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 Delivery (CD) for Models
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- Continuous Delivery: Reliable Software Releases through Build, Test, and Deployment Automation, Jez Humble, David Farley, 2022 (Addison-Wesley Professional) - This foundational book establishes the core principles and practices of Continuous Delivery in software engineering, providing the concepts that are adapted for machine learning models.
- MLOps: Continuous delivery and automation pipelines in machine learning, Google Cloud, 2020 - This whitepaper details MLOps best practices, focusing on how to implement continuous integration, continuous delivery, and continuous training for machine learning systems on Google Cloud, highly relevant for practical implementation details.
- Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications, Chip Huyen, 2022 (O'Reilly Media) - This book offers insights into designing and deploying ML systems, including discussions on model serving, deployment patterns like canary and blue/green, and various testing strategies applicable to continuous delivery of models.