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
Hands-on Practical: Versioning a Simple ML Project
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- Pro Git, Scott Chacon and Ben Straub, 2014 (Apress) - A comprehensive guide to Git, covering fundamental and advanced concepts for versioning code effectively.
- DVC Documentation, Iterative.ai, 2024 - The official and complete guide for Data Version Control (DVC), which is for tracking data and models in ML projects.
- Practical MLOps: Operationalizing Machine Learning Models for Production, Noah Gift and Alfredo Deza, 2021 (O'Reilly Media) - Offers practical guidance on MLOps principles, including strategies for versioning, reproducibility, and managing machine learning workflows.
- Building Machine Learning Powered Applications: Going from Idea to Product, Emmanuel Ameisen, 2020 (O'Reilly Media) - Discusses best practices for developing and deploying ML applications, with information on ensuring reproducibility through proper versioning of code, data, and models.