CloudML Notebook

Provides a Jupyter Notebook Environment to Construct Data Analysis Model

Jupyter Notebook is a web-based interactive development environment for editing and running Python codes with ease. Running source code line by line enables users to check the results for data analysis and visualization quickly, making it a useful development tool for Python-based AI development. Users can easily install and use CloudML Notebook using a web-based console on Samsung Cloud Platform.

Overview

  • Easy Data Analysis

    Configuring a Jupyter Notebook environment that a professional data analysts and AI service developers are used to using is easy. Jupyter Notebook visualizes the code execution results in graph, and put together Python code execution results and parameters in a single file for easy management.

  • Easy Setup of Personal Analysis Environment

    Data analysts can configure a personal analysis environment within Jupyter Notebook. By selecting a kernel image available in custom mode or each project, they can easily configure a personal analysis environment or reconfigure by changing the environment.

  • Seamless Collaboration

    By integrating SCP Container Registry by cluster type, a shared analysis environment is configured immediately, allowing for seamless collaboration.

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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

  • Provide environment for model development and learning

    - Jupyter Notebook-based model development environment
    - Provide an option to select image by Notebook instance type (Including one built-in image)
    - Provide custom image through Pip install

  • Provide customized notebook environment for each use case

    - Parameter tuning function for each model
    - Easy allocation/modification of computing resources
    - Integration with other CloudML services
    - Seamless collaboration with team members using sharable link

Pricing

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