Overview

Service Overview

CloudML is an integrated platform that supports the entire machine learning process—from data analysis to model development, training, validation, and deployment—in a cloud environment.

Features

• Cloud ML is designed to enable users in various roles such as analysts, machine learning engineers, and developers to collaborate in a single environment and easily design and operate machine learning workflows.
• Cloud ML provides an analysis environment based on Python and R, and users with programming experience can leverage the platform more flexibly and effectively. In particular, using the generative AI–based Copilot feature allows code writing, refactoring, error correction, and function recommendation to be performed easily with natural language input, thereby increasing analytical productivity and accessibility.
• Cloud ML systematically supports each stage, including configuring the analysis environment, model development and serving, analysis automation, and visualization. It enables improvements in both productivity and model quality through repetitive experiments and operational automation.

Service Architecture Diagram

CloudML consists of an analysis environment, machine learning lifecycle management, automated analysis support, visualization, and a generative AI‑based Copilot feature, allowing users to perform the entire machine‑learning process in an integrated manner.

Provided features

CloudML provides the following features.

• Visual Modeling: Provides an intuitive interface that lets you build and deploy machine learning models without coding using a Drag&Drop approach. You can easily manage the entire process from data loading to model evaluation and deployment.
• Code-based Development: In the Jupyter Notebook environment, you can freely write and execute code using Python, R, and others. It provides powerful features for advanced users and researchers.
• Workflow Automation: It efficiently automates complex machine learning workflows such as data preprocessing, model training, evaluation, and deployment.
• Experiment Management: You can train machine learning models with various parameter combinations and systematically manage and compare the results.
• Using Copilot Features: It provides a natural-language-based AI assistant that guides and automates the model development process. It supports various tasks such as code generation, refactoring, error correction, and documentation, enhancing productivity.
• Integrated Platform: All features are integrated within CloudML for convenient use.
• Scalability and Flexibility: Supports scaling computing resources and connecting various data sources as needed.

Constraints

Before using CloudML, be sure to check the following constraints and incorporate them into your service usage plan. Since Cloud ML operates in a Kubernetes-based environment, appropriate cluster resource configuration is required for stable service operation.

• Application Basic Resources: To run the Application, a minimum of 24 vCPU cores and 96 GBi of memory are allocated by default.
• Analysis Task Resources: To perform analysis tasks, additional CPU or GPU resource configuration is required beyond the basic resources above. It should be configured appropriately, taking the workload of the analysis tasks into account.
• Copilot (CPU-based usage): To run Copilot on CPU resources, a minimum of 16 vCPU cores and 10 GiB of memory are required. In this case, the CPU resources available for analysis tasks are reduced accordingly.
• Copilot (GPU-based usage): Copilot can also be configured to use dedicated GPU resources.
• Supported LLM models: Currently, the LLM models that can be applied to Copilot are limited to Llama3.

Provision status by region

CloudML is available in the following environments.

Preliminary Service

This is a list of services that must be pre-configured before creating the service. Please refer to the guide provided for each service for details and prepare in advance.