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Introducing MLOps: How to Scale Machine Learning in the Enterprise

By: Publication details: Mumbai: Shroff Publishers & Distributors Pvt. Ltd., 2020Description: 169ISBN:
  • 9789385889769
Subject(s): DDC classification:
  • 006.31 TRE
Summary: 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.
List(s) this item appears in: New Arrivals for the Month of September - 2023
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Item type Current library Collection Call number Status Date due Barcode Item holds
Book Book Alliance College of Engineering and Design CSE & IT 006.31 TRE (Browse shelf(Opens below)) Checked out 18/09/2024 E12293
Total holds: 0

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.

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