Complete MLOps Bootcamp

A comprehensive repository containing 10+ end-to-end Machine Learning projects following the Complete MLOps Bootcamp by Krish Naik on Udemy. This repository covers the entire MLOps lifecycle from data ingestion to model deployment and monitoring.

📚 Course Structure

This repository is organized into modules that cover different aspects of MLOps:

🏗️ Core MLOps Components

• 1-Introduction - MLOps fundamentals and overview
• 2-MLFLOW - Experiment tracking and model registry
• 3-DVC - Data version control and pipeline orchestration
• 4-Dagshub - Git-based ML platform integration
• 5-Mlpipeline - Building ML pipelines
• 6-MLflow with AWS - Cloud integration for model management

🐳 Containerization & Orchestration

• 7-Dockers - Containerizing ML applications
• 8-Apache Airflow - Workflow orchestration and scheduling
• 9-ETL Pipeline - Data extraction, transformation, and loading

🚀 CI/CD & Deployment

• 10-Github Actions - Automated workflows and CI/CD
• 11-Github Actions with DockerHub - Container-based deployments

🎯 End-to-End Projects

• 12-End-End DataScience Project - Complete ML pipeline implementation
• 13-End to End Network Security Project - ML for cybersecurity applications
• 14-End to End NLP Text Summarizer - Natural Language Processing project
• 15-AWS Sagemaker - Cloud-based ML platform
• 16-Grafana - Monitoring and visualization
• 17-LLmOps - Large Language Model operations

🛠️ Technologies Covered

• Experiment Tracking: MLflow, Dagshub
• Version Control: Git, DVC
• Containerization: Docker
• Orchestration: Apache Airflow
• CI/CD: GitHub Actions
• Cloud Platforms: AWS, Sagemaker
• Monitoring: Grafana
• ML Frameworks: TensorFlow, Keras, Scikit-learn
• Data Processing: Pandas, NumPy
• Hyperparameter Tuning: Hyperopt

🎯 Skills Acquired

🤖 Machine Learning Operations

• ML Lifecycle Management: Complete understanding of ML model development, deployment, and monitoring
• Experiment Tracking: Systematic tracking of model experiments, parameters, and metrics
• Model Versioning: Managing different versions of models and datasets
• Automated ML Pipelines: Building end-to-end automated machine learning workflows

🔧 DevOps for ML

• Containerization: Packaging ML applications using Docker
• CI/CD for ML: Implementing continuous integration and deployment for ML models
• Infrastructure as Code: Managing ML infrastructure using code
• Workflow Orchestration: Designing and managing complex ML workflows with Airflow

📊 Data Engineering

• Data Version Control: Tracking and managing dataset versions with DVC
• ETL Pipeline Development: Building robust data extraction, transformation, and loading processes
• Data Validation: Implementing data quality checks and schema validation
• Feature Engineering: Creating and managing ML features systematically

☁️ Cloud & Deployment

• Cloud ML Platforms: Working with AWS Sagemaker for model training and deployment
• Scalable Deployments: Deploying ML models at scale using cloud services
• Model Monitoring: Setting up monitoring and alerting for ML models in production
• A/B Testing: Implementing model comparison and testing strategies

🔍 Advanced ML Techniques

• Hyperparameter Optimization: Automated tuning of model parameters
• Model Evaluation: Comprehensive model performance assessment
• NLP Operations: Managing and deploying natural language processing models
• LLM Operations: Working with large language models in production
• Network Security ML: Applying ML to cybersecurity use cases

📈 Monitoring & Observability

• Model Performance Monitoring: Tracking model drift and performance degradation
• Log Management: Collecting and analyzing ML system logs
• Metrics Visualization: Creating dashboards for ML system monitoring
• Alert Systems: Setting up automated alerts for model issues

🛠️ Software Engineering Best Practices

• Code Organization: Structuring ML projects for maintainability and scalability
• Configuration Management: Managing project configurations and parameters
• Testing ML Systems: Implementing unit tests and integration tests for ML pipelines
• Documentation: Creating comprehensive documentation for ML projects

📋 Prerequisites

Before starting with the projects, ensure you have:

• Python 3.8+ installed
• Git configured
• Docker installed (for containerization projects)
• AWS account (for AWS-related projects)
• Basic understanding of Machine Learning concepts

🚀 Getting Started

1. Clone the repository

   git clone https://github.com/Suraj-G-Rao/Complete-MLOPS.git
   cd Complete-MLOPS

2. Install dependencies

   pip install -r requirements.txt

3. Navigate to the desired module

   cd "12-End-End DataScience Project"

4. Follow the module-specific README for detailed instructions

📁 Project Structure

Each module follows a consistent structure:

• Configuration files (config.yaml, schema.yaml, params.yaml)
• Source code (src/ directory)
• Research notebooks (research/ directory)
• Templates (templates/ directory)
• Main execution scripts (main.py, app.py)

🔧 Common Workflow

For most end-to-end projects, follow these steps:

1. Update configuration files

   • config.yaml - Project configuration
   • schema.yaml - Data schema definitions
   • params.yaml - Model parameters

2. Update components

   • Entity classes
   • Configuration manager
   • Individual components (data ingestion, transformation, etc.)

3. Build the pipeline

   • Update pipeline configuration
   • Modify main execution script

📊 ML Pipeline Stages

Typical ML pipeline includes:

1. Data Ingestion - Collect and load data
2. Data Validation - Validate data quality and schema
3. Data Transformation - Feature engineering and preprocessing
4. Model Training - Train ML models with hyperparameter tuning
5. Model Evaluation - Evaluate model performance using MLflow/Dagshub
6. Model Deployment - Deploy models to production
7. Monitoring - Track model performance and drift

🤝 Contributing

This repository is part of a learning journey. Feel free to:

• Fork the repository
• Create issues for bugs or improvements
• Submit pull requests
• Share your learning experience

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• Krish Naik - For the excellent MLOps Bootcamp course on Udemy
• MLOps Community - For valuable resources and best practices
• Open Source Contributors - For the amazing tools and libraries used

📞 Contact

• Course: Complete MLOps Bootcamp With 10+ End To End ML Projects
• Platform: Udemy

Note: This repository is for educational purposes. Please ensure you have the necessary permissions and licenses for any production use of the code and models.