🚀 MLOps Learning Journey

A comprehensive MLOps learning repository covering Python fundamentals to advanced machine learning operations, experiment tracking, and deployment strategies.

📋 Table of Contents

• 🎯 Overview
• 🛠️ Prerequisites
• 📚 Learning Path
• 🚀 Quick Start
• 📖 Module Breakdown
• 🔧 Tools & Technologies
• 💡 Projects
• 🧱 Additional Tech Stack
• 📘 Resources

🎯 Overview

This repository serves as a complete learning resource for Machine Learning Operations (MLOps), starting from Python fundamentals and progressing to advanced MLOps practices. Whether you're a beginner or looking to enhance your MLOps skills, this structured curriculum will guide you through:

• Python Programming Fundamentals
• Data Analysis & Manipulation
• Machine Learning Model Development
• Experiment Tracking with MLflow
• Cloud-based MLflow on AWS Infrastructure
• AWS SageMaker for Enterprise ML
• Data Version Control with DVC
• Model Deployment & Monitoring
• Web Application Development with Flask

🛠️ Prerequisites

• Basic understanding of programming concepts
• Python 3.8 or higher installed
• Git for version control
• Jupyter Notebook/Lab environment
• Basic knowledge of machine learning concepts (helpful but not required)

📚 Learning Path

📚 Updated Learning Path

graph TD
    A[Python Basics] --> B[Control Flow]
    B --> C[Data Structures]
    C --> D[Functions & Modules]
    D --> E[OOP Concepts]
    E --> F[Exception Handling]
    F --> G[File Operations]
    G --> H[Advanced Python]
    H --> I[Data Analysis]
    I --> J[Database Operations]
    J --> K[Logging]
    K --> L[Flask Web Development]
    L --> M[MLflow & Experiment Tracking]
    M --> N[MLflow on AWS Cloud]
    N --> O[DVC - Data Version Control]
    O --> P[Dagshub Integration]
    P --> Q[AWS EC2 + S3 Deployment]
    Q --> R[Docker Containerization]
    R --> S[Apache Airflow Workflows]
    S --> T[GitHub Actions CI/CD]
    T --> U[AWS Beanstalk + Azure Deployment]
    U --> V[Hugging Face Integration & Fine-Tuning]
    V --> W[AWS SageMaker Pipeline]
    W --> X[Grafana for Monitoring]
    X --> Y[Amazon Bedrock - GenAI]

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🚀 Updated Quick Start

1. Clone the repository:

   git clone https://github.com/jagadeshchilla/MLOPS.git
   cd MLOPS

2. Set up virtual environment:

   python -m venv venv
   source venv/bin/activate  
   venv\Scripts\activate

3. Install dependencies:

   pip install -r mlflow/requirements.txt

4. Start Jupyter Lab:

   jupyter lab

5. Launch MLflow UI:

   mlflow ui

📖 Module Breakdown

🐍 Python Fundamentals (Modules 1-9)

Module	Topic	Description	Key Concepts
1	Python Basics	Variables, data types, operators	Foundation concepts
2	Control Flow	Conditional statements, loops	Decision making & iteration
3	Data Structures	Lists, tuples, sets, dictionaries	Data organization
4	Functions	Function definition, lambda, map/filter	Code reusability
5	Modules	Import systems, packages	Code organization
6	File Handling	File I/O operations	Data persistence
7	Exception Handling	Error handling, custom exceptions	Robust programming
8	OOP	Classes, inheritance, polymorphism	Object-oriented design
9	Advanced Python	Iterators, generators, decorators	Advanced techniques

📊 Data Science & Analysis (Module 10)

• NumPy: Numerical computing and array operations
• Pandas: Data manipulation and analysis
• Matplotlib: Data visualization and plotting
• Seaborn: Statistical data visualization
• Data Processing: Reading from CSV, Excel, and various formats

🗄️ Database Operations (Module 11)

• SQLite3: Database creation and management
• CRUD Operations: Create, Read, Update, Delete
• Data Integration: Connecting Python with databases

📝 Logging & Monitoring (Module 12)

• Python Logging: Structured logging practices
• Multiple Loggers: Advanced logging configurations
• Log Management: Best practices for production systems

🌐 Web Development (Module 13)

• Flask Framework: Web application development
• API Development: RESTful API creation
• Template Rendering: Dynamic web pages
• Static Files: CSS, JavaScript integration

🐳 Docker Containerization

• Container Orchestration: Docker-based application deployment
• Alpine Linux: Lightweight, secure container base images
• Flask Containerization: Production-ready web application containers
• DevOps Integration: CI/CD pipeline integration with Docker
• Microservices Architecture: Scalable, containerized service deployment

🌊 Apache Airflow & Workflow Orchestration

• DAG Development: Directed Acyclic Graph workflow creation
• Task Scheduling: Automated task execution and dependency management
• MLOps Pipeline Orchestration: End-to-end ML workflow automation
• Astro CLI Integration: Modern development and deployment tooling
• Monitoring & Observability: Comprehensive workflow monitoring and alerting

🚀 GitHub Actions CI/CD

• Continuous Integration: Automated code validation and testing
• Continuous Deployment: Streamlined deployment workflows
• Automated Testing: Comprehensive test suite with pytest
• Code Quality Gates: Flake8 linting and coverage reporting
• Multi-Python Support: Matrix testing across Python 3.8, 3.9, 3.10
• Security Scanning: Dependency auditing and vulnerability detection

