evoML Yellow Paper: Evolutionary AI and Optimisation Studio
December 20, 2022 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
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Authors
Lingbo Li, Leslie Kanthan, Michail Basios, Fan Wu, Manal Adham, Vitali Avagyan, Alexis Butler, Paul Brookes, Rafail Giavrimis, Buhong Liu, Chrystalla Pavlou, Matthew Truscott, Vardan Voskanyan
arXiv ID
2212.10671
Category
cs.AI: Artificial Intelligence
Cross-listed
cs.SE
Citations
1
Venue
arXiv.org
Last Checked
4 months ago
Abstract
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.
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