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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
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