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Achieve DSML Value by Aligning Diverse Roles in an MLOps Framework

Diversify AI Talent for Improved Model Deployment

This Gartner research discusses the aspects of an MLOps lifecycle, the various roles and activities involved across business, data science, and IT teams, and Gartner's recommendations for success.

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Create Fusion Teams and Improve Operationalization

Blend Technical and Domain Expertise

According to Gartner, "Leveraging AI talent with diverse skills has always been challenging for organizations, resulting in hurdles in implementing and scaling MLOps. How to overcome this? Leverage diverse AI roles and skills to better deploy and operationalize machine learning projects successfully.”

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Build and Deploy AI at Scale

Close the Feedback Loop Between Operationalized Models and Their Impact

When building an AI strategy that is fit to carry the business through periods of uncertainty, it’s critical to have systems for monitoring models in production and be able to quickly introduce, test, train, and implement new models in order to shift strategies or adapt to changing environments on a dime. Dataiku’s MLOps capabilities enable teams to deploy, monitor, and machine ML projects in production — with speed and at scale.

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Gartner, Achieve DSML Value by Aligning Diverse Roles in an MLOps Framework, Anirudh Ganeshan, Afraz Jaffri, Farhan Choudhary, Shubhangi Vashisth, 22 September 2021. GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally, and is used herein with permission. All rights reserved. This graphic was published by Gartner, Inc. as part of a larger research document and should be evaluated in the context of the entire document. The Gartner document is available upon request from Dataiku.