论文标题
evoml黄纸：进化AI和优化工作室
evoML Yellow Paper: Evolutionary AI and Optimisation Studio
论文作者
论文摘要
机器学习模型的开发和优化可能是一个相当麻烦且资源密集的过程。定制模型通常更难建立和部署，并且需要基础架构和专业知识，而这些基础架构和专业知识通常是昂贵的。机器学习产品开发生命周期必须考虑到开发和部署机器学习模型的困难的需求。 EVOML是AI驱动的工具，可在机器学习模型开发，优化和模型代码优化中提供自动化功能。 EVOML的核心功能包括数据清洁，探索性分析，功能分析和生成，模型优化，模型评估，模型代码优化和模型部署。此外，EVOML的关键特征是它将代码和模型优化嵌入模型开发过程中，并包括多目标优化功能。
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.
