Continuous Quality Assurance (QA) Architecture workflow for Live Learning ML Systems

Session details:

The session aims to demonstrate the effectiveness of continuous QA in reducing undetected model failures, enhancing system reliability, and shortening feedback-to-repair cycles. By embedding QA automation into MLOPs pipelines, this session addresses a critical gap in current QA methodologies for adaptive ML systems, contributing towards scalable, audit-ready, and resilient AI deployments.

takeaways-

Shift Left QA methodologies in AI powered ML systems 
A Practical Framework for Agile + QA +AI
Challenges to Address in Large Organization ML- QA processes