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45.1 MB
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3C813C2B6FAE0B6ECB9779907BDB97813C3891DB
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March 3, 2026, 12:52 a.m.
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(Last updated: May 29, 2026, 10:45 p.m.)
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| ['Hao B. Build a Machine Learning Platform (From Scratch) (MEAP v6) 2025.pdf'] | 0 bytes |
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116.6 MB
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| Uploaded by andryold1 | Size 116.6 MB | Health [ 37 /3 ] | Added 2026-03-01 |
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| Uploaded by indexFroggy | Size 572.9 MB | Health [ 25 /3 ] | Added 2023-10-15 |
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SOURCE: Hao B. Build a Machine Learning Platform (From Scratch) (MEAP v6) 2025
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COVER

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MEDIAINFO
Textbook in PDF format
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.
Preface
Getting started with MLOps and ML engineering
What is MLOps?
Building applications on Kubernetes
Designing reliable ML systems
Orchestrating ML pipelines
Productionizing ML models
Data analysis and preparation
Model training and validation: Part 1
Model training and validation: Part 2
Model inference & serving
Monitoring and explainability
Designing LLM-powered systems
Production LLM system design
Appendix A. Installation and setup
Appendix B. Basics of YAML
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