Advancing AI Starts with Strategic ML DataOps: Key Takeaways

January 17, 2022

Companies in the proof-of-concept stage require large volumes of data to prove their ML model. But, as companies reach production, the volumes of data and data precision take a whole new level of effort.

iMerit Founder and CEO, Radha Basu provides some critical insights into the three key elements required to successfully deploy AI — people, process and technology in her article on CIO Review.

Here are 3 key takeaways from the article:

  • People are needed to advance AI
    AI cannot achieve human-like intelligence without leveraging human intelligence to learn. In many cases, domain experts are needed to train the AI. Talent, expertise, and experience have a major impact on the quality and precision of data needed to solve complex problems.
  • Using the right processes across ML DataOps is key
    Technology is only as good as one’s ability to use it properly, which is why enterprises building AI applications must leverage the right processes across their ML DataOps. Leaning on AI data solutions providers like iMerit gives companies access to domain experts who can guide every phase of a company’s ML DataOps process including requirements definition, workflow engineering, technology and tool selection, talent identification, execution, evaluation and refinement, and analytics.
  • AI data solutions and technology are critical for managing ML data pipelines
    The adoption of AI data solutions and technology infrastructure like iMerit Data Studio is a critical path to creating the high-quality data needed to bring AI to market at scale. iMerit Data Studio delivers high-quality data at scale, enables companies to rapidly scale data annotation teams, leverages experts-in-the-loop across a variety of industries, uses the right annotation tools across the ecosystem, and gives flexibility in data formats and access to quality metrics to analyze results along the way.