Serverless machine learning (ML) on Kubernetes presents an innovative solution for deploying ML models without managing servers. Utilizing frameworks like KServe, which supports various machine learning libraries, this approach offers features such as autoscaling and advanced processing capabilities. While Kubernetes is not inherently serverless, it can be adapted to achieve the desired scalability and flexibility for ML deployments. The guide outlines both the prospects and challenges of this technology, providing a practical roadmap for implementation in the future.
What if you could deploy machine learning (ML) models without wrestling with server management, all while harnessing the power of Kubernetes? It's a tantalizing idea - serverless ML on Kubernetes promises scalability, cost savings, and flexibility.
KServe is a game-changer. Designed for Kubernetes, it's a cloud-agnostic platform for serving ML models at scale. It brings: autoscaling, framework support, and advanced features including pre-processing and monitoring.
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