The next generation of QA problems to solve is here AI and ML. AI and Machine learning have a unique set of challenges when it comes to fit-for-purpose data. How do you create the large amount of data required? How do you tweak the data so it’s meaningful for AI / ML consumption? This seminar will look into how to synthetically create data using GenRocket. The seminar will provide guidelines for how to approach AI / ML Testing. We will walk through a use case for a well-known financial services company.

Takeaways from the talk:

•  How to approach AI \ ML data requirements
• The challenges a company had to overcome
• How GenRocket was used as a data solution
• Review the Outcomes