CloudML Studio

Provides IDE Workflow for Building and Learning Analysis Models

CloudML Studio is a service that visualizes the analytics environment and delivers it in workflow-style, enabling easy AI/ML analytics during AI model development. Using drag & drop, AI/ML analytics workflow is automatically configured.
An at-a-glance view of input/output data and applied algorithms also make AI/ML analytics accessible for cloud experts and non-experts alike.

Overview

  • Enhanced Analytical Productivity

    CloudML Studio provides an AI/ML analytics tool that can manage AI/ML lifecycle with low-code or no-code. By visualizing the entire analytics process, from data collection, storage, processing to analysis, modeling, deployment and monitoring, data analysis process becomes faster and more simple.

  • Automatic Analytical Modeling

    By suggesting algorithms optimal for the analytics environment/data and parameters for each AI model, CloudML Studio performs effective data analysis. In addition, more than 20 AutoML functions are available to optimize and automate analytics modeling.

  • Streamlined Collaboration

    By editing and executing workflows collaboratively, reviewing the results as well as the collaboration process in general become easier. Monitoring the execution history of CloudML Studio is easier as all execution history is stored.

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Service Architecture

    SCP CloudML
  • CloudML Notebook: Model development and learning, Parameter tuning
  • CloudML Studio: Workflow development, Reporting and analytics App development
  • CloudML Pipeline: Allocate resource by component, Manage model lifecycle, Execute pipeline
  • CloudML Experiments: Save and manage experiment data, Model Registry, Model Verification
  • Kubernetes Engine
  • Data Scientist → ML model development/tuning, learning, inference analysis → SCP CloudML(CloudML Notebook) ← Utilize Kubernetes Service → Kubernetes Service (DNS, VPC, Virtual Server, NAS, Security Group, Cloud Monitoring)
  • Data Scientist → ML model development/tuning, learning, inference analysis → SCP CloudML(CloudML Studio) ← Integrate Container Image Registry → Container Image Registry
  • Data Scientist → ML model development/tuning, learning, inference analysis → SCP CloudML(CloudML Pipeline) ← Integrate user authentication → IDP(Identity Provider)*
  • MLOps Engineer → ML model development/tuning, learning, inference analysis → SCP CloudML(CloudML Experiments) ← Integrate DevOps Tool → DevOps Tool* (Nexus, GitHub)
  • MLOps Engineer → ML model development/tuning, learning, inference analysis → SCP CloudML(Kubernetes Engine) ← Save/utilize data set and model → Object Storage*
* To be provided

Key Features

  • Visualize analytics workflow

    - Provide detailed workflow connection status in visualization BI form (Report)
    - Provide analytics app-based workflow simulation

  • Intuitive visualization of input/output data

    - Visualize input data for easy verification of data by field
    - Utilize graph to visualize output data that is easy to understand
    - Support immediate execution of analysis function

  • Provide AutoML function

    - Data cleansing, feature selection, decision tree, regression model, etc.
    - Low code-based workflow modeler

Pricing

    • Billing
    • Container pod usage time of Kubernetes Engine occupied by CloudML Studio (vCore/hr)
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