Current and Future State of ML DataOps Landscape

As enterprises dive deeper into commercializing AI applications to improve business efficiencies, many realize the massive transformation and increasing complexity of the machine learning data operations landscape.

In this session, seed stage and growth stage venture capitalists, Alfred Chuang, Founder and General Partner of Race Capital, Pavan Tripathi, Partner, Bregal Sagemount and Andy Pavlo, Associate Professor / Co-Founder, Carnegie Mellon University / OtterTune, share their perspectives on the current and future state of the machine learning data operations landscape.


Here are 3 key takeaways from the session:

  • For a long time, only large companies with lots of resources built machine learning models. Venture capitalists are seeing relatively small growth stage companies invest in AI and ML projects today. The entry barrier is much lower now, especially since last year.
  • Every company no matter what they do should have a data plan. We are seeing companies that have some sort of data asset whether incidental or purposeful are looking for ways to build applications with it and build automation within their existing platforms.
  • Having a data team is crucial for any company selling a data-driven product. However, it can be challenging to do so since the data scientist and data/ml engineering professions are relatively new. The easiest way would be to train employees with backgrounds in engineering or analytics.

Ready to learn how iMerit can help with your machine learning data operations?