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ML Ops: Operationalizing Data Science

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Description:More than half of the analytics and machine learning (ML) models created by organizations today never make it into production. Instead, many of these ML models do nothing more than provide static insights in a slideshow. If they aren’t truly operational, these models can’t possibly do what you’ve trained them to do.This report introduces practical concepts to help data scientists and application engineers operationalize ML models to drive real business change. Through lessons based on numerous projects around the world, six experts in data analytics provide an applied four-step approach—Build, Manage, Deploy and Integrate, and Monitor—for creating ML-infused applications within your organization.You’ll learn how to: - Fulfill data science value by reducing friction throughout ML pipelines and workflows - Constantly refine ML models through retraining, periodic tuning, and even complete remodeling to ensure long-term accuracy - Design the ML Ops lifecycle to ensure that people-facing models are unbiased, fair, and explainable - Operationalize ML models not only for pipeline deployment but also for external business systems that are more complex and less standardized - Put the four-step Build, Manage, Deploy and Integrate, and Monitor approach into actionWe have made it easy for you to find a PDF Ebooks without any digging. And by having access to our ebooks online or by storing it on your computer, you have convenient answers with ML Ops: Operationalizing Data Science. To get started finding ML Ops: Operationalizing Data Science, you are right to find our website which has a comprehensive collection of manuals listed.
Our library is the biggest of these that have literally hundreds of thousands of different products represented.

Pages: —

Format: PDF, EPUB & Kindle Edition

Publisher: O'Reilly Media, Inc.

Release: 2020

ISBN: 1492074659

Description: More than half of the analytics and machine learning (ML) models created by organizations today never make it into production. Instead, many of these ML models do nothing more than provide static insights in a slideshow. If they aren’t truly operational, these models can’t possibly do what you’ve trained them to do.This report introduces practical concepts to help data scientists and application engineers operationalize ML models to drive real business change. Through lessons based on numerous projects around the world, six experts in data analytics provide an applied four-step approach—Build, Manage, Deploy and Integrate, and Monitor—for creating ML-infused applications within your organization.You’ll learn how to: - Fulfill data science value by reducing friction throughout ML pipelines and workflows - Constantly refine ML models through retraining, periodic tuning, and even complete remodeling to ensure long-term accuracy - Design the ML Ops lifecycle to ensure that people-facing models are unbiased, fair, and explainable - Operationalize ML models not only for pipeline deployment but also for external business systems that are more complex and less standardized - Put the four-step Build, Manage, Deploy and Integrate, and Monitor approach into actionWe have made it easy for you to find a PDF Ebooks without any digging. And by having access to our ebooks online or by storing it on your computer, you have convenient answers with ML Ops: Operationalizing Data Science. To get started finding ML Ops: Operationalizing Data Science, you are right to find our website which has a comprehensive collection of manuals listed.
Our library is the biggest of these that have literally hundreds of thousands of different products represented.

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ML Ops: Operationalizing Data Science

ML Ops: Operationalizing Data Science

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