Practical Automated Machine Learning Using H2O.ai: Discover the power of automated machine learning, from experimentation through to deployment to pro

Accelerate the adoption of machine learning by automating away the complex parts of the ML pipeline using H2O.ai

Key Features

- Learn how to train the best models with a single click using H2O AutoML
- Get a simple explanation of model performance using H2O Explainability
- Easily deploy your trained models to production using H2O MOJO and POJO

Book Description

With the huge amount of data being generated over the internet and the benefits that Machine Learning (ML) predictions bring to businesses, ML implementation has become a low-hanging fruit that everyone is striving for. The complex mathematics behind it, however, can be discouraging for a lot of users. This is where H2O comes in – it automates various repetitive steps, and this encapsulation helps developers focus on results rather than handling complexities.

You'll begin by understanding how H2O's AutoML simplifies the implementation of ML by providing a simple, easy-to-use interface to train and use ML models. Next, you'll see how AutoML automates the entire process of training multiple models, optimizing their hyperparameters, as well as explaining their performance. As you advance, you'll find out how to leverage a Plain Old Java Object (POJO) and Model Object, Optimized (MOJO) to deploy your models to production. Throughout this book, you'll take a hands-on approach to implementation using H2O that'll enable you to set up your ML systems in no time.

By the end of this H2O book, you'll be able to train and use your ML models using H2O AutoML, right from experimentation all the way to production without a single need to understand complex statistics or data science.

What you will learn

- Get to grips with H2O AutoML and learn how to use it
- Explore the H2O Flow Web UI
- Understand how H2O AutoML trains the best models and automates hyperparameter optimization
- Find out how H2O Explainability helps understand model performance
- Explore H2O integration with scikit-learn, the Spring Framework, and Apache Storm
- Discover how to use H2O with Spark using H2O Sparkling Water

Who this book is for

This book is for engineers and data scientists who want to quickly adopt machine learning into their products without worrying about the internal intricacies of training ML models. If you're someone who wants to incorporate machine learning into your software system but don't know where to start or don't have much expertise in the domain of ML, then you'll find this book useful. Basic knowledge of statistics and programming is beneficial. Some understanding of ML and Python will be helpful.

加速機器學習的普及，通過使用H2O.ai自動化ML流程中的複雜部分。

主要特點：

- 使用H2O AutoML一鍵訓練最佳模型
- 使用H2O Explainability獲得模型性能的簡單解釋
- 使用H2O MOJO和POJO輕鬆將訓練好的模型部署到生產環境

書籍描述：

隨著互聯網產生的大量數據以及機器學習（ML）預測為企業帶來的好處，ML實施已成為每個人都在努力追求的目標。然而，其背後的複雜數學可能會使許多用戶望而卻步。這就是H2O的用武之地-它自動化了各種重複的步驟，這種封裝有助於開發人員專注於結果而不是處理複雜性。

您將首先了解H2O的AutoML如何通過提供簡單易用的界面來訓練和使用ML模型，從而簡化ML的實施。接下來，您將看到AutoML如何自動化訓練多個模型的整個過程，優化其超參數以及解釋其性能。隨著進一步的學習，您將了解如何利用Plain Old Java Object（POJO）和Model Object，Optimized（MOJO）將模型部署到生產環境。在整本書中，您將通過使用H2O進行實施的實踐方法，能夠在短時間內建立自己的ML系統。

通過閱讀本書，您將能夠使用H2O AutoML訓練和使用ML模型，從實驗到生產的整個過程中，無需理解複雜的統計學或數據科學。

您將學到什麼：

- 熟悉H2O AutoML並學習如何使用它
- 探索H2O Flow Web UI
- 了解H2O AutoML如何訓練最佳模型並自動優化超參數
- 了解H2O Explainability如何幫助理解模型性能
- 探索H2O與scikit-learn、Spring Framework和Apache Storm的集成
- 發現如何使用H2O Sparkling Water在Spark中使用H2O

本書適合工程師和數據科學家，他們希望快速將機器學習應用於產品中，而不必擔心訓練ML模型的內部細節。如果您想將機器學習融入軟件系統，但不知道從何處開始或在ML領域沒有太多專業知識，那麼本書對您會很有用。具備統計和編程的基礎知識是有益的。對ML和Python的一些了解將有所幫助。

1. Understanding H2O AutoML Basics
2. Working with H2O Flow (H2O's Web UI)
3. Understanding Data Processing
4. Understanding H2O AutoML Training and Architecture
5. Understanding AutoML Algorithms
6. Understanding H2O AutoML Leaderboard and Other Performance Metrics
7. Working with Model Explainability
8. Exploring Optional Parameters for H2O AutoML
9. Exploring Miscellaneous Features in H2O AutoML
10. Working with Plain Old Java Objects (POJOs)
11. Working with Model Object, Optimized (MOJO)
12. Working with H2O AutoML and Apache Spark
13. Using H2O AutoML with Other Technologies

1. 理解 H2O AutoML 基礎知識
2. 使用 H2O Flow（H2O 的 Web UI）
3. 理解資料處理
4. 理解 H2O AutoML 的訓練和架構
5. 理解 AutoML 演算法
6. 理解 H2O AutoML 排行榜和其他效能指標
7. 使用模型可解釋性
8. 探索 H2O AutoML 的可選參數
9. 探索 H2O AutoML 的其他功能
10. 使用 Plain Old Java Objects (POJOs)
11. 使用 Model Object, Optimized (MOJO)
12. 使用 H2O AutoML 和 Apache Spark
13. 將 H2O AutoML 與其他技術結合使用