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
Creating a Model API with Flask
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- Flask Documentation, Pallets, 2024 - The official and comprehensive guide to the Flask micro web framework, essential for understanding its core concepts and how to build web applications and APIs.
- Flask Web Development: Developing Applications with Python, Miguel Grinberg, 2018 (O'Reilly Media) - A practical book guiding readers through building Flask applications, covering API development, handling requests, and deployment considerations, suitable for both beginners and experienced developers.
- Introducing MLOps: How to Go from Model to Production, Mark Treveil and Dataiku, 2020 (O'Reilly Media) - This book provides an overview of MLOps principles and practices, contextualizing model deployment via APIs within the broader machine learning lifecycle.
- Building Machine Learning Powered Applications: Going from Idea to Product, Emmanuel Ameisen, 2020 (O'Reilly Media) - Offers practical strategies for transforming machine learning models into production-ready applications, including detailed discussions on API design and serving models.