Articles liés à Machine Learning System Design: With End-to-end Examples
Synopsis
Get the big picture and the important details with this end-to-end guide for designing highly effective, reliable machine learning systems.
In Machine Learning System Design: With end-to-end examples you will learn:- The big picture of machine learning system design
- Analyzing a problem space to identify the optimal ML solution
- Ace ML system design interviews
- Selecting appropriate metrics and evaluation criteria
- Prioritizing tasks at different stages of ML system design
- Solving dataset-related problems through data gathering, error analysis, and feature engineering
- Recognizing common pitfalls in ML system development
- Designing ML systems to be lean, maintainable, and extensible over time
Machine Learning System Design: With end-to-end examples is a practical guide for planning and designing successful ML applications. It lays out a clear, repeatable framework for building, maintaining, and improving systems at any scale. Authors Arseny Kravchenko and Valeri Babushkin have filled this unique handbook with campfire stories and personal tips from their own extensive careers. You'll learn directly from their experience as you consider every facet of a machine learning system, from requirements gathering and data sourcing to deployment and management of the finished system.
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
À propos des auteurs
Valerii Babushkin is an accomplished data science leader with extensive experience in the tech industry. He currently serves as the VP of Data Science at Blockchain.com, where he is responsible for leading the company's data-driven initiatives. Prior to joining Blockchain.com, Valerii held key roles at leading tech companies, such as Facebook, Alibaba, and X5 Retail Group.
Arseny Kravchenko is a seasoned ML engineer with a proven track record of building and optimizing reliable ML systems for startups, including real-time video processing, manufacturing optimization, and financial transactions analysis.
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
- ÉditeurManning Publications
- Date d'édition2025
- ISBN 10 1633438759
- ISBN 13 9781633438750
- ReliureBroché
- Langueanglais
- Nombre de pages376
- Coordonnées du fabricantnon disponible
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Hardcover. Etat : new. Hardcover. Get the big picture and the important details with this end-to-end guide for designing highly effective, reliable machine learning systems.In Machine Learning System Design: With end-to-end examples you will learn: The big picture of machine learning system designAnalyzing a problem space to identify the optimal ML solutionAce ML system design interviewsSelecting appropriate metrics and evaluation criteriaPrioritizing tasks at different stages of ML system designSolving dataset-related problems through data gathering, error analysis, and feature engineeringRecognizing common pitfalls in ML system developmentDesigning ML systems to be lean, maintainable, and extensible over time Machine Learning System Design: With end-to-end examples is a practical guide for planning and designing successful ML applications. It lays out a clear, repeatable framework for building, maintaining, and improving systems at any scale. Authors Arseny Kravchenko and Valeri Babushkin have filled this unique handbook with campfire stories and personal tips from their own extensive careers. You'll learn directly from their experience as you consider every facet of a machine learning system, from requirements gathering and data sourcing to deployment and management of the finished system. Get the big picture and the important details with this end-to-end guide for designing highly effective, reliable machine learning systems. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. N° de réf. du vendeur 9781633438750
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