The Stack Overflow Podcast
Grab bag! On the floor at HumanX
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.
17:55
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Quick takeaways
- DDN's data intelligence platform optimizes the machine learning lifecycle by enhancing data handling, improving throughput, and reducing latency for impactful AI outcomes.
- While APIs significantly simplify AI integration for developers, challenges in ensuring enterprise readiness and reliability remain crucial for broader adoption.
Deep dives
Optimizing Data for AI Solutions
The discussion highlights the critical role of DDN's data intelligence platform in enhancing the machine learning lifecycle through optimized data handling. DDN Infinia provides comprehensive data preparation and transformation services, focusing on improving throughput and reducing latency. It allows users to manage metadata extensively, enabling precise data selection, such as retrieving specific tissue samples for medical research. By maximizing efficiency in data movement based on enriched metadata, DDN supports users in both training and inference phases, emphasizing that high-quality data is essential for effective AI outcomes.
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