Arize:Observe Unstructured - A Theory Primer for UMAP: Uniform Manifold Approximation and Projection 

Join our talk with Leland McInnes, the creator of UMAP, as he walks through the theory and mathematics behind UMAP. UMAP visualizations of embeddings can be used in practice to troubleshoot high dimensional data in a low dimensional space. Embeddings are vector (mathematical) representations of data where linear distances capture structure in the original datasets, and are proliferating in modern ML systems. This talk will cover the evolution of UMAP, and how UMAP can be used in practice to troubleshoot high dimensional data.