The first workshop on "Towards A Domain-Customized Automated Machine Learning Framework For Networks and Systems" aims at creating a coalition of researchers who aim to build an AutoML platform for network operators. The platform helps network operators bridge the expertise gap when using ML to solve challenging networking problems.
Researchers at this workshop will discuss how we, as a community, can build a framework that: enables users to use ML to solve problems in networked systems without having in-depth ML expertise and that, similarly, enables ML experts to contribute to solving problems in networked systems without having expertise in these domains. We will discuss whether existing AutoML frameworks, as-is can be used by network operators? If not, what needs to change? Can domain customization help? If yes, what are the components of a domain-customized AutoML framework and how are they different from traditional AutoML solutions? What are the important criteria that such a system needs to meet? What are the techniques we can use to build such a framework? What are the collaborations we can initiate across industry and academia to make headway on solving this problem?