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
Data Ingestion and Preparation
Was this section helpful?
- Introducing MLOps: How to go from Model to Production, Hien Luu, Stephen W. Kalinin, and Boris Lublinsky, 2022 (O'Reilly Media) - Explains the MLOps lifecycle and the role of data ingestion and preparation in production machine learning systems.
- Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems, Martin Kleppmann, 2017 (O'Reilly Media) - Discusses principles for building data systems, which are fundamental for reliable and scalable data ingestion in MLOps.
- Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, Aurélien Géron, 2022 (O'Reilly Media) - Offers practical guidance on data cleaning, feature engineering, and data transformation techniques for machine learning models.
- Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists, Alice Zheng and Amanda Casari, 2018 (O'Reilly Media) - Provides various techniques and strategies for creating features from raw data to improve model performance.