Build a Machine Learning Platform (From Scratch)
Benjamin Hao
Sold by AHA-BUCH GmbH, Einbeck, Germany
AbeBooks Seller since 14 August 2006
New - Soft cover
Condition: New
Ships from Germany to U.S.A.
Quantity: 1 available
Add to basketSold by AHA-BUCH GmbH, Einbeck, Germany
AbeBooks Seller since 14 August 2006
Condition: New
Quantity: 1 available
Add to basketNeuware - Get a free Elektronisches Buch (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 platform Deploy machine learning models to production Build end-to-end data pipelines Effective monitoring and explainability 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 author 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. Table of Contents Part 1 1 Getting started with MLOps and ML engineering 2 What is MLOps 3 Building applications on Kubernetes Part 2 4 Designing reliable ML systems 5 Orchestrating ML pipelines 6 Productionizing ML models Part 3 7 Data analysis and preparation 8 Model training and validation: Part 1 9 Model training and validation: Part 2 10 Model inference and serving 11 Monitoring and explainability Part 4 12 Designing LLM-powered systems 13 Production LLM system design A Installation and setup B Basics of YAML.
Seller Inventory # 9781633437333
Machine learning models look great in notebooks, then collapse in production. Ready to build an ML platform that actually delivers? Here’s a step-by-step, project-driven guide to building an MLOps-ready platform from scratch.
Inside you’ll find:
Build a Machine Learning Platform (From Scratch) by Benjamin Tan Wei Hao, Shanoop Padmanabhan, and Varun Mallya delivers a practical field guide in print and eBook formats. Three veteran engineers lead you through every layer of modern MLOps.
The chapters construct two reference systems, an image classifier and a recommendation engine, while teaching orchestration, training, serving, and monitoring techniques. The actionable items for each concept include sample code, architecture diagrams, and checklists.
By the end of this book, you will end up with a reusable blueprint that slashes deployment time, reduces firefighting, and thrives with team growth. You will start shipping platforms that thrive.
Ideal for Python-savvy data scientists and software engineers eager to master production-quality machine learning.
Benjamin Tan Wei Hao is a product manager and principal engineer known for turning data into reliable ML delivery machines. With years leading platform builds, Benjamin distills deep MLOps experience into step-by-step guidance that helps readers ship scalable, maintainable models.
Shanoop Padmanabhan is a software engineering manager recognized for advancing autonomous-vehicle perception through robust ML platforms. He translates complex deployment challenges into replicable patterns.
Varun Mallya is a machine-learning engineer responsible for bank-wide ML platform stability and growth. With experience in scaling mission-critical models, Varun offers grounded insight on reliability and monitoring.
"About this title" may belong to another edition of this title.
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