Running ML projects on Kubernetes

For building reliable and scalable ML systems, you need a rock-solid base. Kubernetes provides the foundation for building scalable and reliable distributed systems along with the self-service capabilities that are required by our platform. The capability of Kubernetes to abstract the hardware infrastructure and consume it as a single unit is of great benefit to our platform.

Another key component is the ability of Kubernetes-based software to run anywhere, from small on-premises data centers to large hyperscalers (Amazon Web Services (AWS), Google Cloud Platform (GCP), Azure). This capability will give you the portability to run your ML platform anywhere you want. The consistency it brings to the consumer of your platform is brilliant as the team can experiment with extremely low initial costs on the cloud and then customize the platform for a wider audience in your enterprise.

The third and final reason to opt for Kubernetes is its capability...