English | 2025 | ISBN: 9781633437333 | 548 pages | True PDF,EPUB | 84.05 MB
Get your machine learning models out of the lab and into production!
Delivering a successful machine learning project is hard. Build a Machine Learning Platform (From Scratch) makes it easier. In it, you'll design a reliable ML system from the ground up, incorporating MLOps and DevOps along with a stack of proven infrastructure tools including Kubeflow, MLFlow, BentoML, Evidently, and Feast.
In Build a Machine Learning Platform (From Scratch) you'll learn how to
Set up an MLOps platform
Deploy machine learning models to production
Build end-to-end data pipelines
Effective monitoring and explainability
A properly designed machine learning system streamlines data workflows, improves collaboration between data and operations teams, and provides much-needed structure for both training and deployment. In Build a Machine Learning Platform (From Scratch) you'll learn how to design and implement a machine learning system from the ground up. You'll appreciate this instantly-useful introduction to achieving the full benefits of automated ML infrastructure.
about the book
Build a Machine Learning Platform (From Scratch) teaches you to set up and run a production-quality machine learning system using open source tools. Chapter-by-chapter, you'll assemble a delivery pipeline for an image classifier and a recommendation system, learning best practices as you go. You'll get hands-on experience with the most important parts of the machine learning workflow, including orchestrating pipelines; model training, inference, and serving; and monitoring and explainability. Soon, you'll be deploying models that are fast to production and easy to maintain and scale.
Build A Machine Learning Platform (from Scratch) (meap V06)
