Research And Implementation Of AI Model Training And Open Service Platform

The AI model training and open service platform is an easy-to-use one-stop AI application development tool for enterprises.By simplifying AI application development process,shielding technology implementation details,providing data management,model management,service management and other functions,it helps enterprises can develop and apply AI products quickly,efficiently and at low cost.However,due to the characteristics of AI application development,such as the complex training data sources and formats,the dependence of training on multiple types of resources,the tedious development process and so on,there are still some problems and challenges in the current open AI solutions and related platforms,such as nonstandard model training data management,unfair scheduling of multi-type training resources,and lack of a complete closed loop of AI application development:1)Model training data management has problems such as messy data format,complicated integration and utilization of different data,low data reuse rate,which affects data quality and training efficiency;2)There are many problems in multi-type training resource scheduling,such as unfair allocation,resource waste and job starvation,which reduces task execution efficiency and resource utilization;3)The functions of data side and service side are still insufficient,such as the lack of data annotation and service monitoring,and the complete closed loop of AI application development is not formed,which reduces the development efficiency.In view of the above problems and challenges,this article focuses on research and analysis of model training data management,multiple heterogeneous resource fair scheduling strategies and AI application development solutions,and completes the research and implementation of AI model training and open service platform.The main research contents are as follows:1)Propose and implement a JSON-based model training data management plan,which is used to unify different model training data format standards,integrate different model training data,and carry out data lifecycle management and data version management to improve data utilization and model training efficiency;2)A multi-resource scheduling strategy with balanced allocation is proposed and implemented,which integrates all resources,balances the allocation of dominant and non-dominant resources respectively,so as to ensure the fair allocation and full utilization of resources,and improve the efficiency of task execution and resource utilization;3)Research and implement a one-stop end-to-end AI model training and open service platform,which integrates data upload and labeling,model training,model evaluation,model testing,and service release to form the complete closed loop of AI application development and improve the efficiency of AI application development.Based on the above research content,this paper designs and implements a one-stop end-to-end AI model training and open service platform,which covers the whole workflow and helps enterprises accelerate the intelligent transformation.And the platform is applied to the key research and development topic "Science and Technology Consulting Data Resource System Research and Resource Construction" to verify the functional integrity of the platform and ensure the effectiveness,availability and reliability of the platform.