Accelerate innovation in the enterprise with distributed ML and DL (sponsored by BlueData)

How do you accelerate innovation and deliver faster time to value for your AI initiative while ensuring enterprise-grade security and high performance? How do you provide easy access to the tools and data your data science teams need for large-scale distributed ML/DL with greater agility for rapid prototyping and iteration?

Nanda Vijaydev shares practical examples of—and lessons learned from—ML/DL use cases in financial services, healthcare, and other industries. You’ll learn how to quickly deploy containerized multinode environments for TensorFlow and other ML/DL tools in a multitenant architecture either on-premises, in the cloud, or in a hybrid environment.