Machine Learning Platform Engineering

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Build an internal developer platform for ML and AI systems
  • February 2026
  • ISBN 9781633437333
  • 504 pages
  • printed in black & white
print book available Feb 23, 2026
ePub + liveBook available Feb 16, 2026

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Look inside
Get your machine learning models out of the lab and into production!

Delivering a successful machine learning project is hard. Machine Learning Platform Engineering 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 Machine Learning Platform Engineering 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 Machine Learning Platform Engineering 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 technology

AI and ML systems have a lot of moving parts, from language libraries and application frameworks, to workflow and deployment infrastructure, to LLMs and other advanced models. A well-designed internal development platform (IDP) gives developers a defined set of tools and guidelines that accelerate the dev process, improving consistency, security, and developer experience.

about the book

Machine Learning Platform Engineering shows you how to build an effective IDP for ML and AI applications. Each chapter illuminates a vital part of the ML workflow, including setting up orchestration pipelines, selecting models, allocating resources for training, inference, and serving, and more. As you go, you’ll create a versatile modern platform using open source tools like Kubeflow, MLFlow, BentoML, Evidently, Feast, and LangChain.

what's inside

  • Set up an end-to-end MLOps/LLMOps platform
  • Deploy ML and AI models to production
  • Effective monitoring, evaluation, and explainability

about the reader

For data scientists or software engineers. Examples in Python.

about the authors

Benjamin Tan Wei Hao leads a team of ML engineers and data scientists at DKatalis. Shanoop Padmanabhan is a software engineering manager at Continental Automotive. Varun Mallya is a senior ML engineer at DKatalis.

A great resource, especially for those looking for a hands-on approach.

Noah Flynn, Amazon

Covers all the patterns you should follow.

Andrew R. Freed, IBM

Rich and well structured.

Vinicios Wentz, Nubank

Packed with code examples and capstone projects.

Nupur Baghel, Google

A must-have if you want to learn how to build and deploy ML models from scratch.

Ravikumar Sanapala, Meta
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choose your plan

team

monthly
annual
$49.99
$499.99
only $41.67 per month
  • five seats for your team
  • access to all Manning books, MEAPs, liveVideos, liveProjects, and audiobooks!
  • choose another free product every time you renew
  • choose twelve free products per year
  • exclusive 50% discount on all purchases
  • renews monthly, pause or cancel renewal anytime
  • renews annually, pause or cancel renewal anytime
  • Machine Learning Platform Engineering ebook for free