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The Artificial Intelligence Teaching And Training System Can Support Users In Building Professional Artificial Intelligence Laboratories. Teacher Users Can Use The Artificial Intelligence Teaching And Training System To Organize Artificial Intelligence-related Experimental Courses, Carry Out Experimental Content, Evaluate Experimental Results, Analyze Experimental Quality, Etc.; Student Users Can Use The Artificial Intelligence Teaching And Training System To Study Artificial Intelligence-related Courses And Practice, So That They Can Gradually Master Artificial Intelligence-related Technical Knowledge And Application Knowledge Through Experiments. The Product Design Of The Artificial Intelligence Teaching And Training System Is Divided Into 5 Levels From Bottom To Top, Namely: User Interface, Teaching And Training Resources, Business Application Management, Virtualization Support Cloud Platform, And Hardware Cluster Platform. The Specific Contents Are As Follows:
Artificial Intelligence Teaching And Training System The Artificial Intelligence Teaching And Training System Adopts B/s Architecture. The Experiments Are Not Restricted By Space And The System Can Be Accessed To Conduct Experiments As Long As The Network Is Connected.
The Artificial Intelligence Teaching And Training System Includes Multiple Artificial Intelligence Courses And More Than 400 Experiments. The Machine Learning Course Mainly Includes The Learning Of Optimization Algorithms And Classification Regression Models, Which Gradually Progress From Basic Experiments To Complex Experiments To Better Master The Analysis And Tuning Of Machine Learning Algorithms In Actual Environments. The Artificial Intelligence Course Mainly Includes Three Major Directions Of Courses: Computer Vision, Natural Language Processing, And Speech Recognition. The System Includes Three Major Deep Learning Frameworks: Tensorflow, Pytorch, And Paddlepaddle. From Framework Construction And Compilation To Combining The Latest Neural Network Models To Solve Computer Vision And Natural Language Processing Problems.
• It Adopts A Distributed Storage Architecture And Provides A Multi-copy Data Protection Mechanism To Ensure The High Reliability Of The Customer’s Private Cloud Management Platform Data.
• The Solution Integrates The Local Large-capacity Hard Disks Of The Hyper-converged Computing Nodes To Build A Common Shared Storage Resource Pool For Storing General Business Data And Virtual Machine Files.
• Build A High-speed Shared Storage Resource Pool Through The Local Ssd Hard Disks Of The Hyper-converged Computing Nodes To Store Database Files.
Hybrid Virtualized Cloud Platform To Meet The Needs Of Multi-Scenario Architecture
Visualized Teaching Data Center To Assist In Diagnosis And Improvement Of Teaching Quality
Scientific And Systematic Experimental System, Good Support For Teaching
Supports Allocation Of Gpu Resources And Experimental Teaching:
Hybrid Virtualized Cloud Platform To Meet The Needs Of Multi-scenario Architecture
