Algorithms that detect anomalies have to learn normal behaviour to be able to identify anomalous behaviour. If broad features of the expected anomalies are known the use of supervised Machine Learning (ML) is in order. But by definition, the most interesting anomalies are those unexpected, and in that case, unsupervised ML should be used. However, unsupervised strategies are substantially less powerful than possible supervised methods –a catch 22 situation.
Artemisa provides a high performance computing infrastructure that operates continuously thanks to its batch system.
At present, the facility is composed of two user-interface machines where users can develop and test their workflow and 23 machines where they can send their batch jobs. All the batch machines contain an NVIDIA GPU Volta V100 to support AI-oriented algorithms.