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
Continuous Integration (CI) for ML Code
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- Introducing MLOps: How to Go from Model to Production, Mark Treveil, Nicolas Omont, Aurélien Géron, Alexander H. G. R. Ikkersheim, Harald Karcher, Paco Nathan, Karl O. P. C. Van Acker, Manuela R. W. Van Acker, 2020 (O'Reilly Media) - This book offers a comprehensive guide to MLOps, including practices for continuous integration, continuous delivery, and continuous training in machine learning systems.
- MLOps: Continuous Delivery and Automation Pipelines in Machine Learning, David Martin, Constantine Golubenko, Hamutal Shiri, David Syer, Danny P. Smith, Janakiram MSV, Evgeniy Smirnov, Jeremy Hylton, 2020 (Google Cloud) - This influential guide from Google Cloud outlines the foundational principles and practices of MLOps, detailing CI/CD for ML pipelines, including data and model validation steps.
- How Do Practitioners Test Machine Learning Systems? An Empirical Study, Coralie Mercier, Walid Maalej, Thorsten W. Joerg, 2021 IEEE Transactions on Software Engineering, Vol. 48 (IEEE) DOI: 10.1109/TSE.2021.3090875 - This empirical study investigates the current practices and challenges of testing machine learning systems in industry, providing a research-backed view on the validation steps discussed in the CI for ML context.