Kubernetes-based Machine Learning Platform

Kubeflow automates repetitive work in the overall pipeline for development, learning, and deployment processes of the machine learning model. MLOps1) environments based on the machine learning platform are provided, offering integrated management of learning data and models as well as operational data.
1) MLOps : A ML engineering discipline that aims to unify machine learning development (Dev) and machine learning system operation (Ops)




Service Architecture

  1. User
  2. Console
  3. Kubernetes Engine : CPU Worker Nodes, GPU Worker Nodes, Persistent Volume
  1. Data Scientist, MLOps Engineer
  2. Kubeflow : Pipeline, Meta data, Model Serving, Notebook, Hyper Parameter Tuning ...
  3. Jupyter Notebook → Model Development → Model Training → Hyper Para.Tuning → Model Serving → Inference Application.

Key Features

  • Kubeflow service creation

    - Request : Automatic deployment and service configuration for the requested Kubernetes clusters
    - View : Offering list, Kubeflow version and resource status
    - Delete : Delete created Kubeflow modules

  • Kubeflow service features

    - Basic features
      · Jupyter Notebook (model development, learning, and inference)
      · Workflow automation (based on machine learning pipelines)
    - Additional features of Samsung Cloud Platform
      · Manage GPU job scheduling and job queue
      · Conduct GPU resource monitoring and GPU fraction
      · Provide Kubeflow engine monitoring/logging and distributed learning job execution/monitoring
      · Build and manage ML framework images (TensorFlow, PyTorch, etc.) and ML images
      · Manage/analyze inference services and manage model experiments/learning nodes
      · Work with user authentication of Samsung Cloud Platform and manage users/projects/announcements


    • Offering
    • SCP Kubeflow SW packaging
    • Billing
    • Charged by the hour for the scale and usage of deployed Kubeflow
      ※ Samsung Cloud Platform for user environment configuration charged additionally
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