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

Description

Build predictive models using large data volumes and deploy them to production using cutting-edge techniques Key FeaturesBuild highly accurate state-of-the-art machine learning models against large-scale dataDeploy models for batch, real-time, and streaming data in a wide variety of target production systemsExplore all the new features of the H2O AI Cloud end-to-end machine learning platformBook 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 learnBuild and deploy machine learning models using H2OExplore advanced model-building techniquesIntegrate Spark and H2O code using H2O Sparkling WaterLaunch self-service model building environmentsDeploy H2O models in a variety of target systems and scoring contextsExpand your machine learning capabilities on the H2O AI CloudWho 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. Table of ContentsOpportunities and ChallengesPlatform Components and Key ConceptsFundamental Workflow - Data to Deployable ModelH2O Model Building at Scale – Capability ArticulationAdvanced Model Building – Part IAdvanced Model Building – Part IIUnderstanding ML ModelsPutting It All TogetherProduction Scoring and the H2O MOJOH2O Model Deployment PatternsThe Administrator and Operations ViewsThe Enterprise Architect and Security ViewsIntroducing the H2O AI CloudH2O at Scale in a Larger Platform ContextAppendix – Alternative Methods to Launch H2O Clusters