Machine Learning Platform Engineering (1 ed)

Machine Learning Platform Engineering: Build an internal developer platform for ML and AI systems (From Scratch)

Delivering a successful machine learning project is challenging, but this book makes it straightforward. You’ll learn how to design a reliable ML system from the ground up, integrating MLOps and DevOps, and leveraging a proven infrastructure stack including Kubeflow, MLFlow, BentoML, Evidently, and Feast.

A well-designed machine learning system streamlines data workflows, improves collaboration between data and operations teams, and provides a solid framework for both training and deployment. This book offers a practical, hands-on guide to building and implementing a complete ML system, helping you unlock the full benefits of automated ML infrastructure.

In Machine Learning Platform Engineering, you’ll learn how to:

• Set up a fully functional MLOps platform
• Deploy machine learning models to production
• Build end-to-end data pipelines
• Implement effective monitoring and model explainability