How to Operationalize Machine Learning and Data Science Projects
Published: 03 July 2018
ID: G00333499
Analyst(s): Erick Brethenoux | Shubhangi Vashisth | Jim Hare
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Summary
The democratization of machine learning platforms is proliferating analytical assets and models. The challenge now is to deploy and operationalize at scale. Data and analytics leaders must establish operational tactics and strategies to secure and systematically monetize data science efforts.
Table of Contents
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Introduction
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Analysis
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Establish a Close, Ongoing Dialogue With Business Counterparts
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Establish a Systematic Operationalization Process
- Operationalization Cycle Functionality
- Operationalization Cycle Process
- Release Phase: Testing Models in Business Conditions
- Activation Phase: Operating Models in Real Business Conditions
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Monitor, Re-evaluate, Tune and Manage Models on an Ongoing Basis
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Secure the Help of Nonanalytical Personnel
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Monitor and Constantly Revalidate the Business Value Delivered by Machine Learning Models in Production
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Establish a Close, Ongoing Dialogue With Business Counterparts
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