Abstract

The article discusses the current state of technologies for automated machine learning. The development trends and the nature of the distribution model - MLaaS - are defined. There is highlighted a number of problems of automating the machine learning process, such as: excessive simplification and specialization of tools, vagueness of implemented processes, lack of flexibility in the infrastructure hardware, using closed algorithms. As a partial or complete solution to them, we have proposed the architecture, consisting of separate modules: models, hybridizer, learning algorithms module, testing module, user support module, and a theoretical framework. The main feature of the given architecture is its modularity, transparency and encapsulation of components. Each module is described as a separate element, implemented as an independent microservice. The paper describes the benefits of applying the given approach to the implementation of automated machine learning systems, the need to implement the given or similar standards. For each of the modules, its purposes, the tasks it solves and the implemented functionality, as well as the data necessary for the functioning and their sources are described. A general diagram showing the flows of information exchange between modules is presented. The main scenarios for the resulting system operation, as well as ways of interacting with it and the result of its operation - the generated model - are described.

Full Text
Paper version not known

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.