Explore methods for structuring and visualizing unstructured data in machine learning applications through this 51-minute podcast episode featuring Markus Stoll, CTO of Renumics. Dive into techniques like UMAP for dimension reduction and tools like Renumics Spotlight to simplify data analysis for ML. Learn how to uncover hidden patterns in multimodal data, evaluate model performance for data subgroups, and identify failure modes in ML models. Gain insights on combining interpretable features, metadata, and embeddings to enhance understanding of machine learning data across various applications, from classification and detection to Retrieval-Augmented Generation.
Visualize - Bringing Structure to Unstructured Data - MLOps Podcast #258
Overview
Syllabus
[] Markus' preferred coffee
[] Takeaways
[] Please like, share, leave a review, and subscribe to our MLOps channels!
[] Register for the Data Engineering for AI/ML Conference now!
[] Current focus and updates
[] 3D Embeddings Visualization Explained
[] Question Embeddings vs Retrieval
[] Using heat maps effectively
[] User insights visualization RAG
[] 3D Crash Simulation Analysis
[] Simulation purpose clarification
[] Evaluating test data use cases
[] Real-world car testing
[] Identifying data issues early
[] Multimodal data integration
[] Custom vs Fine-tuned models
[] Data processing challenges
[] Use case-driven MVP
[48:26 - ] SAS Ad
[] Wrap up
Taught by
MLOps.community
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