Machine learning and artificial intelligence are the hottest buzzwords in today’s tech landscape. Promising highly capable and robust data processing capabilities, there’s good reason to leverage machine learning across today’s business ecosystem. The questions then arise: how do we access and integrate these technologies, and where do they run?

The answer is Google Kubernetes Engine, also known as GKE. GKE is the premier managed services platform for hosting large-scale machine learning workloads, integrating seamlessly with the Google Cloud Platform suite of offerings including BigQuery, Dataflow, and Google’s custom custom-developed application-specific integrated circuits, Tensor Processing Units (TPUs).

Built on Kubernetes, scale comes naturally with GKE. Automatic scaling based on numerous criteria is a core function of GKE, allowing the most complex ML tasks and the largest datasets to be processed and executed without manual intervention. GKE scales up when needed to up to 15,000 nodes – more than 3x the nodes of leading competitors – and scales down with equal alacrity – keeping costs in line while tackling the most challenging machine learning jobs.

In addition to being the best in class under the hood, GKE also provides a highly portable platform for running ML workloads with built-in container isolation. By abstracting away the details of creating and managing infrastructure your business can focus on what matters most, the lifecycle of the software applications and products that are the backbone of your business success. Isolation of containers within Kubernetes ensures that ML workloads are run independently and without interference resulting in a higher level of data integrity and confidence.