Deploy and Manage ML Models with KServe on Kubernetes

A Guide to Serving AI Models on Kubernetes Using KServe.

Introduction

In the rapidly evolving landscape of machine learning (ML) and artificial intelligence (AI), the challenge of deploying, scaling, and managing models in production has grown significantly. Kubernetes, with its powerful orchestration capabilities, has emerged as the platform of choice for deploying scalable and reliable ML workloads. KServe (formerly known as KFServing) is a Kubernetes-based platform that simplifies the deployment of ML models by providing a standardized and extensible solution for serving predictive and generative AI models.

KServe abstracts the complexities of model serving, enabling data scientists and engineers to focus on developing models rather than worrying about the intricacies of deployment. In this article, we’ll explore the core features of KServe, demonstrate its usage with a practical example, and discuss its benefits in production environments.

Why KServe?

KServe is designed to address the specific challenges associated with serving machine learning models in production environments. Here are some of the key features that make KServe an ideal choice for model serving: