中文CyberAI Platform
One-stop large model development, management and deployment, support multi-framework transformation and cloud-side collaborative deployment, build an AI marketplace to achieve efficient and comprehensive management of models, algorithms, data and other assets, and ensure stable and reliable operation of the system through real-time monitoring of prediction performance and resource occupation.
Support rapid connection with the data platform to achieve data + AI integration and deep digital intelligence integration. Provide a unified operation interface, characteristic data and workflow, through the hierarchical design of the base layer, integration layer, interface layer and platform layer, unidirectional integration to ensure architectural stability and flexibility.
Provide a secure algorithm sandbox environment for computing and storage, and combine the security strategy of opening up enterprise data to the outside world. On the premise of ensuring that data assets are secure and controllable, they can be shared openly with partners, building a closed loop, and enabling the opening up of data assets and maximizing their value.
Efficient computing power resource management and scheduling to improve the utilization of computing power resources. Adopt resource pool management and vGPU technology to significantly improve computing power utilization, optimize task execution through intelligent scheduling, unify monitoring and heterogeneous management, reduce operation and maintenance costs, and improve the overall efficiency and performance of the platform.
Has the ability to fine-tune the deployment of models with low threshold and high efficiency. Through visualization tools, you can quickly complete model fine-tuning, complete data management, version control and model publishing functions without the need for professional skills, meet diverse needs, and easily implement model deployment and application.
It supports data types of multi-modal data such as images, text, and audio, and provides access to 20 + data sources such as MySQL, Oracle, MongoDB, and TDengine.
Provide comprehensive computing power resource management, support GPU, VGPU efficient configuration and monitoring, through advanced GPU virtualization technology, combined with DeepSpeed and other deep learning frameworks, to achieve multi-machine multi-card parallel computing, significantly improve the efficiency of computing resources, optimize model training and inference performance.
Support visual modeling in the form of canvas drag and drop, encapsulate data reading, data preprocessing, feature engineering, statistical analysis, machine learning, deep learning, model evaluation and other 100 + operators, interactive Notebook modeling. The model file is easy to deploy and support RESTful API calls to help users achieve no-code modeling.
Provide convenient and efficient large model training solutions to support automated model inference, online service, and resource scheduling optimization. A variety of functional modules such as package model loading, performance tuning, distributed deployment, and elastic expansion help users quickly achieve production-level deployment and management of large-scale artificial intelligence models.
Supports conversational systems (Chat), retrieval-enhanced generation (rag), intelligent agents (Agent), and workflow orchestration (Workflow) of large models to orderly configure various application modules, improve the efficiency and flexibility of business processes, and promote the development of intelligent solutions in actual business.
The system integrates multiple data sources to efficiently index and access information in the knowledge base through information retrieval techniques. When Q&A or content generation is performed, knowledge can be retrieved dynamically based on context, while supporting knowledge base updates, version control, and quality assessment to ensure the accuracy, real-time, and reliability of the knowledge base.
It supports data types of multi-modal data such as images, text, and audio, and provides access to 20 + data sources such as MySQL, Oracle, MongoDB, and TDengine.
Provide comprehensive computing power resource management, support GPU, VGPU efficient configuration and monitoring, through advanced GPU virtualization technology, combined with DeepSpeed and other deep learning frameworks, to achieve multi-machine multi-card parallel computing, significantly improve the efficiency of computing resources, optimize model training and inference performance.
Support visual modeling in the form of canvas drag and drop, encapsulate data reading, data preprocessing, feature engineering, statistical analysis, machine learning, deep learning, model evaluation and other 100 + operators, interactive Notebook modeling. The model file is easy to deploy and support RESTful API calls to help users achieve no-code modeling.
Provide convenient and efficient large model training solutions to support automated model inference, online service, and resource scheduling optimization. A variety of functional modules such as package model loading, performance tuning, distributed deployment, and elastic expansion help users quickly achieve production-level deployment and management of large-scale artificial intelligence models.
Supports conversational systems (Chat), retrieval-enhanced generation (rag), intelligent agents (Agent), and workflow orchestration (Workflow) of large models to orderly configure various application modules, improve the efficiency and flexibility of business processes, and promote the development of intelligent solutions in actual business.
The system integrates multiple data sources to efficiently index and access information in the knowledge base through information retrieval techniques. When Q&A or content generation is performed, knowledge can be retrieved dynamically based on context, while supporting knowledge base updates, version control, and quality assessment to ensure the accuracy, real-time, and reliability of the knowledge base.
CyberAI intelligent analysis assistant, users do not need to master complex SQL syntax, only need to ask questions in natural language, the system intelligently parses and executes accurate queries, and returns the data results required by the business. Through database DDL import and simple check configuration, supplemented by text instructions, data table settings are completed in an instant to accelerate data insights and help business decision analysis.
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