Gartner: How to Operationalize Machine Learning and Data Science Projects


“According to the Gartner Data Science Team survey, conducted at the end of 2017, even within organizations benefiting from the expertise of mature data science teams, less than half of data science projects end up being fully deployed. Another survey, conducted in 2017 on Data and Analytics Trends points to the fact that this phenomenon is not unique to data science, and that deploying AI projects into business processes or applications remains the principal barrier to delivering business value.”*

We believe this gap exists because it is extremely difficult to move models (the core of many data science, machine learning, and AI solutions) beyond development and into production.

Gartner’s report “How to Operationalize Machine Learning and Data Science Projects” sheds light on the following critical issues:

  • Key challenges organizations face with ModelOps.
  • Systematic recommendations for data and analytics leaders to address their ModelOps challenges.

About Domino Data Lab

Domino Data Lab provides the world’s most advanced data science solution, powering organizations that are using predictive models to drive their business. Companies including Allstate, Coatue, Dell, Bayer, Moody’s, and startups alike use Domino to accelerate breakthrough research, increase collaboration and productivity of data scientists, and more rapidly deliver models to drive business impact.

About Gartner

Gartner, Inc. is the world’s leading research and advisory company. They help business leaders across all major functions in every industry and enterprise size with the objective insights they need to make the right decisions. They have been publishing the Magic Quadrant for Data Science Platforms (titled Magic Quadrant for Advanced Analytics Platforms prior to 2017) since 2014.

*Gartner: How to Operationalize Machine Learning and Data Science Projects, Erick Brethenoux, Shubhangi Vashisth, Jim Hare, July 2018.

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