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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.