Reliable Machine Learning for Networking: Key Concerns and Approaches

Machine learning has become one of the go-to methods for solving problems in the field of networking.
This development is driven by data availability in large-scale networks and the commodification of machine learning frameworks.
While this makes it easier for researchers to implement and deploy machine learning solutions on networks quickly, there are a number of vital factors to account for when using machine learning as an approach to a problem in networking and translate testing performance to real networks deployments successfully.
This paper, rather than presenting a particular technical result, discusses the necessary considerations to obtain good results when using machine learning to analyze network-related data.