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On-Demand Webinar: “Redefining MLOps with Model Deployment, Management & Observability”

Our Head of Product unravels challenges and solutions in monitoring ML models, detecting drift, and ensuring seamless collaboration between data scientists and ML engineers. Discover practical insights through a live demo and a real-world use case, empowering you to effectively deploy and manage ML in production.

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, including: 

  • 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.

He 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.

Then check out our free Community Edition to get started today, or contact us if you have any questions about how to better deploy and manage machine learning in production.

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Solutions See how our unified ML platform supports any model for any use case
Computer Vision (AI) Run even complex models in constrained environments, with hundreds or thousands of endpoints