Introducing MLOps

Description

More than half of the analytics and machine learning (ML) models created by organizations today never make it into production. Some of the challenges and barriers to operationalization are technical, but others are organizational. Either way, 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. Through lessons based on numerous MLOps applications around the world, nine experts in machine learning provide insights into the five steps of the model life cycle--Build, Preproduction, Deployment, Monitoring, and Governance--uncovering how robust MLOps processes can be infused throughout. This book helps you: Fulfill data science value by reducing friction throughout ML pipelines and workflows Refine ML models through retraining, periodic tuning, and complete remodeling to ensure long-term accuracy Design the MLOps life cycle to minimize organizational risks with models that are unbiased, fair, and explainable Operationalize ML models for pipeline deployment and for external business systems that are more complex and less standardized Review: when they say introduction they mean very light on content - Book isn't worth the price. It has some useful ideas, but at ~150 pages there isn't much depth... don't pay full price for this. Review: No colour, and it's also free on their website - It's in black and white, yet refers to colours in the text, which we can't see. Embarrassing cost saving decision. The material itself is rushed and high level. It's also FREE (and in colour) on the Dataiku website. Do not waste your money on this.