How to Design Your Analytic Development Life Cycle

According to Gartner Research, by 2020, more than 40% of data science tasks will be automated, resulting in increased productivity and broader usage by citizen data scientists. This is clear evidence that companies will be creating more and more analytic assets than ever.  As the model factory turns on, one important question arises:  How will we take these newly created assets to production?

At Open Data Group, we have spent the last two years helping companies optimize their analytic deployment capabilities, such as migrating credit scoring workloads to the cloud and forecasting macroeconomic indicators at scale.

Join us on June 14th at 1PM EST for an educational webinar How to Design Your Analytic Development Life Cycle co-hosted by Product Manager, Rehgan Avon, and VP of Business Development, Garrett Long.

This second installment of our “Lab to Factory” series is focused on some of the common barriers for getting a model out of development and into the production pipeline.  Rehgan and Garrett will help us understand:

  • Trends driving the adoption of machine learning in today’s market
  • How to define the Analytic Development Life Cycle
  • Common challenges migrating a model downstream to user acceptance testing
  • Best Practices for a User Acceptance Testing transition checklist

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