As AI and ML models become integral to software systems, ensuring their accuracy and reliability through effective testing is paramount. Traditional testing approaches often fall short in addressing the unique challenges posed by these models, such as handling large datasets, verifying model predictions, and maintaining robustness against data drift. This presentation explores Otis Elevator's journey in automating the testing of AI/ML models using cutting-edge tools and techniques. Ayisha Tabbassum's team faced significant hurdles in manually testing their AI models, from the sheer volume...
Ayisha Tabbassum
Cloud Architect
Otis Elevator Co
Ayisha Tabbassum is a certified Enterprise and Multi-Cloud Architect with over nine years of industry experience. She has a proven track record in enterprise, business, and cloud architecture, focusing on infrastructure automation and CI-CD application deployments. Ayisha currently leads observability and AI/ML test automation initiatives at her organization, creating robust testing frameworks using tools like TensorFlow Extended, Apache Airflow, and MLflow. She holds a Master’s in Computer Science from Indiana University and has presented at various technical conferences on AI and Enterprise Architecture.