The Stack Overflow Podcast Grab bag! On the floor at HumanX
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Apr 25, 2025 In this engaging discussion, Priya Joseph, Field CTO at DDN, dives into the intricacies of optimizing data for AI and machine learning. She emphasizes the importance of throughput and latency for achieving efficiency, particularly in breast cancer diagnostics. The conversation touches on data quality, the delicate balance between open and closed source models, and the evolving interplay between APIs and AI technology. Priya's insights reveal how effective data preparation can elevate AI outcomes, making this a must-listen for tech enthusiasts.
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DDN's AI Data Optimization
- DDN optimizes throughput and latency to accelerate data movement and preparation for AI pipelines.
- It integrates a file system and metadata layer for enhanced data handling and retrieval based on detailed annotations.
Metadata Enables Continuous Training
- Use metadata to create precise subsets and generate synthetic data for continual AI model training.
- This strategy helps overcome potential limitations from running out of diverse training data.
Use Metadata and Guardrails
- Adding metadata and good category management improves AI model accuracy and reduces hallucinations.
- Guardrails and retrieval-augmented generation (RAG) techniques are crucial for regulated industries.