HOW TO OPERATIONALIZE MACHINE LEARNING AND DATA SCIENCE PROJECTS

The data science and machine learning platform space is dynamic and crowded. In addition to understanding what the market has to offer, shopping for a platform means assessing the needs of your data science, IT, and leadership teams.

There’s a lot to contemplate before choosing the right platform to accelerate your machine learning life cycle. Our buyer’s guide helps you through the process with an overview of key considerations and an interactive checklist of 60+ infrastructure, security, and integration features.

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53% of data science models are not fully deployed, and nearly half are not deployed at all. When models are not deployed into business processes or applications, it means are not generating business value. Operationalizing data science and machine learning projects is the key to deploying and maintaining models at scale.

 Download this free research note from Gartner to explore the skills and processes your team needs to make a recurring impact.

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Gartner, How to Operationalize Machine Learning and Data Science Projects, Erick Brethenoux, Shubhangi Vashisth, Jim Hare, 3 July 2018. Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, express or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.