Overview

Service Overview

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

Features

• Cloud ML is designed to allow users of various roles such as analysts, machine learning engineers, and developers to collaborate in one environment, and to easily design and operate machine learning workflows.
• Cloud ML provides an analysis environment based on Python and R, and users with programming experience can utilize the platform more flexibly and effectively. In particular, using the Copilot function based on generative AI, you can easily perform code writing, refactoring, error correction, and function recommendations with just natural language input, thereby increasing analysis productivity and analysis accessibility.
• Cloud ML supports each stage of analysis, including environment configuration, model development and serving, analysis automation, and visualization, in a systematic way. It also supports improving both productivity and model quality by automating repetitive experiments and operations.

Service Composition Diagram

CloudML consists of analysis environment, machine learning lifecycle management, automated analysis support, visualization, and generative AI-based Copilot function, and users can perform the entire machine learning process integrally through these components.

Provided Features

CloudML provides the following features.

• Visual Modeling: Provides an intuitive interface to build and deploy machine learning models without coding using a Drag&Drop method. You can easily manage all processes from data import to model evaluation and deployment.
• Code-based development: You can freely write and execute code using Python, R, etc. in the Jupyter Notebook environment. 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.
• Copilot Feature Utilization: Provides natural language-based AI assistant functionality to guide and automate the model development process. It supports various tasks such as code generation, refactoring, error correction, and explanation, thereby improving productivity.
• Integrated Platform: All features are integrated within CloudML, making it convenient to use.
• Scalability and Flexibility: Supports expansion of computing resources and connection to various data sources as needed.

Constraints

Before using CloudML, please check the following restrictions and reflect them in your service usage plan. Cloud ML operates in a Kubernetes-based environment, so proper cluster resource settings are required for stable service operation.

• Application basic resources: For Application operation, a minimum of vCPU 24 cores and 96GB of memory are assigned by default.
• Analysis Job Resources: In addition to the basic resources, analysis jobs require additional CPU or GPU resources to be set. These resources should be set appropriately considering the workload of the analysis job.
• Copilot (CPU-based usage): To run Copilot on CPU resources, a minimum of 16-core vCPU and 10GBi 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 used by setting up dedicated GPU resources.
• Supported LLM models: Currently, the LLM models applicable to Copilot are limited to Llama3.

Region-based provision status

CloudML is available in the following environments.

Preceding Service

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