand:

Types of models
Overfitting/underfitting
Train/val/test splits
Evaluation metrics
When and why to use fine-tuning

5. Experimentation with Models

Try different:

LLMs
Vision models
Speech-to-text & text-to-speech models
Embedding models

Know their strengths, weaknesses, and costs.

6. Deployment

You should know how to:

Containerize apps (Docker)
Build scalable inference APIs
Use load balancers & autoscaling
Handle model caching & batching

7. Cloud Platforms

Choose one and get good at it:

AWS
Azure
GCP

Focus on the services that matter for AI:

S3 / Blob Storage
Lambda
EC2
ECS / EKS
API Gateway
CloudWatch

8. Monitoring & Logging

A real AI system must log:

Input/output
Latencies
Failures
Drift
Usage analytics

Tools preferred:

Prometheus
Grafana
Langfuse
Mlflow
Weights & Biases (optional)

🌟 Building a Strong Portfolio (Your Golden Ticket to Jobs)

1. End-to-End Projects

Employers love projects that show:

UI
API
Model adaptation
Deployment
Monitoring

Build real, useful AI systems such as:

PDF chatbot with AI search
AI video analysis tool
Multi-agent workflow automations
Voice assistant for your domain
AI dashboard with monitoring

2. UI + API Skills

A good AI engineer builds:

A clean, functional frontend (React/Next.js)
A robust backend (FastAPI/Django)
A scalable inference system (Docker + Cloud)

3. GitHub

Your GitHub should be:

Clean
Documented
Organized by projects
With clear READMEs

4. Technical Blog Posts

Writing is a superpower.

Publish what you learn on:

Medium
Dev.to
Hashnode

Topics you can write about:

How you built your system
Mistakes you made
What you learned
Costs & optimizations
Benchmarks

🏁 Final Thoughts

AI Engineering is not an academic field.

It’s a builder’s discipline.

If you know how to:

pick the right pretrained model
adapt it
deploy it
scale it
monitor it

You’re already ahead of 95% of people.

This roadmap is your guide, now start building.

Linear Regression

Dhanvina N — Tue, 02 Dec 2025 07:32:54 +0000

Linear Regression Explained Simply (Using Only 3 Houses)

Step 1: Imagine you have only 3 houses

House size (x)	Real price (y)
1	1.5
2	2.2
3	2.9

x = size in thousands of square feet
y = price in hundreds of thousands of dollars

Your goal: predict the price from the size using a straight line.

Step 2: What does a straight line look like?

Every straight-line prediction model follows:

predicted price = (some number) × size + (another number)

We name these numbers:

w → weight/slope
b → bias/intercept

So the model is:

ŷ = w × x + b

Where ŷ means predicted y.

Step 3: Pick a random line to start

Let's guess:

w = 0.5
b = 1.0

Now compute predictions:

x (size)	Prediction ŷ = 0.5x + 1.0	Real y	Error (ŷ − y)
1	1.5	1.5	0
2	2.0	2.2	-0.2
3	3.0	2.9	+0.1

Sometimes we predict low, sometimes high.

Step 4: Convert “a bit wrong” into ONE number

We need a single value describing how bad the line is.
But errors cancel out (e.g., -0.2 + 0.1 ≠ helpful).

So we use two tricks:

1. Square the errors

0² = 0
(-0.2)² = 0.04
(0.1)² = 0.01

2. Take the average

MSE = (0 + 0.04 + 0.01) / 3
    = 0.05 / 3
    ≈ 0.0167

This is the Mean Squared Error (MSE).

Smaller MSE = better line.

Step 5: Try another line and compare

New guess:

w = 0.8
b = 0.7

x	Prediction ŷ = 0.8x + 0.7	Real y	Error	Squared Error
1	1.5	1.5	0	0
2	2.3	2.2	+0.1	0.01
3	3.1	2.9	+0.2	0.04

MSE = (0 + 0.01 + 0.04) / 3
    ≈ 0.0167

Same as before — not better yet.

Step 6: The goal

Try many combinations of w and b until you find the ones that give the smallest possible MSE.

That best pair:

(best w, best b)

is the optimal straight line for your data.

Final takeaway

Linear regression is:

“Find the straight line that makes the average squared error as small as possible.”

Why Not Brute Force Linear Regression? Introducing Gradient Descent

When we try millions of combinations of w and b to find the best line, we are doing brute force search.

Why brute force is a bad idea?

