AI&MLOps Platform

Kubernetes-based Machine Learning Platform

AI&MLOps Platform 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. AI&MLOps Platform : 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

  • Basic function (Both Mini and Enterprise)

    - Create AI platform (auto-deployment/configuration), view (platform version, resource status), and delete
    - Provide Jupyter Notebook : Model development, learning, inference
    - Automate machine learning pipeline workflow
    - Provide other open source Kubeflow default feature

  • Additional feature (Enterprise)

    - Advanced AI/ML platform dashboard
    - AI/ML notebook server : Base image, user-defined image
    - AI/ML job : Job creation, template, archive, scheduling, execution, monitoring
      ※ Support GPU resource monitoring, GPU fraction
      ※ Providing job operator for Large Language Model training (DeepSpeed)
    - Build and manage user image
    - AI JumpStarter and ETM (Experiment Tracking Management)
    - Serving : Dashboard, register/manage model, inference, predictions visualization
    - Managing platform resource : Manage resource usage by project, monitor resource usage
    - Manage project user/permissions, admin feature, adjust platform configuration


    • Offering
    • SW packaging for configuring AI&MLOps environments
    • Billing
    • Charged by the hour for the scale and usage of deployed AI&MLOps Platform
      ※ Samsung Cloud Platform for user environment configuration charged additionally
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