📕 If only someone wrote the book on ML Observability*
Getting a model from research to production is hard. So is keeping it there as performance degradation can happen anytime. How can you quickly get to the bottom of issues in a post-COVID world that makes easy insights difficult?
Arize AI wrote a book on how to answer that question. ML Observability 101 covers everything from how to get to the bottom of model performance issues to primers on explainability, drift and service health.
Want to hear how one company is tackling ML monitoring and observability or ask questions live? Join us for a foray into ML observability in lending, featuring a fireside chat with Richard Woolston, Data Science Manager at America First Credit Union.