It takes too long.
Trying millions of pairs of parameters becomes extremely slow.
Small datasets → maybe okay.
Real datasets → impossible.
With 3 houses, brute force is fine.
With 100,000 houses, a computer would struggle.

So instead of guessing randomly, we use a far smarter method.

The Clever Trick: Ask the Loss Function for Directions

We treat the MSE (Mean Squared Error) like a landscape:

Every pair (w, b) is a point on the surface.
The height of that point is the MSE at those parameter values.
The lowest height = the best line.

Think of it as a bowl-shaped valley.
Your job is to walk to the bottom.

But here’s the key idea:

Mathematics can tell you exactly which direction is downhill from where you stand.

That direction is called the gradient.

What the Gradient Tells Us

At your current values:

w = 0.5  
b = 1.0  
MSE ≈ 0.0167

We ask:

1. “If I increase w a tiny bit (+0.01), does MSE go up or down?”

MSE goes up → the slope is positive → move w downward (decrease w).

2. “If I increase b a tiny bit (+0.01), does MSE go up or down?”

MSE goes down → the slope is negative → move b upward (increase b).

So the gradient tells us:

Move w slightly down.
Move b slightly up.

The Update Rule (Gradient Descent)

We update both parameters:

new w = current w − (learning rate × slope_w)
new b = current b − (learning rate × slope_b)

Then:

Recalculate the new MSE
Recalculate the slopes
Take another step downhill
Repeat 20–100 times

Instead of testing millions of combinations, we follow the downhill slope directly to the minimum.

Why Linear Regression Still Feels Like a Mystery (And What Is Actually Happening)

Now you know the basic idea of linear regression, but the internal mechanics can still feel mysterious.
This walkthrough removes the mystery by showing exactly what is happening inside gradient descent, step by step, using the same 3-house example.

Our Dataset

x (size)	y (real price)
1	1.5
2	2.2
3	2.9

We start with a random guess:

w = 0.5
b = 1.0

Step 1: Make Predictions

Using ŷ = w·x + b:

House 1 → 0.5×1 + 1.0 = 1.5
House 2 → 0.5×2 + 1.0 = 2.0
House 3 → 0.5×3 + 1.0 = 3.0

Step 2: Compute Errors

error = y − ŷ

House 1: 1.5 − 1.5 = 0
House 2: 2.2 − 2.0 = +0.2
House 3: 2.9 − 3.0 = −0.1

These errors determine how we must adjust w and b.

Step 3: What Happens if w Changes a Little?

Increase w slightly:

new w = 0.51
b stays = 1.0

New predictions:

House 1 → 1.51
House 2 → 2.02
House 3 → 3.06

New errors:

House 1: −0.01
House 2: +0.18
House 3: −0.16

The overall squared error becomes slightly larger.
Conclusion: increasing w makes the model worse → w should be decreased.

That “how much worse” is exactly the gradient with respect to w.

Step 4: The Gradient Formula (No Mystery Anymore)

For linear regression, the exact slope (gradient) of MSE tells us how to update w:

gradient_w = −2 × average(x × error)

Compute it:

House 1 → 1 × 0 = 0
House 2 → 2 × 0.2 = 0.4
House 3 → 3 × (−0.1) = −0.3

Sum = 0 + 0.4 − 0.3 = +0.1
Average = 0.1 / 3 = 0.033
Apply −2:

gradient_w ≈ −0.066

This negative gradient means: decreasing w reduces the error.

Step 5: Gradient for b

gradient_b = −2 × average(error)

Average error = (0 + 0.2 − 0.1) / 3 = 0.033
Apply −2:

gradient_b ≈ −0.066

Same direction: decreasing b reduces error.

Step 6: Update w and b

General update rule:

new_value = old_value − learning_rate × gradient

With a moderate learning rate (for demonstration):

w_new ≈ 0.73
b_new ≈ 0.93

After just one update step, the MSE drops from about 0.0167 to 0.008.
The model is already noticeably better.

What Gradient Descent Is Really Doing

Gradient descent repeatedly performs these simple steps:

Compute each prediction ŷ
Compute each error (y − ŷ)
Multiply errors by x to understand how each house influences w
Average those influence values
Adjust w toward lower error
Adjust b using the average error
Repeat 50–200 times

This is the entire mechanism behind linear regression training.

Linear Regression Explained in Complete Beginner Mode

Part 1: What Linear Regression Is Trying to Do

We have houses.
For each house we know:

x = size of the house
y = real selling price

We want a straight line that predicts price from size:

predicted price = w × x + b

w = how much price increases when size increases by 1
b = base price when size is zero

Our goal is simple:
Find the best possible w and b.

