[Farid-Khan]

English | 2026 | ISBN: 1633437337 | 504 pages | True PDF | 30.3 MB

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.

Contents of Download:

? Machine Learning Platform Engineering.pdf (Benjamin Tan Wei Hao, Shanoop Padmanabhan, Varun Mallya) (30.3 MB)

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⭐️ Machine Learning Platform Engineering Build An Internal Developer Platform For ML And AI Systems ✅ (31.31 MB)
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