How to Achieve AI and Machine Learning Operational Excellence

Today, applied AI and Machine Learning are driving new ways to learn, understand, and profit in organizations.  There is an incredible opportunity, given the scale of available data and the low cost of computation, to revolutionize nearly every type of business.  As a result, teams of people and scores of companies are focusing on developing great “models” that derive business value.

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”

Watch this educational webinar How to Achieve AI and Machine Learning Operational Excellence co-hosted by Product Manager, Rehgan Avon, and VP of Business Development, Garrett Long.  Rehgan and Garrett will outline the basics of a Model Development Life Cycle approach and some of the core technical abstractions, processes, and organizational challenges experienced when scaling AI and Machine Learning in the enterprise:

  • How to deploy models
  • Who is responsible for what
  • Who should monitor assets
  • What does it mean to roll back a model
  • How to automate, as much as possible, iterations and updates
  • How to connect data science, data engineering and DevOps in a natural and scalable way


Originally Recorded
October 18, 2018