Part 2: How We Measure “Best”

We measure how wrong our line is using MSE (Mean Squared Error).

For each house:

Predict the price: ŷ = w×x + b
Compute error: y − ŷ
Square the error: (y − ŷ)²
Add squared errors for all houses
Divide by number of houses

Formula:

MSE = (1/N) × Σ (y − ŷ)²

Smaller MSE = a better line.

This is the only quantity we try to minimize.

Part 3: The Key Idea — Nudge w and b in the Right Direction

We want to adjust w and b so that MSE gets smaller.

Imagine nudging w slightly upward (by something tiny like +0.0001).
Two possibilities:

If MSE increases → wrong direction; w should move down
If MSE decreases → correct direction; w should move up

The amount MSE changes when w changes slightly is the slope or gradient.

Same idea applies to b.

Part 4: Deriving the Gradient in Simple Arithmetic

Start with one house.
Its squared error is:

(y − (w×x + b))²

Let:

e = y − (w×x + b)

Then squared error = e².

How does e² change when w changes slightly?

A basic math rule:

change in (e²) = 2 × e × (change in e)

What is the change in e when w increases?

e = y − w×x − b

If w increases, w×x increases, so e decreases:

change in e = −x

Thus:

change in (e²) = 2 × e × (−x) = −2 × e × x

This is for one house.

For all houses, we sum and average:

gradient_w = (1/N) × Σ [ −2 × (y − ŷ) × x ]

Gradient for b is easier:

change in e when b changes = −1
gradient_b = (1/N) × Σ [ −2 × (y − ŷ) ]

These are the exact formulas every linear regression library uses.

Part 5: Final Gradient Formulas

gradient_w = −(2/N) × Σ [ (y − ŷ) × x ]
gradient_b = −(2/N) × Σ (y − ŷ)

To reduce MSE, we move opposite the gradient:

w ← w − learning_rate × gradient_w
b ← b − learning_rate × gradient_b

Or, expanding the negatives:

w ← w + learning_rate × (2/N) × Σ [ (y − ŷ) × x ]
b ← b + learning_rate × (2/N) × Σ (y − ŷ)

This is the complete update rule used in gradient descent.

Part 6: Full Example Done Completely by Hand

Our dataset:

x	y
1	1.5
2	2.2
3	2.9

Start with a very poor guess:

w = 0
b = 0

Step 1: Predictions

All predictions are zero:

ŷ1 = 0
ŷ2 = 0
ŷ3 = 0

Errors:

1.5, 2.2, 2.9

Step 2: Update w

Compute average of (error × x):

(1.5×1 + 2.2×2 + 2.9×3) / 3
= (1.5 + 4.4 + 8.7) / 3
= 14.6 / 3
≈ 4.867

With learning rate = 0.1 and factor 2:

new w ≈ 0 + 0.1 × 2 × 4.867 ≈ 0.973

Step 3: Update b

Average error:

(1.5 + 2.2 + 2.9) / 3 = 7.6 / 3 ≈ 2.533

Update:

new b ≈ 0 + 0.1 × 2 × 2.533 ≈ 0.507

After just one update, parameters jump from (0, 0) → approximately (0.97, 0.51).
This is already much closer to the optimal line.

Repeat 20–50 steps and the updates stabilize.
Those final w and b are the best-fitting straight line for the data.

This is exactly what happens inside any machine learning library when you call .fit().

Summary in Plain Language

Start with random w and b.
Compute predictions for all houses.
Compute errors (y − ŷ).
To update w: multiply each error by its x, average them, and nudge w in that direction.
To update b: average all errors and nudge b in that direction.
Repeat until nothing changes.

There is no hidden machinery.
Only simple arithmetic repeated many times.

Two Ways to Solve Linear Regression: Gradient Descent vs the Closed-Form Formula

Now that gradient descent makes sense, it is important to know that there is actually another method to compute the best line. In fact, for simple linear regression, there is a formula that gives the perfect answer in one step with no looping at all.

There are two approaches:

Gradient Descent → takes many small steps, works for any model
Closed-Form Solution (Ordinary Least Squares) → gives exact w and b instantly

For basic linear regression, the closed-form method is faster, simpler, and exact.

