You can run a wide variety of applications in Kubernetes Engine, including compute-intensive applications such as machine learning (ML), image processing and financial modeling. By packaging your workloads into containers, you can benefit from the massive processing power of Kubernetes Engine’s hardware accelerators whenever you need, without having to manage hardware or even VMs. This talk will explore the variety of options that are currently supported by Kubernetes Engine, along with their performance characteristics based on commonly used ML models.
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