{"product_id":"machine-learning-platform-engineering-build-an-internal-developer-platform-for-ml-and-ai-systems-9781633437333","title":"Machine Learning Platform Engineering: Build an Internal Developer Platform for ML and AI Systems","description":"\u003cp\u003e • Author(s): Benjamin Tan Wei Hao | Shanoop Padmanabhan | Varun Mallya\u003cbr\u003e • Publisher: Manning Publications\u003cbr\u003e • Publisher Imprint: Manning Publications\u003cbr\u003e • BISAC: Data Science - Machine Learning\u003c\/p\u003e\u003cp\u003e\u003cb\u003eGet 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.\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003e\u003c\/p\u003eDelivering 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. \u003cp\u003e\u003c\/p\u003eA 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. \u003cp\u003e\u003c\/p\u003eIn \u003ci\u003eMachine Learning Platform Engineering\u003c\/i\u003e you'll learn how to: \u003cp\u003e\u003c\/p\u003e - Set up an MLOps platform\u003cbr\u003e - Deploy machine learning models to production\u003cbr\u003e - Build end-to-end data pipelines\u003cbr\u003e - Effective monitoring and explainability \u003cp\u003e\u003c\/p\u003e \u003cb\u003eAbout the technology\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003e 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. \u003cp\u003e\u003c\/p\u003e \u003cb\u003eAbout the book\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003e \u003ci\u003eMachine Learning Platform Engineering\u003c\/i\u003e 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. \u003cp\u003e\u003c\/p\u003e \u003cb\u003eWhat's inside\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003e - Set up an end-to-end MLOps\/LLMOps platform\u003cbr\u003e - Deploy ML and AI models to production\u003cbr\u003e - Effective monitoring, evaluation, and explainability \u003cp\u003e\u003c\/p\u003e\u003cb\u003eAbout the reader\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003e For data scientists or software engineers. Examples in Python. \u003cp\u003e\u003c\/p\u003e \u003cb\u003eAbout the author\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003e \u003cb\u003eBenjamin Tan Wei Hao\u003c\/b\u003e leads a team of ML engineers and data scientists at DKatalis. \u003cb\u003eShanoop Padmanabhan\u003c\/b\u003e is a software engineering manager at Continental Automotive. \u003cb\u003eVarun Mallya\u003c\/b\u003e is a senior ML engineer at DKatalis. \u003cp\u003e\u003c\/p\u003e \u003cb\u003eTable of Contents\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003e Part 1\u003cbr\u003e 1 Getting started with MLOps and ML engineering\u003cbr\u003e 2 What is MLOps?\u003cbr\u003e 3 Building applications on Kubernetes\u003cbr\u003e Part 2\u003cbr\u003e 4 Designing reliable ML systems\u003cbr\u003e 5 Orchestrating ML pipelines\u003cbr\u003e 6 Productionizing ML models\u003cbr\u003e Part 3\u003cbr\u003e 7 Data analysis and preparation\u003cbr\u003e 8 Model training and validation: Part 1\u003cbr\u003e 9 Model training and validation: Part 2\u003cbr\u003e 10 Model inference and serving\u003cbr\u003e 11 Monitoring and explainability\u003cbr\u003e Part 4\u003cbr\u003e 12 Designing LLM-powered systems\u003cbr\u003e 13 Production LLM system design\u003cbr\u003e A Installation and setup\u003cbr\u003e B Basics of YAML","brand":"Manning Publications","offers":[{"title":"Paperback","offer_id":47568203284631,"sku":"9781633437333","price":5221.0,"currency_code":"INR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0666\/3471\/1191\/files\/9781633437333.webp?v=1774866655","url":"https:\/\/atlanticbooks.com\/products\/machine-learning-platform-engineering-build-an-internal-developer-platform-for-ml-and-ai-systems-9781633437333","provider":"Atlantic Books","version":"1.0","type":"link"}