Accelerate innovation with DevOps-like agility for machine learning pipelines

You need to be able to accelerate innovation and deliver faster time to value for your AI initiative while ensuring enterprise grade security and high performance. You need to provide easy access to the tools and data your data science teams need for large-scale distributed ML and DL with greater agility for rapid prototyping, iteration, and deployment.

Nanda Vijaydev dives into examples of—and lessons from—ML and DL use cases in financial services, healthcare, and other industries. You’ll learn how to quickly spin up containerized multinode environments for TensorFlow and other ML and DL tools—to train models in a multitenant architecture on-premises, in the cloud, or in a hybrid environment. She also explores the challenges and best practices regarding ML operationalization and model deployment in production.