Data science and machine learning teams are now starting to be measured on business results rather than production metrics (the number of models produced, or projects started, for example). Consequently, the required disciplined approach brought about by commercial platforms is becoming a required condition to achieving business value and data science team sustainability.

By 2022, 75% of new end-user solutions leveraging AI and ML techniques will be built with commercial instead of open-source platforms

Gartner Predicts

What does Commercial AI/ML enable?

  • Better productivity: The assembly and retro-fitting of diverse OS tools requires lots of skills and manual labor, which has become the focal point of commercial attention, along with a clearer path toward business value.

  • Democratization of AI: Commercial providers will increasingly “smooth” the rough edges often associated with open-source projects by orchestrating the user experience and “connecting the dots.” This provides much more integrated capabilities through orchestration which makes cutting-edge AI/ML development much more available to the broader skill set found in most enterprises.

  • Better AI/ML planning and roadmaps: Current AI strategies are full of uncertainty, conflict and vagueness. The comeback of capable commercial platforms will increase plannability and IT roadmaps by providing concrete anchor points into the IT software infrastructure.

How Does This Impact Your Organization and Skills?

  • Increased use of commercial data science and machine learning will help to narrow the current skills gap in these areas.

  • It will also facilitate greater collaboration among AI/ML developers with varying levels of skill.

We've got you covered!

Relevant Sessions

  • The Foundation of Data Science and Machine Learning: Delivering Value in the Age of AI
  • Augmented Data Management Forges a New Alliance Between Human and Artificial Intelligence
  • Top Technology Trends in Data and Analytics That Will Change Your Business
  • Myths and Pitfalls of Artificial Intelligence and How to Navigate Them
  • Magic Quadrant for Analytics and BI and Data Science and ML

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