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
Containerization with Docker
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- Get started with Docker, Docker Documentation, 2024 - Provides a comprehensive introduction to Docker's core concepts, architecture, and workflow, which aligns directly with the section's content.
- Docker: Up & Running, Sean P. Kane, Karl Matthias, 2018 (O'Reilly Media, Inc.) - Offers a practical guide to Docker, covering its architecture, command-line interface, and how to build, run, and manage containers effectively.
- Practical MLOps: How to Get Models into Production, Noah Gift, Alfredo Deza, 2021 (O'Reilly Media) - Discusses the role of containerization with Docker as a foundational practice for MLOps, focusing on model deployment, reproducibility, and environment consistency in production.
- Custom containers for predictions, Google Cloud Documentation, 2024 (Google Cloud) - Illustrates how major cloud platforms like Google Cloud use Docker containers to deploy machine learning models for serving predictions, reinforcing the practical application of containerization in MLOps.