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176676434
Machine Learning Platform Engineering: Build an internal developer platform for ML and AI systems (From Scratch)
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Stavka #:
176676434
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Detalji o proizvodu
- Get a free eBook (PDF or ePub) from Manning as well as access to the online liveBook format (and its AI assistant that will answer your questions in any language) when you purchase the print book.Delivering a successful machine learning project is hard. This book 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.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 this book 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.In Machine Learning Platform Engineering you’ll learn how to:Set up an MLOps platformDeploy machine learning models to productionBuild end-to-end data pipelinesEffective monitoring and explainabilityAbout the technologyAI 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 bookMachine 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 insideSet up an end-to-end MLOps/LLMOps platformDeploy ML and AI models to productionEffective monitoring, evaluation, and explainabilityAbout the readerFor data scientists or software engineers. Examples in Python.About the authorBenjamin 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.Table of ContentsPart 11 Getting started with MLOps and ML engineering2 What is MLOps?3 Building applications on KubernetesPart 24 Designing reliable ML systems5 Orchestrating ML pipelines6 Productionizing ML modelsPart 37 Data analysis and preparation8 Model training and validation: Part 19 Model training and validation: Part 210 Model inference and serving11 Monitoring and explainabilityPart 412 Designing LLM-powered systems13 Production LLM system designA Installation and setupB Basics of YAML
| Publisher | Manning Publications |
| Publication date | March 10, 2026 |
| Language | English |
| Print length | 504 pages |
| ISBN-10 | 1633437337 |
| ISBN-13 | 978-1633437333 |
| Item Weight | 13.7 ounces (388.4 grams) |
| Dimensions | 7.38 x 1.14 x 9.25 inches (18.7 x 2.9 x 23.5 cm) |
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