Machine Learning at Scale with H2O

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. ...