More than half of the analytics and machine learning (ML) models created by organizations today never make it into production. Whether the reason is technical or organizational, the bottom line is that models not in production can't provide business impact. This book introduces the key concepts of MLOps to help data scientists and application engineers not only operationalize ML models to drive real business change but also maintain and improve those models over time.
Description:
More than half of the analytics and machine learning (ML) models created by organizations today never make it into production. Whether the reason is technical or organizational, the bottom line is that models not in production can't provide business impact. This book introduces the key concepts of MLOps to help data scientists and application engineers not only operationalize ML models to drive real business change but also maintain and improve those models over time.