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Designed for AI applications, this system is built on the MLOps architecture, integrating a no-code model training interface, system resource management, and model lifecycle management. Users can train, deploy, and manage AI models effortlessly—without programming skills.
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AIDMS eliminates technical barriers through a user-friendly no-code platform, offering a rich set of AI models and development tools. This allows professionals from various fields to create AI applications easily. In addition, it also has cross-departmental simultaneous collaboration functions.
By integrating the latest models in real time, AIDMS enhances AI recognition accuracy, maximizing AI application benefits.
AIDMS provides comprehensive cloud and on-premises deployment solutions. It fully supports mainstream operating systems and programming languages, enabling smooth AI integration into smart devices, automation equipment, and more.
As a trusted partner of NVIDIA, Leadtek offers high-performance NVIDIA GPUs and certified systems. When paired with AIDMS, resource allocation is optimized, ensuring maximum return on investment.
AIDMS automatically generates labeled data files, allowing manual adjustments as needed. It also supports the integration of generative AI, LLM, RAG, and more, enabling flexible AI service expansion.
| Platform |  AIDMS Private Server Platform |
Other Brand A Cloud Service |
Other Brand B Standalone Software |
|---|---|---|---|
| Model Selection | No-code, with a variety of models available for selection, allowing for quick switching, testing, and validation of different AI models. Save time and effort by skipping intricate setup and lengthy adjustment processes. | Requires coding. | No-code, each model is standalone. |
| Model Deployment | Flexible and unrestricted cloud and on-premises deployment. | Relatively cumbersome. | Deployment incurs additional costs. |
| Model Version Control | Organized and traceable development process, making project progression more structured. | Self-managed. | X |
| Model Development | Open models, users can build their own models, allowing system architecture and functions to better meet their needs. | Self-developed. | X |
| Overall AI Environment Setup & Usage Cost | Low | High | Medium |
| Hardware Resource Allocation & Management | Allocates CPU, GPU, and RAM resources based on application needs and number of users. | X | X |
| Target Users | Anyone | AI Engineers | Software Engineers |
| Type | Anomaly Detection Classification Object Detection Instance Segmentation Semantic Segmentation OCR |
| Model | FastFLow VGG RestNet Inception V3 DensNet MobileNet V3 EfficientNet V2 Vision-Transformer Swin-Transformer DAMO-YOLO YOLO V7 YOLO V9 YOLO V10 YOLO V11 U-Net DeepLab V3+ BiSeNet V2 TrOCR |
Edge
Server
Robotic
Automation
Mobile
Embedded
Real-time collaboration, enhancing team efficiency
Team members from different departments can join AI development in their own workspaces
Covering AI Workstations and AI Servers, equipped with powerful NVIDIA RTX GPUs, featuring flexible system configurations and professional services to meet the needs of various industries and businesses of all sizes.
Learn more about NVIDIA-Certified Systems specifications here.
Future
