Generative AI is under the spotlight and it has diverse applications but there are also many considerations when deploying a generative model at scale. This presentation will make a deep dive into multiple architectures and talk about optimization hacks for the sophisticated data pipelines that generative AI requires. The session will cover:
– How to create and prepare a dataset for training at scale in single GPU and multi GPU environments.
– How to optimize your data pipeline for training and inference in production considering the complex deep learning models that need to be run.
– Tradeoff between higher quality outputs versus training time and resources and processing times.
Agenda:
– Basic concepts in Generative AI: GAN networks and Stable Diffusion
– Training and inference data pipelines
– Industry applications and use cases
Talk by: Paula Martinez and Rodrigo Beceiro
Here’s more to explore:
LLM Compact Guide: https://dbricks.co/43WuQyb
Big Book of MLOps: https://dbricks.co/3r0Pqiz
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