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
Monitoring Models in Production
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- Machine Learning Engineering, Andriy Burkov, Stefan Loy, Nicolas Linder, 2020 (O'Reilly Media) - Covers the complete machine learning lifecycle, with dedicated sections for model deployment, monitoring, and maintenance in production.
- Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications, Chip Huyen, 2022 (O'Reilly Media) - Discusses the design and implementation of production machine learning systems, including essential aspects of monitoring model performance and operational health.
- A Survey on Concept Drift Adaption, João Gama, Indrė Žliobaitė, Albert Bifet, Myra Spiliopoulou, Paul Vanhoof, 2014 ACM Computing Surveys (CSUR), Vol. 46 (Association for Computing Machinery) DOI: 10.1145/2523813 - Provides a comprehensive survey of techniques and methods for adapting to concept drift in data streams, a core problem in model staleness.
- MLOps: Continuous delivery and automation pipelines in machine learning, Dale Markowitz, Boris Tetiyevsky, Michael N. Wudka, 2023 (Google Cloud) - An official guide that describes the principles of MLOps, including continuous monitoring and its importance in production machine learning systems.