Machine Learning Production Systems: Engineering Machine Learning Models and Pip

Description: Machine Learning Production Systems by Robert Crowe, Hannes Hapke, Emily Caveness, Di Zhu Estimated delivery 4-14 business days Format Paperback Condition Brand New Description Whether you currently work to create products and services that use machine learning, or would like to in the future, this practical book teaches you the basics and advanced aspects of the production ML lifecycle. Publisher Description Using machine learning for products, services, and critical business processes is quite different from using ML in an academic or research setting-especially for recent ML graduates and those moving from research to a commercial environment. Whether you currently work to create products and services that use ML, or would like to in the future, this practical book gives you a broad view of the entire field.Authors Robert Crowe, Hannes Hapke, Emily Caveness, and Di Zhu help you identify topics that you can dive into deeper, along with reference materials and tutorials that teach you the details. Youll learn the state of the art of machine learning engineering, including a wide range of topics such as modeling, deployment, and MLOps. Youll learn the basics and advanced aspects to understand the production ML lifecycle.This book provides four in-depth sections that cover all aspects of machine learning engineering:Data: collecting, labeling, validating, automation, and data preprocessing; data feature engineering and selection; data journey and storageModeling: high performance modeling; model resource management techniques; model analysis and interoperability; neural architecture searchDeployment: model serving patterns and infrastructure for ML models and LLMs; management and delivery; monitoring and loggingProductionalizing: ML pipelines; classifying unstructured texts and images; genAI model pipelines Author Biography Robert Crowe is a data scientist and TensorFlow enthusiast. Robert has a passion for helping developers quickly learn what they need to be productive. Robert is the Senior Product Manager for TensorFlow Open-Source and MLOps at Google and helps ML teams meet the challenges of creating products and services with ML. Previously, Robert led software engineering teams for both large and small companies, always focusing on clean, elegant solutions to well-defined needs. Hannes Hapke is a Senior Machine Learning Engineer at Digits, and has co-authored multiple machine learning publications, including the book "Building Machine Learning Pipelines" by OReilly Media. He has also presented state-of-the-art ML work at conferences like ODSC or OReillys TensorFlow World and is an active contributor to TensorFlows TFX Addons project. Hannes is passionate about machine learning engineering and production machine learning use cases using the latest machine learning developments. Emily Caveness is a software engineer at Google. She currently works on ML data analysis and validation. Di Zhu is an engineer at Google. She has worked on a variety of projects, including MLOps infrastructure, applied machine learning solutions for different verticals including vision, ranking, dynamic pricing, etc. She is passionate about using engineering to solve real-world problems, designing and delivering MLOps solutions for several critical Google products and external partners. In addition to professional pursuits, Di is also a tennis player, Latin dancing competitor, and piano player. Details ISBN 1098156013 ISBN-13 9781098156015 Title Machine Learning Production Systems Author Robert Crowe, Hannes Hapke, Emily Caveness, Di Zhu Format Paperback Year 2024 Pages 260 Edition 2nd Publisher OReilly Media GE_Item_ID:168276407; About Us Grand Eagle Retail is the ideal place for all your shopping needs! With fast shipping, low prices, friendly service and over 1,000,000 in stock items - you're bound to find what you want, at a price you'll love! Shipping & Delivery Times Shipping is FREE to any address in USA. Please view eBay estimated delivery times at the top of the listing. Deliveries are made by either USPS or Courier. We are unable to deliver faster than stated. International deliveries will take 1-6 weeks. NOTE: We are unable to offer combined shipping for multiple items purchased. This is because our items are shipped from different locations. Returns If you wish to return an item, please consult our Returns Policy as below: Please contact Customer Services and request "Return Authorisation" before you send your item back to us. Unauthorised returns will not be accepted. Returns must be postmarked within 4 business days of authorisation and must be in resellable condition. Returns are shipped at the customer's risk. We cannot take responsibility for items which are lost or damaged in transit. For purchases where a shipping charge was paid, there will be no refund of the original shipping charge. Additional Questions If you have any questions please feel free to Contact Us. 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End Time: 2024-11-16T23:31:43.000Z

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