GPU Considerations, Labeling Privacy, Rapid Fine Tuning, and the Role of Private Eval Pipelines to Benchmark New Models
MLOps.community
00:00
Navigating the Evolving AI and GPU Landscape
This chapter explores the shifting dynamics of AI model performance, with a focus on the rise of new competitors and the challenges this brings for product development. It delves into the complexities of GPU resource management, including the trade-offs between on-demand and reserved capacities, while highlighting the need for adaptability in model deployment. Additionally, the discussion addresses hardware selection, privacy concerns, and the implications of GPU shortages in the context of training and inference strategies.
Transcript
Play full episode
