From images and video to natural language and audio, unstructured data coupled with machine learning can unlock deeper AI potential and ROI for many organizations and use cases. Embeddings are the core of how deep learning models represent structures and are fundamental to how the next generation of ML models work.

Join us at Arize:Observe Unstructured to learn about emerging techniques like UMAP to transform unstructured data into embeddings that can be more efficiently processed by ML models, hear from leading-edge ML platform teams implementing new technologies to monitor and improve models in production, and try out these approaches and tools in a hands-on workshop.

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Arize Keynote

Arize:Observe Unstructured keynote

Jason Lopatecki, Founder and CEO, Arize AI
Aparna Dhinakaran, Co-Founder and CPO, Arize AI

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Hugging Face

Accelerating Machine Learning from Research to Production with Hugging Face

Jeff Boudier, Product Director, Hugging Face
Francisco Castillo, Software Engineer, Arize AI

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Workshop: Monitor & Troubleshoot Embeddings

Amber Roberts, Machine Learning Engineer, Arize AI

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Handling the Challenges of Unstructured Data, The Unsung Hero of Machine Learning

Dan Jeffries, Chief Technical Evangelist, Pachyderm / Managing Director, AIIA

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A Theory Primer for UMAP: Uniform Manifold Approximation and Projection

Leland McInnes, Founder, UMAP

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How to improve performance of unstructured models with less data

Maxime Voisin, Head of Catalog and Models, Labelbox
Claire Longo, Customer Success Lead, Arize AI

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Featured Speakers

Peter Welinder

VP of Product & Partnerships, OpenAI

Jeff Boudier

Product Director, Hugging Face

Leland McInnes

Creator of UMAP

Dan Jeffries

Chief Technical Evangelist, Pachyderm / Managing Director, AIIA

Maxime Voisin
Maxime Voisin

Head of Catalog and Models, Labelbox