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
Introduction to Model Registries
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- Introducing MLOps: How to go from Model to Production, Mark Treveil, Nicolas Omont, Clément Stenac, Kenji Lefevre, Du Phan, Joachim Zentici, Adrien Lavoillotte, Makoto Miyazaki, Lynn Heidmann, 2020 (O'Reilly Media) - A book that offers a comprehensive overview of MLOps principles, practices, and tools, including discussions on model management and registries within the machine learning lifecycle.
- MLflow Model Registry, MLflow Documentation, 2024 (Databricks, Inc.) - Official documentation for MLflow's Model Registry, detailing its features for versioning, metadata tracking, and lifecycle management of machine learning models.
- Amazon SageMaker Model Registry, Amazon Web Services, 2024 (Amazon Web Services) - Official guide to Amazon SageMaker's Model Registry, explaining how to catalog, version, and manage models for deployment across different stages.
- Register and manage models with Model Registry in Vertex AI, Google Cloud Documentation, 2024 (Google Cloud) - Google Cloud's official documentation on Vertex AI Model Registry, outlining its capabilities for centralized model management, versioning, and lifecycle transitions.