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
The Goals of an MLOps Strategy
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- Introducing MLOps: How to Go from Idea to Production, Mark Treveil, Nicolas Omont, Aurélien Géron, Clément Stenac, Cécile Tran, Andreas Brauchli, Noah Steward, João Moura, Michel Tugendhat, Larysa Visengeriyeva, Harley Davidson, Alexey Goldin, Justin Francis, John D. K. Miller, Roger B. Chen, David S. D. Jones, and Sallyann Freudenberg, 2020 (O'Reilly Media) - A practical guide to MLOps, outlining its principles, benefits, and how to implement a complete MLOps strategy, covering automation, reproducibility, reliability, and collaboration.
- MLOps: Continuous delivery and automation for machine learning, V. Lakshmanan, S. G. Chandrasekaran, S. S. Padmanabhan, V. J. Raman, M. J. F. Johnson, and D. L. Martin, 2022 (Google Cloud) - An authoritative whitepaper from Google Cloud that defines MLOps, outlines its principles, and details how to implement continuous delivery and automation for machine learning, directly supporting the stated goals.
- MLOps: A Comprehensive Definition, Principles, and a Framework for Machine Learning Operations, Jörg Kietzmann, Andreas K. Steiner, Michael K. Reiss, Christian J. F. Maass, 2022 ACM Computing Surveys, Vol. 55 (Association for Computing Machinery) DOI: 10.1145/3549727 - Provides a thorough academic definition of MLOps, its underlying principles, and a structured framework. It offers a research-backed perspective on the goals and components of an MLOps strategy.