iMerit Founder and CEO Radha Basu shares why machine learning data operations plays a critical role in bringing artificial intelligence to market at scale and unveils why 2022 is shaping up to be the ‘Year of ML DataOps.’
Here are 3 key takeaways from the session:
- A feedback loop of results will continuously force enterprises to adapt their ML data operations to meet the demands of their models. Algorithms in the field will come back with edge cases, which data operations will scramble to resolve before the algorithm is redeployed.
- Factors that help increase annotation efficiency include, expertise on nuances and edge cases, work experience, motivation for the job, cross-trained annotators, confidence to challenge algorithms and provide insights, diversity of opinions and backgrounds, and accountability and transparency.
- The combination of technology and human-in-the-loop expertise gives enterprises a true end-to-end solution as they move to deploy their models in the field. By bridging together the right expertise, judgment, and technology, the highest quality data possible will be generated.
