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.

May 9th, 2023 | 2:40 PM

Introducing Elemeta: OSS meta-feature extractor for NLP & vision

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

Data-driven retraining with production insights