February 2026
Intermediate
12h 12m
English
video
Machine Learning Platform Engineering, Video Edition
by Benjamin Tan Wei Hao, Varun Mallya, Shanoop Padmanabhan
Closed Captioning available in German, English, Spanish, French, Italian, Japanese
Overview
In Video Editions the narrator reads the book while the content, figures, code listings, diagrams, and text appear on the screen. Like an audiobook that you can also watch as a video.
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.
Quotes
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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