Events

Meet the team and learn about our ML monitoring methodologies and best practices.

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Webinars

Workshop: A Guide to Putting Together a Continuous ML Stack

We’ll take a hands-on dive into implementing the 1st level of MLOps maturity and performing continuous training of the model by automating our ML pipeline. We’ll start with the ML pipeline and see how we can detect performance degradation and data drift in order to trigger the pipeline and create a new model based on fresh data.
November 22nd, 2022 | 10:00 AM EST
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Talks

Retraining won’t fix your model (always)

When models misbehave, we often turn to retraining to fix the problem, but retraining is not always the best or only solution out there. In this session we'll take a crash intro in alternative techniques.
November 29th, 2022 | 9:45 AM EST
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Conferences

Toronto Machine Learning Summit

Swing by booth #6 and let's talk MLOps and ML observability
November 29th - 30th, 2022 | Toronto
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Conferences

ODSC West

Swing by booth #28 and let's talk MLOps and ML observability
November 1st - 3rd, 2022 | San Francisco
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Talks

Data-driven retraining with production insights

In this talk, we'll showcase, through ML monitoring and notebooks, how data scientists and ML engineers can leverage ML monitoring to find the best data and retraining strategy mix to resolve machine learning performance issues. This data-driven, production-first approach enables more thoughtful retraining selections, shorter and leaner retraining cycles, and can be integrated into MLOps CI/CD pipelines for continuous model retraining upon anomaly detection.
November 1st - 3rd, 2022 | San Francisco
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Talks

A Guide to Multi-Tenancy Architectures in ML

This session will cover architectural considerations for multi-tenancy in ML, best practices in traditional software engineering that can be copy/pasted over to MLOps, as well as new considerations unique to ML
October 13th, 2022 | 1:00 PM ET
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