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

by Salil Ajgaonkar

Length: 396 pages
Edition: 1
Language: English
Publisher: Packt Publishing
Publication Date: 2022-09-26
ISBN-10: 1801074526
ISBN-13: 9781801074520
Sales Rank: #0 (See Top 100 Books)

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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.

Practical Automated Machine Learning Using H2O.ai Copyright © 2022 Packt Publishing
Contributors
About the author
About the reviewer
Preface
    Who this book is for
    What this book covers
    To get the most out of this book
    Download the example code files
    Download the color images
    Conventions used
    Get in touch
    Share Your Thoughts
Part 1 H2O AutoML Basics
Chapter 1: Understanding H2O AutoML Basics
    Technical requirements
    Understanding AutoML and H2O AutoML
        AutoML
        H2O AutoML
    Minimum system requirements to use H2O AutoML
    Installing Java
    Basic implementation of H2O using Python
        Installing Python
        Installing H2O using Python
    Basic implementation of H2O using R
        Installing R
        Installing H2O using R
    Training your first ML model using H2O AutoML
        Understanding the Iris flower dataset
        Model training
    Summary
Chapter 2: Working with H2O Flow (H2O’s Web UI)
    Technical requirements
    Understanding the basics of H2O Flow
        Downloading and launching H2O Flow
        Exploring H2O Flow
    Working with data functions in H2O Flow
        Importing the dataset
        Parsing the dataset
        Observing the dataframe
        Splitting a dataframe
    Working with model training functions in H2O Flow
        Understanding the AutoML parameters in H2O Flow
        Training and understanding models using AutoML in H2O Flow
    Working with prediction functions in H2O Flow
        Making predictions using H2O Flow
        Understanding the prediction results
    Summary
Part 2 H2O AutoML Deep Dive
Chapter 3: Understanding Data Processing
    Technical requirements
    Reframing your dataframe
        Combining columns from two dataframes
        Combining rows from two dataframes
        Merging two dataframes
    Handling missing values in the dataframe
        Filling NA values
        Replacing values in a frame
        Imputation
    Manipulating feature columns of the dataframe
        Sorting columns
        Changing column types
    Tokenization of textual data
    Encoding data using target encoding
    Summary
Chapter 4: Understanding H2O AutoML Architecture and Training
    Observing the high-level architecture of H2O
        Observing the client layer
        Observing the JVM component layer
    Learning about the flow of interaction between the client and the H2O service
        Learning about H2O client-server interactions during the ingestion of data
        Knowing the sequence of interactions in H2O during model training
    Understanding how H2O AutoML performs hyperparameter optimization and training
        Understanding hyperparameters
        Understanding hyperparameter optimization
    Summary
Chapter 5: Understanding AutoML Algorithms
    Understanding the different types of ML algorithms
    Understanding the Generalized Linear Model algorithm
        Introduction to linear regression
        Understanding the assumptions of linear regression
        Working with a Generalized Linear Model
    Understanding the Distributed Random Forest algorithm
        Introduction to decision trees
        Introduction to Random Forest
        Understanding Extremely Randomized Trees
    Understanding the Gradient Boosting Machine algorithm
        Building a Gradient Boosting Machine
    Understanding what is Deep Learning
        Understanding neural networks
    Summary
Chapter 6: Understanding H2O AutoML Leaderboard and Other Performance Metrics
    Exploring the H2O AutoML leaderboard performance metrics
        Understanding the mean squared error and the root mean squared error
        Working with the confusion matrix
        Calculating the receiver operating characteristic and its area under the curve (ROC-AUC)
        Calculating the precision-recall curve and its area under the curve (AUC-PR)
        Working with log loss
    Exploring other model performance metrics
        Understanding the F1 score performance metric
        Calculating the absolute Matthews correlation coefficient
        Measuring the R2 performance metric
    Summary
Chapter 7: Working with Model Explainability
    Technical requirements
    Working with the model explainability interface
        Implementing the model explainability interface in Python
        Implementing the model explainability interface in R
    Exploring the various explainability features
        Understanding residual analysis
        Understanding variable importance
        Understanding feature importance heatmaps
        Understanding model correlation heatmaps
        Understanding partial dependency plots
        Understanding SHAP summary plots
        Understanding individual conditional expectation plots
        Understanding learning curve plots
    Summary
Part 3 H2O AutoML Advanced Implementation and Productization
Chapter 8: Exploring Optional Parameters for H2O AutoML
    Technical requirements
    Experimenting with parameters that support imbalanced classes
        Understanding undersampling the majority class
        Understanding oversampling the minority class
        Working with class balancing parameters in H2O AutoML
    Experimenting with parameters that support early stopping
        Understanding early stopping
        Working with early stopping parameters in H2O AutoML
    Experimenting with parameters that support cross-validation
        Understanding cross-validation
        Working with cross-validation parameters in H2O AutoML
    Summary
9
Exploring Miscellaneous Features in H2O AutoML
    Technical requirements
    Understanding H2O AutoML integration in scikit-learn
        Building and installing scikit-learn
        Experimenting with scikit-learn
        Using H2O AutoML in scikit-learn
    Understanding H2O AutoML event logging
    Summary
Chapter 10: Working with Plain Old Java Objects (POJOs)
    Technical requirements
    Introduction to POJOs
    Extracting H2O models as POJOs
        Downloading H2O models as POJOs in Python
        Downloading H2O models as POJOs in R
        Downloading H2O models as POJOs in H2O Flow
    Using a H2O model as a POJO
    Summary
Chapter 11: Working with Model Object, Optimized (MOJO)
    Technical requirements
    Understanding what a MOJO is
    Extracting H2O models as MOJOs
        Extracting H2O models as MOJOs in Python
        Extracting H2O models as MOJOs in R
        Extracting H2O models as MOJOs in H2O Flow
    Viewing model MOJOs
    Using H2O AutoML model MOJOs to make predictions
    Summary
Chapter 12: Working with H2O AutoML and Apache Spark
    Technical requirements
    Exploring Apache Spark
        Understanding the components of Apache Spark
        Understanding the Apache Spark architecture
        Understanding what a Resilient Distributed Dataset is
    Exploring H2O Sparkling Water
        Downloading and installing H2O Sparkling Water
        Implementing Spark and H2O AutoML using H2O Sparkling Water
    Summary
Chapter 13: Using H2O AutoML with Other Technologies
    Technical requirements
    Using H2O AutoML and Spring Boot
        Understanding the problem statement
        Designing the architecture
        Working on the implementation
    Using H2O AutoML and Apache Storm
        What is Apache Storm?
        Understanding the problem statement
        Designing the architecture
        Working on the implementation
    Summary
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