2018 Gartner Research Report on How to Operationalize Machine Learning and Data Science Projects

“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.”

“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 (Data and Analytics Trends) conducted in 2017 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.”*

Don’t be a statistic – help your organization avoid the pitfalls of many. Download the report and we believe you will learn:

  • The challenges most face when putting models into production and operating them in the long-term
  • The tactics Gartner recommends to getting past these challenges
  • How you can be an asset to your organization by helping it overcome the model deployment challenges 
  • How to really cash in on the data science initiatives you’re investing in today
*Sources: Gartner, How to Operationalize Machine Learning and Data Science Projects, Erick Brethenoux, Shubhangi Vashisth, et al., 3 July 2018.
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