evoML Yellow Paper: Evolutionary AI and Optimisation Studio
This tool addresses the problem of high costs and complexity in ML model development for practitioners, but it appears incremental as it builds on existing automation concepts.
The paper tackles the cumbersome and resource-intensive process of machine learning model development and optimization by introducing evoML, an AI-powered tool that automates functionalities such as data cleaning, feature analysis, model optimization, and deployment, with key features including code optimization and multi-objective capabilities.
Machine learning model development and optimisation can be a rather cumbersome and resource-intensive process. Custom models are often more difficult to build and deploy, and they require infrastructure and expertise which are often costly to acquire and maintain. Machine learning product development lifecycle must take into account the need to navigate the difficulties of developing and deploying machine learning models. evoML is an AI-powered tool that provides automated functionalities in machine learning model development, optimisation, and model code optimisation. Core functionalities of evoML include data cleaning, exploratory analysis, feature analysis and generation, model optimisation, model evaluation, model code optimisation, and model deployment. Additionally, a key feature of evoML is that it embeds code and model optimisation into the model development process, and includes multi-objective optimisation capabilities.