Cross-platform Desktop Application for Automated Machine Learning Pipeline Building

Machine learning is gaining popularity with increasing computational power and amount of data, achieving state of the art performance in various tasks, thus enlarging the demand of specialist in this field. Students and researchers spend a lot of time on theory, but when they switch their focus to practice, they duplicate their work and code every time they build, train and deploy models. At the same time, they have more important problems to do, so they need to iterate quickly in their routine tasks. As a result, a big part of a developer's work is spent not on the research and experiments but on installing tools, resolving errors in code and thinking why the model is behaving so poorly. In this work, the review of tools that can save one's time while studying or developing machine learning models is provided and the idea behind the novel solution to make learning curve more efficient and reduce time to market of such models is briefly explained.