Basically, well-thought-out training data means fewer exceptions and errors. The main reason to invest in machine learning and AI tools is to be able to solve problems faster and more reliably. There is a misnomer by newcomers to the industry that AI is self-propelled and can be fully autonomous. However, the truth is that for most businesses, 10-20% error and exceptions will always exist.
This bucket of low confidence or exceptional Color Correction Service records is not a curse, it is an opportunity. Exceptions can be handled and analyzed "manually" and then can be converted into new or better rules or logic. 8. What process would you recommend for ongoing data quality assurance? When, if at all, would you recommend machine learning transition to fully autonomous operation? Does training ever stop for an AI? Vidya: Certainly the heavy lifting required during the initial setup of an is very different from what is required for ongoing maintenance.