Machine Learning at Scale with H2O : a Practical Guide to Building and Deploying Machine Learning Models on Enterprise Systems /

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 product...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Keys, Gregory
Otros Autores: Whiting, David
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Birmingham : Packt Publishing, Limited, 2022.
Temas:
Machine learning.
Predictive analytics.
Apprentissage automatique.
Machine learning
Predictive analytics
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)

MARC

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245 1 0 |a Machine Learning at Scale with H2O :  |b a Practical Guide to Building and Deploying Machine Learning Models on Enterprise Systems /  |c Gregory Keys, David Whiting. 
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520 |a 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. 
505 0 |a 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. 
590 |a O'Reilly  |b O'Reilly Online Learning: Academic/Public Library Edition 
650 0 |a Machine learning. 
650 0 |a Predictive analytics. 
650 6 |a Apprentissage automatique. 
650 7 |a Machine learning  |2 fast 
650 7 |a Predictive analytics  |2 fast 
700 1 |a Whiting, David. 
776 0 8 |i Print version:  |a Keys, Gregory.  |t Machine Learning at Scale with H2O.  |d Birmingham : Packt Publishing, Limited, ©2022 
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