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
Deployment Patterns: Online vs. Batch Prediction
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- Designing Machine Learning Systems: An Iterative Process for Production-Ready AI, Chip Huyen, 2022 (O'Reilly Media) - This book details the end-to-end design of ML systems, with a dedicated section on deployment strategies for online and batch inference.
- Machine Learning Engineering, Andriy Burkov, 2020 (True Positive Inc.) - Offers practical guidance on building, deploying, and maintaining ML systems, including architectural considerations for serving models in different operational contexts.
- MLOps: A guide to operations for machine learning, Google Cloud, 2023 (Google Cloud) - Provides an industry perspective on MLOps best practices, covering model deployment patterns like online and batch prediction within a robust ML production system.
- CS 329S: Machine Learning Systems Design - Lecture on Model Deployment, Chip Huyen, 2022 (Stanford University) - Course materials from Stanford University that discuss various model serving strategies, including the differences and trade-offs between online and batch prediction.