🔬 MLflow & Experiment Tracking

• Experiment Tracking: Model versioning and metrics logging
• Model Registry: Centralized model management
• Deployment: Model serving and monitoring
• Hyperparameter Tuning: Automated optimization workflows

📂 Data Version Control (DVC)

• DVC Init & Setup: Track large data files and models
• Data Pipelines: Automate preprocessing and training steps
• Versioning: Manage dataset/model history like Git
• Remote Storage: Push/pull from S3, GDrive, or DagsHub

🌐 DagsHub Integration

• End-to-End MLOps Platform: Combines Git, DVC, MLflow
• Experiment Logging: Track experiments on DagsHub via MLflow
• Collaboration: Share datasets, models, and runs
• Visualization: View lineage, metrics, and model artifacts

☁️ Cloud Deployments (AWS & Azure)

• AWS EC2 + S3: Host MLflow tracking server, manage artifacts
• Elastic Beanstalk: Deploy production-ready ML web apps
• Azure App Services: Cross-cloud deployment of Flask/API apps
• CodePipeline & IAM: CI/CD automation and role-based security

📊 Grafana Monitoring Integration

• Real-time Dashboarding: Monitor training logs, system metrics
• Custom Alerts: Track data drift, performance, and failures
• CloudWatch + Grafana: Unified AWS observability
• ML Model Monitoring: Visualize accuracy, latency, loss over time

🔧 Tools & Technologies

Technologies i have learned so far

Core Technologies

• Python 3.8+
• Jupyter Notebooks
• Version Control

Data Science Stack

• NumPy
• Pandas
• Matplotlib
• Seaborn

Machine Learning & MLOps

• Scikit-learn
• MLflow
• DVC
• DagsHub
• TensorFlow
• Keras
• Hugging Face

Web Development

• Flask
• HTML5
• CSS3

Containerization & DevOps

• Docker
• Alpine Linux

Workflow Orchestration

• Apache Airflow
• Astro CLI

CI/CD & Automation

• GitHub Actions
• AWS CodePipeline
• Pytest
• Automated Quality Gates

Cloud Infrastructure

• Amazon Web Services
• Elastic Compute Cloud
• Simple Storage Service
• Relational Database Service
• Elastic Container Registry
• Elastic Beanstalk
• Machine Learning Platform
• Identity & Access Management
• Amazon Bedrock
• Microsoft Azure

Databases & Monitoring

• MySQL
• Grafana

💡 Projects

I have done some projects along with learning this tech stack — it was a wonderful journey!
Each project helped reinforce practical MLOps, data science, DevOps, and cloud deployment skills.
Check out the table below to explore my work:

Project Name	Description	Tech Stack	Link
House Price Prediction with MLflow	ML model training and experiment tracking with MLflow		Repo
ANN with MLflow	Neural network model tracked and visualized with MLflow		Repo
DVC & MLflow Pipeline	ML pipeline with DVC and MLflow for versioning and tracking		Repo
NASA APOD ETL Pipeline	ETL pipeline using Apache Airflow to pull data from NASA API		Repo
GitHub Actions Test	Simple CI/CD pipeline using GitHub Actions		Repo
Dockerized Flask App	Flask app containerized using Docker		Repo
Wine Quality Prediction	ML model for predicting wine quality		Repo
Phishing Detection API	ML API to detect phishing websites		Repo
Student Performance (EC2)	ML deployment on AWS EC2 using Docker + CI/CD		Repo
Student Performance (Beanstalk)	Flask ML app deployed via AWS Elastic Beanstalk		Repo
Student Performance (Azure)	Flask app deployed to Azure App Service		Repo
Text Summarizer API	Text summarization using Transformers and Hugging Face		Repo
Mobile Classification (SageMaker)	Mobile price classification using AWS SageMaker		Repo

🧱 Additional Tech Stack

I have learned this tech stack as part of my MLOps journey — spanning model development, experiment tracking, containerization, orchestration, automation, and deployment.

✅ Learned MLOps Tools & Libraries

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🚀 Other MLOps Tools I'm Planning to Explore

Here are some powerful tools I aim to explore and implement in upcoming projects for a complete MLOps lifecycle:

These tools will help me advance into scalable and enterprise-ready MLOps workflows using Kubernetes, event-driven pipelines, and real-time monitoring.

📘 Resources

Here are some of the learning materials and official documentation that helped me build strong foundations in MLOps and Cloud-based ML systems:

🎓 Courses

• Complete MLOps Bootcamp: Machine Learning Operations for Beginners (Udemy)

📚 Official Documentation

• Python Docs
• MLflow Documentation
• DVC Docs
• Dagshub Docs
• Docker Docs
• Apache Airflow Docs
• GitHub Actions Docs
• AWS EC2 Docs
• AWS S3 Docs
• AWS SageMaker Docs
• AWS Bedrock Docs
• AWS RDS Docs
• AWS Elastic Beanstalk Docs
• Azure Docs
• Grafana Docs
• FastAPI Docs
• Hugging Face Transformers Docs
• TensorFlow Docs
• Keras Docs
• Jupyter Notebook Docs

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Happy Learning! 🚀

"The journey of a thousand models begins with a single commit."