QA in ML

Trust is mission-critical for any technology, so if AI/ML solutions are to supplant and complement software, AI must reach the reliability standards currently expected from software. The difference is Quality Assurance (QA) has existed in software for three decades, and the burgeoning field of ML has barely begun to perform quality controls. This talk explores the history of QA, its crucial role, and lessons from other disciplines applicable to machine learning. It also discusses the role of Explainable AI, MLOps, data engineering, and data science best practices. The session highlights the challenges of standardizing QA efforts in ML, the emergence of new roles like ML QA within DevOps, SecOps, and MLOps teams, and the evolution of data scientist and ML engineer roles to enhance quality, ultimately aiming to make AI/ML more trustworthy to end-users.