K

KServe

Open Source
Deployment and Serving Updated Feb 15, 2026
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Overview

KServe is a Kubernetes Custom Resource Definition for serving predictive and generative machine learning models. It provides a simple and standardized way to deploy and manage ML models in a Kubernetes environment. KServe supports multiple frameworks and libraries, including TensorFlow, PyTorch, and scikit-learn.

Problem It Solves

Simplifying the deployment and management of machine learning models in a Kubernetes environment

Target Audience: Machine learning engineers and data scientists

Inputs

  • • Trained machine learning models
  • • Model configuration files
  • • Data for prediction

Outputs

  • • Predictions
  • • Model performance metrics
  • • Deployment logs

Example Workflow

  1. 1 Model training
  2. 2 Model packaging
  3. 3 Model deployment
  4. 4 Model serving
  5. 5 Model monitoring
  6. 6 Model updating

Sample System Prompt 


              Deploy a trained TensorFlow model to a Kubernetes cluster using KServe

Tools & Technologies

Kubernetes Docker TensorFlow PyTorch scikit-learn

Alternatives

  • • TensorFlow Serving
  • • AWS SageMaker
  • • Azure Machine Learning

FAQs

Is this agent open-source?
Yes
Can this agent be self-hosted?
Yes
What skill level is required?
Advanced