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
Fundamentals of Model Monitoring
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- Introducing MLOps: How to go from Model to Money, Mark Treveil, Nicolas Omont, Aurélien Géron, Hannes Hapke, Denis Rothman, Stephen Mellor, and Noah Gift, 2022 (O'Reilly Media) - A practical guide covering the full MLOps lifecycle, including detailed sections on model deployment, monitoring, and continuous integration/delivery for machine learning.
- A Survey on Concept Drift Adaptation in Machine Learning, J. Lu, A. Liu, F. Chen, P. Wang, and J. Ma, 2019 ACM Computing Surveys (CSUR), Vol. 52 (Association for Computing Machinery) DOI: 10.1145/3343160 - This survey provides an overview of various methods for detecting and adapting to concept drift, a problem directly relevant to maintaining model performance in changing environments.
- Machine Learning Engineering, Andriy Burkov, 2020 (True Positive Inc.) - A practical and comprehensive guide to putting machine learning models into production, covering topics such as deployment, monitoring, and maintenance.