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Introduction

Updated Mar 18, 2025 ·

MLOps on Kubernetes​

Kubernetes supports MLOps with its scalable and flexible design.

  • Isolated environments

    • Uses Pods and Storage for experiments.
    • Keeps ML workflows separate and organized.

  • Model deployment & monitoring

    • Tracks models through Pod lifecycles.
    • Manages updates with container images.

  • Collaborative workflows

    • Enables teams to improve models together.
    • Automates tasks with Kubernetes-based pipelines.

Popular frameworks:

  • MLflow – Tracks and manages ML experiments.
  • Kubeflow – Simplifies ML deployment on Kubernetes.

Kubeflow​

Kubeflow makes running ML models on Kubernetes easier.

  • Supports the full ML lifecycle

    • Handles data processing, training, and deployment.
    • Automates model testing and scaling.

  • Flexible components

    • Allows custom workflows for different needs.
    • Works with various ML tools and frameworks.

  • Python integration

    • Interacts with Kubernetes without kubectl.
    • Simplifies automation and scripting.