3 Important AI/ML Tools You Can Deploy on Kubernetes

Infrastructure technology has evolved rapidly, with cloud native applications now able to run stateful workloads efficiently on Kubernetes. This has led to the integration of streaming workloads and analytics into the mainstream in Kubernetes ecosystem. The industry is now focusing on AI/ML workloads due to the need for faster and more agile MLOps to support real-time artificial intelligence (AI). Key projects like Feast, a feature store running in Kubernetes, and KServe, an API endpoint for deploying machine learning models, are simplifying the process of building and maintaining ML models. Additionally, vector similarity search (VSS) tools such as Weaviate and Milvus are enhancing data retrieval capabilities. Deploying AI/ML workloads in Kubernetes can lead to increased productivity and reduced costs for organizations.