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
Overview of the End-to-End ML Lifecycle
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- Engineering MLOps: An End-to-End Guide to Design, Implement and Manage Production-Ready Machine Learning Systems, Emmanuel Raj, Larysa Visengeriyeva, Michael Nguyen, and David S. Chou, 2021 (Packt Publishing) - A comprehensive book that systematically covers the various stages of the ML lifecycle within an MLOps framework, from data to monitoring.
- MLOps: Continuous Delivery and Automation for Machine Learning on Google Cloud, Google Cloud, 2022 Google Cloud Whitepaper (Google Cloud) - An authoritative whitepaper detailing Google's perspective on MLOps, focusing on automation and continuous practices throughout the ML lifecycle.
- Practical MLOps: How to Take Machine Learning Models from Prototype to Production, Noah Gift, Alfredo Deza, 2021 (O'Reilly Media) - A practical guide that walks through the implementation of each stage of the ML lifecycle for building and managing production machine learning systems.