Accelerating ML development with optimized performance and cost

Successful machine learning deployments can depend on the ability of infrastructure to support the performance and budget requirements of a workload. Google’s open, flexible, and scalable AI infrastructure supports a wide variety of AI workloads, enabling customers to increase velocity to production, reduce costs, and to meet changing requirements over time. In this session, we discuss how to optimize across the AI stack with the latest GPUs and TPUs, fully-managed purpose-built AI infrastructure capabilities with Vertex AI, and state of the art solutions for demanding workloads. Hear about how Uber, Cohere, Credit Karma, Arbor Biotechnologies, and other enterprises are leveraging Google Cloud's AI Infrastructure to innovate, accelerate deployment, and drive business value.

Optimize training performance with Reduction Server on Vertex AI → https://goo.gle/3SE2VNX

Speakers: Mikhail Chrestkha, Kai Wang, Joanna Yoo, Siddhartha Kamalakara

Watch more:
All sessions from Google Cloud Next → https://goo.gle/next22-allsessions
Watch all the Modernize sessions → https://goo.gle/next22-modernize

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