The Closed-Form Formula (One-Step Solution)

For simple linear regression with one feature x, the optimal slope and intercept are:

w = Σ[(x − x_mean)(y − y_mean)] / Σ[(x − x_mean)²]
b = y_mean − w × x_mean

This computes the best-fit line in one calculation.

Applying the Formula to Our Example

Dataset:

x	y
1	1.5
2	2.2
3	2.9

Step 1: Compute Means

x_mean = (1 + 2 + 3) / 3 = 2
y_mean = (1.5 + 2.2 + 2.9) / 3 = 2.2

Step 2: Build the Deviation Table

x	y	x−2	y−2.2	(x−2)(y−2.2)	(x−2)²
1	1.5	-1	-0.7	0.7	1
2	2.2	0	0	0	0
3	2.9	1	0.7	0.7	1

Step 3: Sum the Required Columns

Σ(x−mean)(y−mean) = 1.4
Σ(x−mean)² = 2

Step 4: Apply the Formula

w = 1.4 / 2 = 0.7
b = 2.2 − 0.7 × 2 = 0.8

Final model:

price = 0.7 × size + 0.8

Check Against Data

x = 1 → 0.7 + 0.8 = 1.5
x = 2 → 1.4 + 0.8 = 2.2
x = 3 → 2.1 + 0.8 = 2.9

The line fits all three points exactly.

Why This Formula Works (Intuition)

The numerator:

Σ[(x − x_mean)(y − y_mean)]

measures how much x and y move together.

If x is above average and y is also above average → positive contribution
If they move in opposite directions → negative contribution

The denominator:

Σ[(x − x_mean)²]

measures how much x varies on its own.

Thus:

w = (movement together) / (movement of x alone)

Once the slope is fixed, the intercept b simply shifts the line so it passes through the point:

(x_mean, y_mean)

The Matrix Version (for multiple features)

In general linear algebra form:

w = (XᵀX)⁻¹ Xᵀ y

This is the Ordinary Least Squares (OLS) solution.
For one feature, it reduces exactly to the two formulas we computed.

Summary: Gradient Descent vs Closed-Form

Method	Steps Required	Loop Needed	Exact?	Works for Huge Data?
Gradient Descent	Many small updates	Yes	Approximate	Yes
Closed-Form OLS	One computation	No	Exact	Only if data fits RAM

For simple linear regression, the closed-form method is ideal.
For complex models (neural networks, large datasets, many parameters), gradient descent is required.

Why We Still Use Gradient Descent When a Perfect Closed-Form Formula Exists

After learning the closed-form solution for linear regression, it is natural to wonder:

“If we can compute w and b instantly, why do we ever bother with gradient descent?”

The short answer:
The closed-form formula is excellent for small problems, but it breaks down completely once the model or dataset becomes large.
Gradient descent, in contrast, scales to extremely large modern machine-learning problems.

Comparison: Closed-Form vs Gradient Descent

Situation	Closed-Form (OLS)	Gradient Descent	Winner
1 feature, 100 data points	Instant, exact	Works but slower	Closed-form
10 features, 1M data points	Works	Works	Both fine
1,000+ features	Must compute a large XᵀX matrix → high memory	Computes updates step-by-step → efficient	Gradient descent
100,000+ features (e.g., text embeddings)	XᵀX is enormous → cannot fit in RAM	Still works with manageable memory	Gradient descent
Neural networks (millions/billions of parameters)	No closed-form solution exists	Designed to optimize such models	Gradient descent
Streaming/online data	Must recompute from scratch	Updates incrementally	Gradient descent
Add regularization (L1/L2)	Closed-form becomes more complex	Gradient descent only needs a small modification	Gradient descent (usually simpler)

Why Closed-Form Breaks in Real Life

Example: Large tabular dataset

A housing dataset with:

1,000,000 houses
500 features

XᵀX becomes a 500 × 500 matrix → manageable.

But modern machine learning rarely has 500 features.
Instead, consider:

Example: Image or text models

A feature vector might have:

100,000 dimensions (e.g., bag-of-words, embeddings)
or millions of parameters (neural networks)

The closed-form formula requires:

(XᵀX)⁻¹

But XᵀX becomes:

100,000 × 100,000 matrix (10 billion entries)
completely impossible to store or invert

Gradient descent does not require any matrix inversion.
It only needs to compute simple operations on the dataset in batches.

This is why every modern machine learning framework—TensorFlow, PyTorch, JAX—uses gradient-based optimization.

