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
MLOps vs. DevOps: Similarities and Differences
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- MLOps: Continuous delivery and automation pipelines in machine learning, Google Cloud, 2024 (Google Cloud) - This comprehensive guide explains MLOps principles, architectural patterns, and practical considerations for building automated machine learning pipelines, showing its extension from DevOps.
- Introducing MLOps: How to go from Model Centric to Data Centric AI, Mark Treveil, Nicolas Omont, Aurélien Géron, Michel Blancard, Côme de Treglode, Gregory P. Tenten, Kevin Stumpf, Houssam A. Bakkali, and Adrien Lavoillotte, 2020 (O'Reilly Media) - A book providing a thorough introduction to MLOps, covering its lifecycle, tools, and the distinctions from traditional software development practices like DevOps.
- Hidden Technical Debt in Machine Learning Systems, D. Sculley, Gary Holt, Daniel Golovin, Eugene Davydov, Todd Phillips, Dietmar Ebner, Vinay Chaudhary, Michael Young, Jean-François Crespo, Dan Dennison, 2015 Advances in Neural Information Processing Systems, Vol. 28 (Curran Associates, Inc.) - This influential paper identifies unique challenges in deploying and maintaining machine learning systems that require specialized practices like MLOps, contrasting them with traditional software.
- Towards MLOps: An Overview of the Current Tools and Future Challenges, Wael H. Gomaa and Ahmed Hamdy, 2022 Journal of Cloud Computing, Vol. 11 (SpringerOpen) DOI: 10.1186/s13677-022-00329-8 - This paper surveys the MLOps field, discussing its principles, tools, and challenges, providing context for the expanded scope compared to DevOps in managing ML systems.