Design And Implementation Of Machine Learning Application Development Platform

With the vigorous development of Internet computer technology,data collection methods and data storage technologies have become more and more perfect.The amount of data generated by companies and enterprises storage and scientific research has increased dramatically,and machine learning data processing technology has become more and more widely used in industry.Howerver,the machine learning algorithm models are difficult to use,so the realization of model construction and parameter tuning requires the help of professional algorithm engineers.Many companies need a lot of cost resources to use machine learning to train models.In response to the above problems,this paper is commissioned by the company to design and implement a machine learning application development platform.The platform completes the training of the algorithm model through a series of operations such as dragging and dropping component nodes,constructing workflow diagrams,manual parameter configuration,and visually displaying training results.It enables beginners who do not have machine learning expertise to get started quickly,reducing the development cost of machine learning algorithm models.The main work of this paper includes the following aspects:(1)Requirement analysis.This paper firstly introduces the development status of the machine learning visualization platform,expounds the importance of the platform to the enterprise,and explains in detail the related technologies that need to be used in the implementation of the system.On this basis,the overall functions of the system are sorted out according to the requirements of the partners,the business functions and interaction logic of the system are standardized.The functional requirements and performance requirements of the machine learning application development platform are clarified.(2)Design ideas.According to the results of requirement analysis,the system mainly has five core modules.The data set module is mainly responsible for the import of data sets and the upload of sample data.The component scheduling module is mainly responsible for the construction of the algorithm model.This module is based on the various algorithms in the machine learning component and configures the parameters for distributed training of the algorithm model.The visual modeling module is mainly responsible for drag-and-drop tools and the display of training results.The experimental process control module is mainly responsible for handling process-related management operations.The model service module is mainly responsible for the preservation and calling of algorithm models.The back end of this platform is mainly developed in Java language,and the Spring Boot framework is used to build the whole project based on the J2 EE standard architecture.The database uses the relational database MySQL and the distributed database HBase,and the front end uses the React framework and visualization technology to achieve.(3)Deployment test.After the system is coded,the test environment is deployed and the test case forms are analyzed and designed according to the requirements.The main modules of the system are tested in detail.This paper test the performance of the system based on non-functional requirements to ensure that the system meets the delivery standards.After system testing,the functions of each module of the machine learning application development platform implemented in this paper are running normally,meeting the performance requirements of the enterprise,and having reached the needs of the partner enterprise.In summary,the system provides a visual model construction platform.Users can complete complex model training by dragging and dropping component nodes,which can effectively reduce development costs and improve work efficiency.