A Simple Way to Remember It

Closed-form (OLS):
Works perfectly, but only for small, simple linear models.
Gradient descent:
Works for linear models, logistic regression, deep learning, transformers, large-scale systems—essentially everything.

scikit-learn’s LinearRegression() uses the closed-form solution because typical tabular datasets are small enough.
TensorFlow and PyTorch use gradient-based methods exclusively because they target large, complex models.

Automate Your Data Workflows: Why Pressing Download Button Isn’t Always Enough!

Dhanvina N — Sun, 25 Aug 2024 14:22:57 +0000

Ever found yourself downloading datasets from Kaggle or other online sources, only to get bogged down by repetitive tasks like data cleaning and splitting? Imagine if you could automate these processes, making data management as breezy as a click of a button! That’s where Apache Airflow comes into play. Let’s dive into how you can set up an automated pipeline for handling massive datasets, complete with a NAS (Network-Attached Storage) for seamless data management. 🚀

Why Automate?

Before we dive into the nitty-gritty, let’s explore why automating data workflows can save you time and sanity:

Reduce Repetition: Automate repetitive tasks to focus on more exciting aspects of your project.
Increase Efficiency: Quickly handle updates or new data without manual intervention.
Ensure Consistency: Maintain consistent data processing standards every time.

Step-by-Step Guide to Your Data Pipeline

Let’s walk through setting up a data pipeline using Apache Airflow, focusing on automating dataset downloads, data cleaning, and splitting—all while leveraging your NAS for storage.

File structure

/your_project/
│
├── dags/
│   └── kaggle_data_pipeline.py      # Airflow DAG script for automation
│
├── scripts/
│   ├── cleaning_script.py           # Data cleaning script
│   └── split_script.py              # Data splitting script
│
├── data/
│   ├── raw/                        # Raw dataset files
│   ├── processed/                 # Cleaned and split dataset files
│   └── external/                  # External files or archives
│
├── airflow_config/
│   └── airflow.cfg                 # Airflow configuration file (if customized)
│
├── Dockerfile                       # Optional: Dockerfile for containerizing
├── docker-compose.yml               # Optional: Docker Compose configuration
├── requirements.txt                # Python dependencies for your project
└── README.md                       # Project documentation

1. Set Up Apache Airflow
First things first, let’s get Airflow up and running.

Install Apache Airflow:

# Create and activate a virtual environment
python3 -m venv airflow_env
source airflow_env/bin/activate

# Install Airflow
pip install apache-airflow

Initialize the Airflow Database:

airflow db init

Create an Admin User:

airflow users create --username admin --firstname Admin --lastname User --role Admin --email admin@example.com

Start Airflow:

airflow webserver --port 8080
airflow scheduler

Access Airflow UI: Go to http://localhost:8080 in your web browser.

2. Connect Your NAS
Mount NAS Storage: Ensure your NAS is mounted on your system. For instance:

sudo mount -t nfs <NAS_IP>:/path/to/nas /mnt/nas

3. Create Your Data Pipeline DAG
Create a Python file (e.g., kaggle_data_pipeline.py) in the ~/airflow/dags directory with the following code:

from airflow import DAG
from airflow.operators.python_operator import PythonOperator
from datetime import datetime, timedelta
import os
import subprocess

# Default arguments
default_args = {
    'owner': 'your_name',
    'depends_on_past': False,
    'start_date': datetime(2024, 8, 1),
    'email_on_failure': False,
    'email_on_retry': False,
    'retries': 1,
    'retry_delay': timedelta(minutes=5),
}

# Define the DAG
dag = DAG(
    'kaggle_data_pipeline',
    default_args=default_args,
    description='Automated Pipeline for Kaggle Datasets',
    schedule_interval=timedelta(days=1),
)

# Define Python functions for each task
def download_data(**kwargs):
    # Replace with your Kaggle dataset URL and credentials
    subprocess.run(["kaggle", "datasets", "download", "-d", "<DATASET_ID>", "-p", "/mnt/nas/data"])

def extract_data(**kwargs):
    # Extract data if it's in a compressed format
    subprocess.run(["unzip", "/mnt/nas/data/dataset.zip", "-d", "/mnt/nas/data"])

def clean_data(**kwargs):
    # Example cleaning script call
    subprocess.run(["python", "/path/to/cleaning_script.py", "--input", "/mnt/nas/data"])

def split_data(**kwargs):
    # Example splitting script call
    subprocess.run(["python", "/path/to/split_script.py", "--input", "/mnt/nas/data"])

