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This talk explains how embeddings support unstructured machine learning models and how teams can monitor them for drift, visualize problems, and trace issues to underlying data. It includes a product demonstration.
Syllabus
[] Introduction to the topic
[] Troubleshooting unstructured ML models is difficult
[] Challenges with monitoring unstructured data
[] How data looks like
[] Embeddings are the backbone of unstructured models
[] ML teams need a common tool
[] What are embeddings?
[] The real WHY behind AI
[] ML observability for unstructured data
[] Index and Monitor every Embedding
[] Measuring drift of unstructured data
[] Interactive visualizations
[] Fix underlying data issue
[] Data-centric AI workflow
[] Demo of the product
[] Wrap up
Taught by
MLOps.community