From GPUs to MLOps: building a platform for multimodal AI applications

This webinar will feature:

1. Insights into the unique challenges and solutions for ML infrastructure in training multimodal foundation models. GPU requirements, integrating big data technologies, HPC, and CUDA for distributed training.
2. Tools and principles for a cloud-native ML platform, including infrastructure-as-code, containerization, and orchestration
3. How to identify and address gaps in ML workflows. Learn to balance the needs of data scientists, ML engineers, and data engineers while maintaining a robust, scalable, and secure ML infrastructure.

Audience - who should join?

Platform engineers, platform architects, DevOps engineers, site reliability engineers (SREs), infrastructure and operations, security engineers, enterprise and solution architects, application developers with an affinity for platform engineering and technical management focusing on improving DevEx and ops efficiency.