Senthil Padmanabhan, VP of Platform and Infrastructure at eBay, discusses how eBay’s use of fine-tuning is improving their operational workflows. He explains why fine-tuning existing models can often be more cost-effective than training from scratch, and how methods like low-ranking adaptation (LoRa) are pivotal in improving AI performance.

Key Takeaways:

The impact of strategic fine tuning on achieving peak performance
AI's broader impact on eBay's infrastructure, fraud detection, and customer-facing experiences
The challenges of tech debt and software upgrades
Case studies from eBay that illustrate productivity gains via fine-tuning
Considerations for choosing between open and proprietary AI models
The importance of data quality over quantity in fine-tuning processes
Predictions for the future of AI development and fine-tuning techniques

Guest Bio: Senthil Padmanabhan is a Vice President and Technical Fellow at eBay, where he runs Platform and Infra (private cloud) Engineering for the global eCommerce marketplace, providing infrastructure for 4000+ engineers to deliver products at scale. Before this role, he was in the Product engineering space, building consumer-facing applications at eBay and Yahoo.

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Produced by: Sam Laliberte

LIVE from AI Summit: The Impact of Strategic Fine Tuning on Achieving Peak Performance

The Brave Technologist

LIVE from AI Summit: The Impact of Strategic Fine Tuning on Achieving Peak Performance