As the adoption of MLOps for training, deploying large ML models grows, the need for multitenancy in MLOps systems also increases. As organizations scale their ML operations, the ability to share resources efficiently, maintain isolation, and ensure security across multiple teams becomes paramount. Thus the talk covers some of the fundamental needs and challenges in adopting multi-tenancy in MLOps.

The talk covers how features such as isolation of ML workflows, resource quotas, role-based access control, data isolation, and a shared artifact repository contribute to a secure and efficient multi-tenant environment and helps overcome some of the associated challenges in running MLOps efficiently. Achieving the above is hard if done independently.

The talk demonstrates how to implement the above with ease using open-source tools like Flyte efficiently and cost-effectively, enabling different teams within an organization to operate in isolation while sharing resources efficiently.