Speaker
Diana Gaponcic
(IT-PW-PI)
Description
This session will focus on the infrastructure and low level tools required to efficiently deploy machine learning applications. In particular, it will cover:
- The different data types and how they can impact ML workloads, as well as support in different types of hardware and software libraries
- Key differences between CPUs and GPUs and how they impact ML workloads (training and serving)
- The available techniques in IT services for GPU sharing and partitioning. In particular, it will cover how applications can build on the existing Kubernetes service to simplify these operations
- Hands-on exercises on using GPUs for different types of workloads
