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.
翻译:机械学习模式的开发和优化可能是一个相当繁琐和资源密集型的过程。定制模式往往更难建立和部署,它们需要往往成本高昂的基础设施和专门知识来获取和维持。机械学习产品开发生命周期必须考虑到需要克服开发和部署机器学习模式的困难。电子版本ML是一个AI驱动的工具,在机器学习模式开发、优化和示范代码优化方面提供自动化功能。电子版本ML的核心功能包括数据清理、探索分析、特征分析和生成、模型优化、模型评估、示范代码优化和模型部署。此外,电子版本ML的一个关键特征是将代码和模式优化纳入模型开发进程,并包括多目标优化能力。