Machine Learning: Google Is Now Offering "GPU's as a Service" (GOOG; NVDA)

From Google's Cloud Platform blog:

GPUs as a service with Kubernetes Engine are now generally available 

[Editor's note: This is one of many posts on enterprise features enabled by Kubernetes Engine 1.10. For the full coverage, follow along here.]
Today, we’re excited to announce the general availability of GPUs in Google Kubernetes Engine, which have become one of the platform’s fastest growing features since they entered beta earlier this year, with core-hours soaring by 10X since the end of 2017. Together with the GA of Kubernetes Engine 1.10, GPUs make Kubernetes Engine a great fit for enterprise machine learning (ML) workloads. By using GPUs in Kubernetes Engine for your CUDA workloads, you benefit from the massive processing power of GPUs whenever you need, without having to manage hardware or even VMs.

We recently introduced the latest and the fastest NVIDIA Tesla V100 to the portfolio, and the P100 is generally available. Last but not least, we also offer the entry-level K80, which is largely responsible for the popularity of GPUs. All our GPU models are available as Preemptible GPUs, as a way to reduce costs while benefiting from GPUs in Google Cloud. Check out the latest prices for GPUs here. As the growth in GPU core-hours indicates, our users are excited about GPUs in Kubernetes Engine. Ocado, the world’s largest online-only grocery retailer, is always looking to apply state-of-the-art machine learning models for Ocado.com customers and Ocado Smart Platform retail partners, and runs the models on preemptible, GPU-accelerated instances on Kubernetes Engine.

“GPU-attached nodes combined with Kubernetes provide a powerful, cost-effective and flexible environment for enterprise-grade machine learning. Ocado chose Kubernetes for its scalability, portability, strong ecosystem and huge community support. It’s lighter, more flexible and easier to maintain compared to a cluster of traditional VMs. It also has great ease-of-use and the ability to attach hardware accelerators such as GPUs and TPUs, providing a huge boost over traditional CPUs.” — Martin Nikolov, Research Software Engineer, Ocado

 ...MUCH MORE

See also the "Getting Started with Google Kubernetes Engine" at Coursera if you want to start your DIY ML co.

As Wikipedia says about Kubernetes:

The original codename for Kubernetes within Google was Project Seven, a reference to Star Trek character Seven of Nine that is a 'friendlier' Borg.^[9] The seven spokes on the wheel of the Kubernetes logo is a nod to that codename.