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020    1800569297 
020    9781800569294|q(electronic bk.) 
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035    (OCoLC)1334891150 
037    9781800566019|bO'Reilly Media 
037    10162916|bIEEE 
040    EBLCP|beng|epn|cEBLCP|dORMDA|dOCLCQ|dOCLCF|dN$T|dOCLCQ
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049    INap 
082 04 006.3/1 
082 04 006.3/1|223/eng/20220802 
099    eBook O'Reilly for Public Libraries 
100 1  Keys, Gregory. 
245 10 Machine Learning at Scale with H2O :|ba Practical Guide to
       Building and Deploying Machine Learning Models on 
       Enterprise Systems /|cGregory Keys, David Whiting.
       |h[O'Reilly electronic resource] 
260    Birmingham :|bPackt Publishing, Limited,|c2022. 
300    1 online resource (396 pages) 
336    text|btxt|2rdacontent 
337    computer|bc|2rdamedia 
338    online resource|bcr|2rdacarrier 
505 0  Table of Contents Opportunities and Challenges Platform 
       Components and Key Concepts Fundamental Workflow - Data to
       Deployable Model H2O Model Building at Scale - Capability 
       Articulation Advanced Model Building - Part I Advanced 
       Model Building - Part II Understanding ML Models Putting 
       It All Together Production Scoring and the H2O MOJO H2O 
       Model Deployment Patterns The Administrator and Operations
       Views The Enterprise Architect and Security Views 
       Introducing the H2O AI Cloud H2O at Scale in a Larger 
       Platform Context Appendix - Alternative Methods to Launch 
       H2O Clusters. 
520    Build predictive models using large data volumes and 
       deploy them to production using cutting-edge techniques 
       Key Features Build highly accurate state-of-the-art 
       machine learning models against large-scale data Deploy 
       models for batch, real-time, and streaming data in a wide 
       variety of target production systems Explore all the new 
       features of the H2O AI Cloud end-to-end machine learning 
       platform Book Description H2O is an open source, fast, and
       scalable machine learning framework that allows you to 
       build models using big data and then easily 
       productionalize them in diverse enterprise environments. 
       Machine Learning at Scale with H2O begins with an overview
       of the challenges faced in building machine learning 
       models on large enterprise systems, and then addresses how
       H2O helps you to overcome them. You'll start by exploring 
       H2O's in-memory distributed architecture and find out how 
       it enables you to build highly accurate and explainable 
       models on massive datasets using your favorite ML 
       algorithms, language, and IDE. You'll also get to grips 
       with the seamless integration of H2O model building and 
       deployment with Spark using H2O Sparkling Water. You'll 
       then learn how to easily deploy models with H2O MOJO. Next,
       the book shows you how H2O Enterprise Steam handles admin 
       configurations and user management, and then helps you to 
       identify different stakeholder perspectives that a data 
       scientist must understand in order to succeed in an 
       enterprise setting. Finally, you'll be introduced to the 
       H2O AI Cloud platform and explore the entire machine 
       learning life cycle using multiple advanced AI 
       capabilities. By the end of this book, you'll be able to 
       build and deploy advanced, state-of-the-art machine 
       learning models for your business needs. What you will 
       learn Build and deploy machine learning models using H2O 
       Explore advanced model-building techniques Integrate Spark
       and H2O code using H2O Sparkling Water Launch self-service
       model building environments Deploy H2O models in a variety
       of target systems and scoring contexts Expand your machine
       learning capabilities on the H2O AI Cloud Who this book is
       for This book is for data scientists and machine learning 
       engineers who want to gain hands-on machine learning 
       experience by building and deploying state-of-the-art 
       models with advanced techniques using H2O technology. An 
       understanding of the data science process and experience 
       in Python programming is recommended. This book will also 
       benefit students by helping them understand how machine 
       learning works in real-world enterprise scenarios. 
588 0  Print version record. 
590    O'Reilly|bO'Reilly Online Learning: Academic/Public 
       Library Edition 
650  0 Machine learning. 
650  0 Predictive analytics. 
650  6 Apprentissage automatique. 
650  7 Machine learning|2fast 
650  7 Predictive analytics|2fast 
700 1  Whiting, David. 
776 08 |iPrint version:|aKeys, Gregory.|tMachine Learning at 
       Scale with H2O.|dBirmingham : Packt Publishing, Limited, 
       ©2022 
856 40 |uhttps://ezproxy.naperville-lib.org/login?url=https://
       learning.oreilly.com/library/view/~/9781800566019/?ar
       |zAvailable on O'Reilly for Public Libraries 
938    ProQuest Ebook Central|bEBLB|nEBL7029037 
938    EBSCOhost|bEBSC|n3324228 
994    92|bJFN