Redefining MLOps with Model Deployment, Management & Observability
What happens after your machine learning models are deployed in production? How do you make sure that your model performance does not degrade as data and the world change?
The constantly changing data creates challenges for data scientists and engineering teams on how to detect which models have been affected and how to get their ML applications up and running seamlessly.
In this session our Head of Product takes a deep dive into the new ML model monitoring and drift detection technology.
What you’ll learn in this session:
How to track the ongoing accuracy of models in production
- How to immediately detect drift before it causes significant damage to the business
- How to locate the cause of model drifting in live environments
Younes also discusses how data scientists and ML engineers can collaborate effectively using their respective tools to identify issues and take the necessary actions with a live demo and a real world use case.