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 Training (CT)
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- MLOps: Continuous Delivery and Automation Pipelines in Machine Learning, Google Cloud, 2020 (Google Cloud) - This official guide from Google Cloud details the principles and practices of MLOps, including the role of continuous training in maintaining model performance and reliability.
- Practical MLOps: Operationalizing Machine Learning Models, Noah Gift and Alfredo Deza, 2021 (O'Reilly Media) - An authoritative book that covers the entire MLOps lifecycle, with dedicated sections on continuous training, model drift, and pipeline automation.
- MLOps - A systematic review, Anila Syed, Mika Mäntylä, Kai Petersen, and Markus Borg, 2022 Journal of Systems and Software, Vol. 183 (Elsevier) DOI: 10.1016/j.jss.2021.111108 - A recent academic review that systematically maps the MLOps research landscape, providing a scholarly perspective on continuous training within the broader MLOps framework.
- Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications, Chip Huyen, 2022 (O'Reilly Media) - This book offers a comprehensive guide to building robust ML systems, addressing challenges like model drift and the necessity of continuous model updates in production environments.