# Define tasks
download_task = PythonOperator(
    task_id='download_data',
    python_callable=download_data,
    dag=dag,
)

extract_task = PythonOperator(
    task_id='extract_data',
    python_callable=extract_data,
    dag=dag,
)

clean_task = PythonOperator(
    task_id='clean_data',
    python_callable=clean_data,
    dag=dag,
)

split_task = PythonOperator(
    task_id='split_data',
    python_callable=split_data,
    dag=dag,
)

# Set task dependencies
download_task >> extract_task >> clean_task >> split_task

Create Data Processing Scripts
scripts/cleaning_script.py

import argparse
import os

def clean_data(input_path):
    # Implement your data cleaning logic here
    print(f"Cleaning data in {input_path}...")

if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument('--input', required=True, help="Path to the data directory")
    args = parser.parse_args()

    clean_data(args.input)

scripts/split_script.py

import argparse
import os

def split_data(input_path):
    # Implement your data splitting logic here
    print(f"Splitting data in {input_path}...")

if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument('--input', required=True, help="Path to the data directory")
    args = parser.parse_args()

    split_data(args.input)

Dockerize Your Setup

FROM apache/airflow:2.5.1

USER root

# Install any additional packages
RUN pip install kaggle

# Copy DAGs and scripts
COPY dags/ /opt/airflow/dags/
COPY scripts/ /opt/airflow/scripts/

USER airflow

docker-compose.yml

version: '3'
services:
  airflow-webserver:
    image: apache/airflow:2.5.1
    ports:
      - "8080:8080"
    environment:
      - AIRFLOW__CORE__SQL_ALCHEMY_DATABASE_URI=sqlite:///airflow.db
      - AIRFLOW__CORE__EXECUTOR=LocalExecutor
    volumes:
      - ./dags:/opt/airflow/dags
      - ./scripts:/opt/airflow/scripts
    command: webserver

  airflow-scheduler:
    image: apache/airflow:2.5.1
    environment:
      - AIRFLOW__CORE__SQL_ALCHEMY_DATABASE_URI=sqlite:///airflow.db
      - AIRFLOW__CORE__EXECUTOR=LocalExecutor
    volumes:
      - ./dags:/opt/airflow/dags
      - ./scripts:/opt/airflow/scripts
    command: scheduler

Run Your Pipeline
Start Airflow Services:

docker-compose up

Monitor Pipeline:

Access the Airflow UI at http://localhost:8080 to trigger and monitor the pipeline

GitHub Actions Setup
GitHub Actions allows you to automate workflows directly within your GitHub repository. Here’s how you can set it up to run your Dockerized pipeline:

Create GitHub Actions Workflow
Create a .github/workflows Directory:

mkdir -p .github/workflows

Create a Workflow File:

.github/workflows/ci-cd.yml

name: CI/CD Pipeline

on:
  push:
    branches:
      - main
  pull_request:
    branches:
      - main

jobs:
  build:
    runs-on: ubuntu-latest
    steps:
      - name: Checkout code
        uses: actions/checkout@v3

      - name: Set up Docker Buildx
        uses: docker/setup-buildx-action@v2

      - name: Build and push Docker image
        uses: docker/build-push-action@v4
        with:
          context: .
          push: true
          tags: your_dockerhub_username/your_image_name:latest

      - name: Run Docker container
        run: |
          docker run -d --name airflow_container -p 8080:8080 your_dockerhub_username/your_image_name:latest

4. What’s Happening Here?

download_data: Automatically downloads the dataset from Kaggle to your NAS.
extract_data: Unzips the dataset if needed.
clean_data: Cleans the data using your custom script.
split_data: Splits the data into training, validation, and testing sets.

5. Run and Monitor Your Pipeline
Access the Airflow UI to manually trigger the DAG or monitor its execution.
Check Logs for detailed information on each task.

6. Optimize and Scale
As your dataset grows or your needs change:

Adjust Task Parallelism: Configure Airflow to handle multiple tasks concurrently.
Enhance Data Cleaning: Update your cleaning and splitting scripts as needed.
Add More Tasks: Integrate additional data processing steps into your pipeline.

Conclusion

Automating your data workflows with Apache Airflow can transform how you manage and process datasets. From downloading and cleaning to splitting and scaling, Airflow’s orchestration capabilities streamline your data pipeline, allowing you to focus on what really matters—analyzing and deriving insights from your data.

So, set up your pipeline today, kick back, and let Airflow do the heavy lifting!