Introduction to MLOps
Chapter 1: Foundations of MLOps
What is MLOps?
Why MLOps is Necessary for Machine Learning
MLOps vs. DevOps: Similarities and Differences
The Goals of an MLOps Strategy
Core Principles of MLOps
Common Challenges in Production Machine Learning
Chapter 2: The Machine Learning Lifecycle
Overview of the End-to-End ML Lifecycle
Data Ingestion and Preparation
Model Training and Experimentation
Model Evaluation and Validation
Model Deployment Strategies
Monitoring Models in Production
The Feedback Loop for Model Improvement
Chapter 3: Versioning in Machine Learning
The Importance of Reproducibility
Version Control for Code with Git
Introduction to Data Versioning
Techniques for Model Versioning
Managing Experiment Tracking
Hands-on Practical: Versioning a Simple ML Project
Chapter 4: Automation and CI/CD for ML
Introduction to CI/CD
Continuous Integration (CI) for ML Code
Continuous Delivery (CD) for Models
Continuous Training (CT)
Building a Basic ML Pipeline
Introduction to Orchestration Tools
Hands-on Practical: Create a Simple CI Pipeline with GitHub Actions
Chapter 5: Model Deployment and Serving
What is Model Deployment?
Containerization with Docker
Creating a Model API with Flask
Deployment Patterns: Online vs. Batch Prediction
Introduction to Model Registries
Fundamentals of Model Monitoring
Hands-on Practical: Package and Deploy a Model as a Docker Container
Techniques for Model Versioning
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- MLflow Model Registry, Databricks, 2024 - Official guide for MLflow's centralized system for managing machine learning models, covering versioning, stage transitions, and metadata.
- DVC: Data and Model Versioning, Iterative AI, 2024 (Iterative AI) - Documentation explaining how DVC provides Git-like version control for large data files and models, addressing the limitations of Git for binary artifacts.
- Engineering MLOps: From Model to Production, Emmanuel Raj, Mark Wallace, 2021 (O'Reilly Media) - A guide to building production-ready machine learning systems, with sections dedicated to model versioning and management practices.
- Manage models with Model Registry in Vertex AI, Google Cloud, 2024 (Google Cloud) - Official documentation illustrating how Google Cloud's Vertex AI platform provides a centralized model registry for managing the machine learning model lifecycle.