Deploy and Monitor your ML Application with Flask and WhyLabs

This article discusses the importance of improving observability for AI systems post-deployment. It presents an approach to enhance the observability of ML applications by efficiently logging and monitoring models using Flask and WhyLabs. The author demonstrates this through a Flask application for pattern recognition based on the Iris Dataset, integrated with the WhyLabs Observability Platform. The platform allows access to statistics, metrics, and performance data gathered from every part of the ML pipeline. The article also covers how to detect feature drift using monitoring dashboards.