Streamlining Model Lifecycle with KubeRay
This blog is based on my work at CloudRaft!
Introduction
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.