What is ML Observability?
Will your model work in production? Why isn’t your model performing the way you thought it would? What’s wrong, why?
Successfully taking a machine learning model from research to production is hard. As more and more machine learning models are deployed into production, it is imperative we have better observability tools to monitor, troubleshoot, and explain their decisions.
ML Observability helps you eliminate the guesswork and deliver continuous model improvements. Learn how to:
- Use statistical distance checks to monitor features and model output in production
- Analyze performance regressions such as drift and how it impacts business metrics
- Use troubleshooting techniques to determine if issues are model or data related