Model Inference
Flexible Model Inference Solutions for AI-Powered Applications
The model inference solution provides customers with comprehensive, flexible and efficient model inference services, covering application modes such as AI computing cloud, AI intelligent computing platform, hardware and software integrated machine and edge node. For example, in the field of natural language processing, large language models can be used for tasks such as machine translation, sentiment analysis, and text generation, thereby improving the accuracy of translation, the accuracy of sentiment analysis, and the quality of text generation. In the field of computer vision, large-scale image recognition models can be used for tasks such as image classification, target detection, and image generation, thereby improving the accuracy of recognition, the accuracy of detection, and the quality of generation.
Capabilities
One-click model deployment
Integrates industry-leading models with a variety of large model options such as ChatGLM, Baichuan, and LLaMA. Supports one-click deployment and custom model uploading for seamless integration of AI services into business workflows.
Multiple computing resources support
Provides diverse computing resources including NVIDIA GPU, Ascend GPU, Haiguang DCU, and CPU to meet varying performance needs. Supports flexible resource scheduling for optimal utilization.
Containerization technology support
Encapsulates model applications using containerization to simplify deployment and operation. Enhances system security, data protection, and prevents cross-contamination.
AI framework compatibility and cluster management
Compatible with mainstream AI frameworks like PyTorch, TensorFlow, and PaddlePaddle. Offers efficient cluster management with minute-level reasoning cluster creation and elastic scaling based on load demands.
Seamless AI Integration for Accelerated Business Intelligence
This solution integrates the world`s leading AI technology and extensive computing resources to help users easily implement the entire process from model access to efficient deployment, accelerate the implementation of AI applications, and improve the level of business intelligence.
Challenges
Uneven distribution of resources
In a multi-machine, multi-card environment and complex corporate architecture, how to effectively allocate GPU resources to ensure the rapid execution of high-priority tasks is a major challenge in model training.
Complex operation and maintenance management
As the complexity of AI models increases, the complexity of operation and maintenance management also increases, and intelligent tools are needed to simplify the management process.
Slow fault recovery
GPU cluster failures are much more frequent than traditional clusters. How can we reduce failure recovery time to minimize the impact on training tasks?
Difficulty in cost control
The cost of intelligent computing resources continues to rise. How to control costs while ensuring training efficiency is an important issue facing enterprises.
Advantages
Improve Business Efficiency
Efficient model deployment and management can significantly shorten the model launch cycle and enhance business response speed and processing capabilities.
Technical Flexibility
Supports multiple deployment modes and AI frameworks, enabling adaptability to diverse scenarios and improving scalability.
Reduce Costs
Flexible resource scheduling and diversified computing power support help users control costs effectively and avoid resource waste.
Lower Technical Threshold
The simplified deployment process allows businesses and individuals to easily adopt AI technologies.
Driving AI Innovation
Custom model deployment features encourage users to create personalized AI applications, fostering innovation and integration into business.
Ensure Data Security
Enhanced data security and isolation mechanisms safeguard user data throughout the inference process.