Events

In this talk, we will introduce Elemeta, our OSS meta-feature extractor library in Python, which applies a structured approach to unstructured data by extracting information from text and images to create enriched tabular representations. With Elemeta, practitioners can utilize structured ML monitoring techniques in addition to the typical latent embedding visualizations and engineer alternative features to be utilized in simpler models such as decision trees.

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.

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.

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

Swing by the Superwise booth and let's talk MLOps and ML monitoring.

Swing by the Superwise booth and let's talk MLOps and ML monitoring.