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: Package and Deploy a Model as a Docker Container
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- Dockerfile reference, Docker, 2023 - Official documentation detailing Dockerfile instructions and best practices for building Docker images, crucial for containerizing applications.
- Flask Documentation, Pallets, 2023 - The official guide to the Flask web framework, essential for creating Python-based web services and REST APIs to serve machine learning models.
- Introducing MLOps: How to go from model to production, Mark Treveil, Nicolas Omont, Aurélien Simon, Andreas Schuderer, Hannes Hapke, Noah Gift, and David Noël, 2020 (O'Reilly Media) - A comprehensive book covering the principles and practices of MLOps, including strategies for deploying machine learning models using containerization.
- Model persistence, scikit-learn developers, 2023 - Official documentation explaining how to save and load scikit-learn models using joblib or pickle, which is fundamental for model deployment.