As the demand for ML and LLM models continues to grow, so does the need for reliability and scalability. Integrating Kubernetes into model development emerges as a powerful solution. By leveraging Kubernetes, we can streamline the process of model development, decrease costs, and enhance model reliability. This can be achieved using Ray on Kubernetes. But before that, let’s take a look at the lifecycle of an ML model.
This blog is based on my work at CloudRaft! (My first blog at CloudRaft)
Kubernetes marks its 10th anniversary this year with the release of version v1.30.0, solidifying its status as the cloud platform of choice. Managed Kubernetes clusters like EKS, GKS and AKS represent 73% of the total cluster, remaining 27% are self-managed as per Dynatrace. The last decade has been an era of public cloud but due to increasing costs, some businesses are trying to find a balance with the hybrid cloud. Approximately 76% of organizations now leverage multiple clouds which is a combination of public and private clouds as per VMware. Kubernetes allows us to build a multi-cloud and private cloud layer on the hardware of choice and in a cost-effective way without committing to one specific cloud.