CyberAl is a one-stop AI platform for developers and enterprises. It provides interactive visual modeling, compute resource management, large-scale model training and deployment, and AI application development. The platform supports efficient data processing and model training, and optimizes computing costs through elastic resource scheduling. This ensures users achieve superior performance and reliable technical support in complex AI application development.
Support one-stop machine learning platform capability, once the training dataset is prepared, all modeling work (including data access, data preprocessing, feature analysis, model training, model evaluation, and model publishing) can be achieved through the platform.
Encapsulate over 100 visual modeling operators, supporting no-code machine learning and deep learning, thereby reducing the barrier to using the product.
Support common programming languages and multiple data processing methods, as well as model deployment, to meet enterprise customization needs.
Support more than 5 types of databases, including structured and unstructured, provide access to 20+ types of data sources, and allow flexible switching between different data sources
Support mainstream deep learning frameworks and add GPU computing power support to facilitate the training and practical application of massive data and complex models.
Support multiple data types including images, text, and audio, and provide integration with over 20 data sources, including MySQL, Oracle, MongoDB, and TDengine.
Support multiple data types including images, text, and audio, and provide integration with over 20 data sources, including MySQL, Oracle, MongoDB, and TDengine.
Support visual modeling through a drag-and-drop canvas, encapsulating over 100 operators for data reading, data preprocessing, feature engineering, statistical analysis, machine learning, deep learning, and model evaluation, to help users achieve codeless modeling.
Support visual modeling through a drag-and-drop canvas, encapsulating over 100 operators for data reading, data preprocessing, feature engineering, statistical analysis, machine learning, deep learning, and model evaluation, to help users achieve codeless modeling.
Provide interactive Notebook-based programmatic modeling, supporting multiple languages such as Python, Spark, R, C, C++, and SQL for data analysis and machine learning modeling.
Provide interactive Notebook-based programmatic modeling, supporting multiple languages such as Python, Spark, R, C, C++, and SQL for data analysis and machine learning modeling.
:Support online deployment and invocation of model file types such as PMML, pickle, and pth, and support RESTful API calls.
:Support online deployment and invocation of model file types such as PMML, pickle, and pth, and support RESTful API calls.
Support multiple data types including images, text, and audio, and provide integration with over 20 data sources, including MySQL, Oracle, MongoDB, and TDengine.
Support visual modeling through a drag-and-drop canvas, encapsulating over 100 operators for data reading, data preprocessing, feature engineering, statistical analysis, machine learning, deep learning, and model evaluation, to help users achieve codeless modeling.
Provide interactive Notebook-based programmatic modeling, supporting multiple languages such as Python, Spark, R, C, C++, and SQL for data analysis and machine learning modeling.
:Support online deployment and invocation of model file types such as PMML, pickle, and pth, and support RESTful API calls.
CyberAI can help users build a secure and reliable AI teaching and training platform. The platform includes a wealth of courses and case resources, allowing data processing and visual analysis through drag-and-drop and low-code methods. It provides an experimental environment for AI modeling for both teachers and students to meet the diverse needs of universities, including internal and external course design, practical training, and research exercises.
This artificial intelligence platform builds an integrated teaching and training environment for the university, optimizing the allocation and utilization of educational resources. Teachers can create courses on the platform, configure course content, datasets, and modeling environments, and manage permissions based on student rosters. After logging in, students can create experiments and perform modeling operations according to the courses they are enrolled in, completing assigned learning tasks and submitting assignments. This approach ensures personalized and flexible teaching activities, while enhancing learning outcomes and management efficiency.
This artificial intelligence platform builds an integrated teaching and training environment for the university, optimizing the allocation and utilization of educational resources. Teachers can create courses on the platform, configure course content, datasets, and modeling environments, and manage permissions based on student rosters. After logging in, students can create experiments and perform modeling operations according to the courses they are enrolled in, completing assigned learning tasks and submitting assignments. This approach ensures personalized and flexible teaching activities, while enhancing learning outcomes and management efficiency.
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