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
What is MLOps?
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- MLOps: Continuous delivery and automation pipelines in machine learning, Valeriy Katkalo, Andrew Ferlitsch, Brian Suk, Karl Weinwurm, Alex Shterman, Alexey S. Goldin, Dmitry Ryabtsev, Evgeny Ignatenko, 2023 Google Cloud (Google Cloud) - Outlines the core concepts of MLOps, demonstrating how to implement continuous integration, delivery, and training for machine learning systems using a practical framework.
- MLOps: A Survey, Taxonomy, and the Future Direction, J. D. K. S. Ranathunga, Z. H. F. N. S. Ranasinghe, T. L. T. Mahesan, U. V. W. L. K. De Silva, T. G. I. N. Thilakarathne, R. H. R. C. R. Silva, 2022 ACM Computing Surveys, Vol. 54 (Association for Computing Machinery) DOI: 10.1145/3547180 - Offers a systematic survey of MLOps, providing a taxonomy of its components, challenges, and future research directions, suitable for understanding the academic framing.
- Practical MLOps: How to take a model from experimentation to production, Noah Gift, Alfredo Deza, 2021 (O'Reilly Media) - Guides readers through the practical implementation of MLOps, focusing on tools and techniques for building and managing production-grade machine learning systems.