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 â€“ it automates various repetitive steps, and this encapsulation helps developers focus on results rather than handling complexities.
Youâ€™ll begin by understanding how H2Oâ€™s AutoML simplifies the implementation of ML by providing a simple, easy-to-use interface to train and use ML models. Next, youâ€™ll see how AutoML automates the entire process of training multiple models, optimizing their hyperparameters, as well as explaining their performance. As you advance, youâ€™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, youâ€™ll take a hands-on approach to implementation using H2O thatâ€™ll enable you to set up your ML systems in no time.
By the end of this H2O book, youâ€™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.