MLOps Fundamentals

MLOps Principles and Lifecycle

• What you Need to Know

  • MLOps Core Principles

    • Automation of ML pipeline components and workflows
    • Continuous integration and deployment for ML systems
    • Monitoring and observability for ML model performance
    • Reproducibility and versioning of data, code, and models
    • Resources:
      • MLOps Principles - Foundational MLOps concepts and practices
      • Google MLOps Guide - MLOps maturity levels and implementation
      • MLOps Manifesto - Community-driven MLOps best practices

  • ML Model Lifecycle Management

    • Model development, training, and validation phases
    • Model deployment, serving, and retirement processes
    • Model versioning and experiment tracking
    • Resources:
      • ML Model Lifecycle - Azure ML model management
      • Model Management Best Practices - ML system design patterns
      • MLflow Model Registry - Centralized model store

  • MLOps Maturity Levels

    • Level 0: Manual process and ad-hoc deployment
    • Level 1: ML pipeline automation and continuous training
    • Level 2: CI/CD pipeline automation for ML systems
    • Resources:
      • MLOps Maturity Model - Microsoft MLOps maturity framework
      • MLOps Maturity Assessment - Google MLOps levels
      • MLOps Capability Model - MLOps stack and capabilities

Experiment Tracking and Model Registry

• What you Need to Know

  • MLflow for Experiment Management

    • Experiment tracking and parameter logging
    • Model packaging and artifact management
    • Model registry and lifecycle management
    • Resources:
      • MLflow Documentation - Complete MLflow platform guide
      • MLflow Tracking - Experiment logging and management
      • MLflow Tutorial - Hands-on MLflow examples

  • Weights & Biases for Advanced Tracking

    • Experiment visualization and comparison
    • Hyperparameter optimization and sweeps
    • Model performance monitoring and alerts
    • Resources:
      • Weights & Biases Documentation - Advanced experiment tracking
      • W&B Quickstart - Getting started with experiment tracking
      • W&B Examples - Practical W&B implementations

  • Alternative Tracking Solutions

    • Neptune for collaborative ML development
    • TensorBoard for TensorFlow experiment tracking
    • Comet for comprehensive ML experiment management
    • Resources:
      • Neptune Documentation - Collaborative ML development platform
      • TensorBoard Guide - TensorFlow visualization toolkit
      • Comet Documentation - ML experiment management platform

Version Control for ML

• What you Need to Know

  • Data Version Control (DVC)

    • Data versioning and pipeline reproduction
    • Remote storage integration and data sharing
    • Pipeline definition and dependency tracking
    • Resources:
      • DVC Documentation - Data version control system
      • DVC Tutorial - Getting started with data versioning
      • DVC with Git - Git-based ML workflows

  • Git-based ML Workflows

    • Branch strategies for ML development
    • Code review processes for ML projects
    • Git hooks and automation for ML workflows
    • Resources:
      • Git for Data Science - Version control best practices
      • GitFlow for ML - Branching strategies for ML projects
      • Pre-commit Hooks - Automated code quality checks

  • Model Versioning Strategies

    • Semantic versioning for ML models
    • Model lineage and dependency tracking
    • Model comparison and A/B testing frameworks
    • Resources:
      • Model Versioning Best Practices - ML model version management
      • ML Model Lineage - Tracking model ancestry
      • Model Comparison Frameworks - Model drift and comparison tools

Continuous Integration for ML

• What you Need to Know

  • CI/CD Pipeline Design for ML

    • Automated testing for ML code and data
    • Model validation and performance testing
    • Deployment automation and rollback strategies
    • Resources:
      • CI/CD for Machine Learning - Continuous delivery for ML systems
      • GitHub Actions for ML - CI/CD automation platform
      • GitLab CI/CD for ML - Integrated DevOps for ML

  • Automated Testing for ML Systems

    • Unit testing for ML code and functions
    • Integration testing for ML pipelines
    • Model performance and accuracy testing
    • Resources:
      • Testing ML Systems - Comprehensive ML testing guide
      • pytest for ML - Python testing framework
      • Great Expectations - Data quality and validation testing

