MLOps for Generative AI
The rapid improvement in Generative AI promises use cases that were unthinkable just a year ago. Beyond the hype, deploying LLMs securely in user-facing production applications is a new and complex challenge that renders MLOps more relevant than ever. How do you deploy these models into real business environments, along with the required data and application logic? How do you serve them continuously, efficiently, and at scale? How do you manage their life cycle in production (deploy, monitor, retrain)? How do you leverage GPUs efficiently for your Hugging Face deep learning models?
Hugging Face is a critical enabler for Generative AI use cases, making pre-trained LLMs accessible for anyone to leverage the power of these models in a wide range of applications.
Our Session at NVIDIA GTC 2023: How to Easily Deploy Your Hugging Face Models to Production, at Scale
In the session below, Iguazio CTO Yaron Haviv shares MLOps orchestration best practices to automate the continuous integration and deployment of Hugging Face LLM models, along with the application logic in production. Haviv also shares a demo showing how to manage and monitor the application pipelines, at scale and how to enable GPU sharing to maximize application performance while protecting your investment in AI infrastructure as well as how to make the whole process efficient, effective, and collaborative.