Architecture Design of Intelligent Education and Teaching System Based on Machine Learning Algorithm

With the great success of machine learning (ML) algorithms represented by deep learning in vision, speech recognition and other fields, while looking forward to and promoting the large-scale educational application of artificial intelligence (AI), data center network is an important infrastructure supporting big data and cloud computing platforms, which is widely used in data-intensive and large-scale modular parallel computing tasks. AI has entered an unprecedented period of rapid adoption and stability. At the beginning of architecture design, the traditional distributed big data processing system didn't optimize the ML task, but ensured the training convergence efficiency, improved the iterative computing speed and improved the model quality. The application of AI education is limited by the common sense and symbolic grounding and transfer of AI technology. Based on the above analysis, this paper aims to solve the efficiency bottleneck of distributed system in dealing with ML tasks, and design and implement a high-performance distributed ML system for heterogeneous environment in data centers, so as to promote the intelligent development of education and teaching by applying heuristic AI teaching ideas. Overall, the degree of automation that AI technology can achieve in education is still very limited. It is more realistic to strengthen teacher design than to replace it.