Preface

At this point in time, machine learning (ML) requires little introduction: it is both pervasive and transformative to businesses, non-profits, and scientific organizations. ML is built on data. We are all aware of the exponential growth of data collected each year, and the growing diversity of sources that generate this data. This book is about leveraging these massive data volumes to do ML. We call this machine learning at scale and define it on three pillars: building high-quality models on large to massive datasets, deploying them for scoring in diverse enterprise environments, and navigating multiple stakeholder concerns along the way. Here, scale considers both data volume and enterprise context, model building, and model deployment. In this book, we will show you, in practical terms, how H2O overcomes the many challenges of performing ML at scale.

The book starts with a general overview of the challenges of performing ML at scale, and how the H2O framework overcomes these challenges while producing high-quality models and enterprise-grade deployments. From there, it transitions to advanced treatment of model-building techniques and model deployment patterns using H2O at Scale. We then look at its technological underpinnings from the perspective of multiple enterprise stakeholders who need to understand, deploy, and maintain this system, and show how this relates to data scientist activities and needs. We finish by showing how H2O at Scale can be implemented on its own or as part of the larger and richly featured H2O AI Cloud platform, where it takes on exciting new levels of ML possibilities and business value.

By the end of this book, you'll have the knowledge needed to build high-quality explainable ML models from massive datasets, deploy these models to a great diversity of enterprise systems, and assemble state-of-the-art ML solutions that achieve unique forms of business value.