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
Core Principles of MLOps
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- MLOps: Continuous delivery and automation pipelines in machine learning, Vishnu Rachakonda, Gaurav Chakravorty, and Haryadi Sugiarto, 2021 (Google Cloud) - A foundational guide outlining best practices for implementing MLOps, focusing on automation, continuous integration, continuous delivery, and continuous training.
- Introducing MLOps: How to go from model to production, Mark Treveil, Nicolas Omont, Aurélien Geron, Noah Gift, Alexey Grigorev, Ines Montani, Adam Paszke, and Jeremy Howard, 2020 (O'Reilly Media) - This book provides a comprehensive introduction to the principles and practices of MLOps, covering the entire machine learning lifecycle from development to deployment and monitoring.
- Monitoring machine learning models in production: A survey, Saqib Ejaz, Muhammad Atif, Nima Dini, and Daniel Rodriguez, 2023 Information and Software Technology, Vol. 154 (Elsevier) DOI: 10.1016/j.infsof.2022.107089 - A recent survey examining the challenges and techniques for effectively monitoring machine learning models once they are deployed, including detection of data and concept drift.