Continuous ML Improvement: Observability with Built-In Explainability
Speakers: Amy Hodler, Lead Evangelist, Fiddler | Amal Iyer, Data Scientist, Fiddler
ML models tend to lose their predictive power over time and can fail silently. In this session, we’ll review how to identify and stay ahead of the common culprits: model drift, data integrity, outliers and bias. You’ll see how cutting-edge explainable AI and model analytics can quickly find the root cause of operational issues. And we’ll outline how model and cohort comparison help teams iterate and get new models in production faster.
We’ll demonstrate how leading enterprises on AWS are achieving trustworthy AI by integrating model performance management into their MLOps lifecycle, with Amazon SageMaker and AWS. You’ll walk away knowing how to use continuous ML monitoring and explainability to achieve optimal model performance and accelerate business outcomes.