  • Code Quality and Linting for ML

    • Code formatting and style enforcement
    • Static analysis and security scanning
    • Documentation generation and maintenance
    • Resources:
      • Black Code Formatter - Python code formatting
      • Flake8 Linting - Python code quality tools
      • mypy Type Checking - Static type checking for Python

Infrastructure and Environment Management

• What you Need to Know

  • Containerization for ML Workloads

    • Docker best practices for ML applications
    • Multi-stage builds and image optimization
    • GPU support and CUDA container configuration
    • Resources:
      • Docker for ML - Containerization fundamentals
      • ML Docker Images - Pre-built ML container images
      • NVIDIA Docker - GPU support in containers

  • Environment Reproducibility

    • Conda and pip environment management
    • Requirements.txt and environment.yml best practices
    • Virtual environment isolation strategies
    • Resources:
      • Conda Documentation - Package and environment management
      • Poetry for ML - Modern Python dependency management
      • Pipenv Guide - Python virtual environment tool

  • Cloud Environment Setup

    • Cloud-based development environments
    • Jupyter notebook and lab configurations
    • Remote development and collaboration tools
    • Resources:
      • Google Colab - Free cloud-based ML environment
      • AWS SageMaker Studio - Integrated ML development environment
      • Azure ML Compute Instances - Cloud-based ML development

Data Management and Governance

• What you Need to Know

  • Data Pipeline Architecture

    • ETL/ELT processes for ML data preparation
    • Data quality monitoring and validation
    • Data lineage and impact analysis
    • Resources:
      • Apache Airflow - Workflow orchestration for data pipelines
      • Prefect - Modern workflow orchestration
      • Luigi - Python pipeline framework

  • Feature Store Implementation

    • Centralized feature management and serving
    • Feature versioning and lineage tracking
    • Online and offline feature serving
    • Resources:
      • Feast Feature Store - Open-source feature store
      • Tecton Feature Platform - Enterprise feature store concepts
      • AWS Feature Store - SageMaker feature store

  • Data Security and Privacy

    • Data encryption and access controls
    • Privacy-preserving ML techniques
    • Compliance with data protection regulations
    • Resources:
      • Data Security Best Practices - OWASP security guidelines
      • Differential Privacy - Privacy-preserving ML techniques
      • GDPR Compliance for ML - Data protection regulation compliance

Model Development Best Practices

• What you Need to Know

  • Reproducible ML Experiments

    • Seed management and deterministic training
    • Configuration management and parameter tracking
    • Environment and dependency documentation
    • Resources:
      • Reproducible ML - Johns Hopkins reproducibility course
      • Hydra Configuration - Configuration management for ML
      • Sacred Experiment Management - Experiment configuration and tracking

  • Model Development Workflows

    • Iterative model development and validation
    • Cross-validation and hyperparameter tuning
    • Model selection and ensemble techniques
    • Resources:
      • Scikit-learn Pipeline - ML workflow construction
      • Optuna Hyperparameter Optimization - Automated hyperparameter tuning
      • Model Selection Guide - Model evaluation and selection

  • Code Organization and Modularity

    • Project structure and code organization
    • Modular ML code design patterns
    • Configuration-driven development
    • Resources:
      • Cookiecutter Data Science - Project template for ML
      • ML Code Structure - ML project organization
      • Clean Code for ML - Code quality best practices

Collaboration and Team Workflows

• What you Need to Know

  • Cross-functional Team Collaboration

    • Data scientist and engineer collaboration patterns
    • Model handoff and deployment processes
    • Communication and documentation standards
    • Resources:
      • Team Data Science Process - Microsoft team collaboration framework
      • ML Team Collaboration - Product management for AI/ML
      • Documentation Best Practices - Technical documentation guide

  • Code Review for ML Projects

    • ML-specific code review guidelines
    • Model validation and testing review
    • Performance and efficiency review criteria
    • Resources:
      • ML Code Review Guide - Microsoft ML code review guidelines
      • Google ML Style Guide - Code style and review standards
      • Pull Request Best Practices - GitHub collaboration guide

Ready to Build Pipelines? Continue to Module 2: ML Pipelines to master automated training workflows, data processing pipelines, and continuous model development.