The AI/ML industry is currently facing a significant challenge, as the number of available tools and deployment options have skyrocketed. It’s not always clear where you should run your training and inference workloads. Companies are struggling to determine the best path to increase infrastructure utilization and reduce overall operational and compute costs.
Join our panelists for an insightful discussion on
- How can you maximize the utilization of your compute/GPU resources
- What are your platform options if you want to run AI/ML on-premises due to compliance and governance reasons
- How to abstract the cloud native infrastructure and operational complexity from your data scientists and AI/ML model creators
- What are the use cases for running training in the public cloud and inference on-premises or on the edge, and vice versa