Building A Robust ML DataOps Ecosystem: Key Takeaways

December 24, 2021

Machine learning is, at its core, a combination of data and the model. As ML has scaled, there has been a great deal of focus on tuning and optimizing the model to produce the best possible performance. In line with this development, a greater focus is seen today on ML DataOps – the subset of MLOps that focuses entirely on the collection, preparation, and use of data complete with a feedback cycle.

iMerit Founder and CEO, Radha Basu discusses the three pillars of ML DataOps – people, technology, and processes; and what an ideal DataOps ecosystem player looks like, in her article on CXO Outlook: Building A Robust ML DataOps Ecosystem.

Here are 3 key takeaways from the article:

  1. A structured ML DataOps pipeline creates the ability to handle data at scale, as it goes through the cyclical journey of AI training and deployment. The all-critical transition from testing to production must be tackled through repeatable and scalable processes, to ensure the sustainability of the resulting AI solutions.
  2. Within the ML DataOps ecosystem, companies focus on different aspects of the data pipeline and bring their specialized solutions to the market. The solutions offered by companies within the ecosystem broadly fall under three categories: people, technology and tooling, and processes or end-to-end solutions.
  3. The last mile of AI development lies in the resolution of edge cases. The ability to handle edge cases can make or break the production readiness of a trained ML system. Companies in the ecosystem are constantly finding ways to seamlessly integrate human expertise with tooling capabilities for auditing, monitoring, and handling edge cases. This is a critical step in getting ML past the final hurdles to production.

Read the complete article here: Building A Robust ML DataOps Ecosystem