Machine Learning at Scale with H2O. A practical guide to building and deploying machine learning models on enterprise systems

Autorzy:: Gregory Keys, David Whiting

Machine Learning at Scale with H2O. A practical guide to building and deploying machine learning models on enterprise systems Gregory Keys, David Whiting - okladka książki

Wydawnictwo:: Packt Publishing (Z chęcią przeczytam książkę w języku polskim)
Ocena:: Bądź pierwszym, który oceni tę książkę
Stron:: 396
Dostępne formaty:: PDF

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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.

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O autorach książki

Gregory Keys is a Senior Solution Architect at H2O and has over 20 years of experience designing and implementing software and data systems. He innovated a model deployment and governance framework that was incorporated into Cloudera machine learning product line.

David Whiting is a Data Science Director and Head of Training at H2O.ai. He has over 18 years of experience in business, consulting, and academia. He is adept at developing and maintaining long-term collaborations with experts in multiple fields. He has both led and participated in multi-disciplinary teams and he enjoys mentoring developing analysts and has a substantial experience in doing so.

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Szczegóły książki

Tytuł oryginału:: Machine Learning at Scale with H2O. A practical guide to building and deploying machine learning models on enterprise systems
ISBN Ebooka:: 978-18-005-6929-4, 9781800569294
Data wydania ebooka :: 2022-07-29 Data wydania ebooka często jest dniem wprowadzenia tytułu do sprzedaży i może nie być równoznaczna z datą wydania książki papierowej. Dodatkowe informacje możesz znaleźć w darmowym fragmencie. Jeśli masz wątpliwości skontaktuj się z nami sklep@ebookpoint.pl.
Język publikacji:: angielski
Rozmiar pliku Pdf:: 13.9MB
Rozmiar pliku ePub:: 2.2MB

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Informatyka » Biznes IT » Big data » Uczenie maszynowe

Spis treści książki

1. Opportunities and Challenges
2. Platform Components and Key Concepts
3. Fundamental Workflow - Data to Deployable Model
4. H2O Model Building at Scale – Capability Articulation
5. Advanced Model Building – Part I
6. Advanced Model Building – Part II
7. Understanding ML Models
8. Putting It All Together
9. Production Scoring and the H2O MOJO
10. H2O Model Deployment Patterns
11. The Administrator and Operations Views
12. The Enterprise Architect and Security Views
13. Introducing the H2O AI Cloud
14. H2O at Scale in a Larger Platform Context
15. Appendix – Alternative Methods to Launch H2O Clusters

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Machine Learning at Scale with H2O. A practical guide to building and deploying machine learning models on enterprise systems

Ebook 29,90 zł najniższa cena z 30 dni

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Opis książki : Machine Learning at Scale with H2O. A practical guide to building and deploying machine learning models on enterprise systems

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