Redefining MLOps with Model Deployment, Management and Observability in Production

May 24, 10:00 AM PDT
  • Virtual () Wallaroo
  • 190 RSVP

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 we will take a deep dive into the new ML model monitoring and drift detection technology. We will discuss:
- How to track the ongoing accuracy of their 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.

We will also discuss 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.

Younes Amar

Head of Product Wallaroo AI. Prior to joining Wallaroo, Younes was the data science and AI product lead at Tempus Labs, working with a large team of data scientists, with the mission of leveraging large scale multimodal clinical and genomic data to develop AI-enabled products that help accelerate scientific discovery and improve patients’ outcomes. Younes has extensive experience in software engineering and product development with focus on delivering high impact analytics and ML platforms in various industries such as ESG, Government, logistics, healthcare and insurance
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