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 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.

使用大量數據建立預測模型，並使用尖端技術將其部署到生產環境中。

主要特點：
- 使用大規模數據構建高度準確的最新機器學習模型
- 在各種目標生產系統中為批處理、實時處理和流式數據部署模型
- 探索 H2O AI Cloud 端到端機器學習平台的所有新功能

書籍描述：
H2O 是一個開源、快速且可擴展的機器學習框架，允許您使用大數據構建模型，然後輕鬆將其部署到各種企業環境中。

《機器學習在 H2O 上的規模化》首先概述了在大型企業系統上構建機器學習模型所面臨的挑戰，然後介紹了 H2O 如何幫助您克服這些挑戰。您將首先探索 H2O 的內存分佈式架構，了解它如何使您能夠使用您喜歡的機器學習算法、語言和 IDE 在大型數據集上構建高度準確且可解釋的模型。您還將瞭解到如何使用 H2O Sparkling Water 將 H2O 模型構建和部署與 Spark 無縫集成。然後，本書將向您展示如何使用 H2O MOJO 輕鬆部署模型。接下來，本書將向您展示 H2O Enterprise Steam 如何處理管理配置和用戶管理，並幫助您識別數據科學家在企業環境中必須理解的不同利益相關者觀點，以便取得成功。最後，您將介紹 H2O AI Cloud 平台，並使用多種高級 AI 功能探索整個機器學習生命周期。

通過閱讀本書，您將能夠為您的業務需求構建和部署先進的、最新的機器學習模型。

學到的內容：
- 使用 H2O 構建和部署機器學習模型
- 探索高級模型構建技術
- 使用 H2O Sparkling Water 將 Spark 和 H2O 代碼集成
- 啟動自助服務模型構建環境
- 在各種目標系統和評分上下文中部署 H2O 模型
- 在 H2O AI Cloud 上擴展您的機器學習能力

適合閱讀對象：
本書適合數據科學家和機器學習工程師，他們希望通過使用 H2O 技術構建和部署最先進的模型以及使用高級技術獲得實踐機器學習經驗。建議具備對數據科學流程的理解和 Python 編程經驗。本書還將幫助學生們理解機器學習在真實企業場景中的應用。