One of the challenges in training and testing ML algorithms is to obtain high quality data: the live data your algorithms will be working on often isn’t availabe from the word go and even if it is, it may not contain all the features you want to train your algorithm on.
One approach to dealing with this is to generate data using simulations – this gives you full control both of the features contained within the data and also the volume and frequency of the data.
In this post we show you how to monitor long-standing simulations and use the data for machine learning purposes without having to deploy on your own hardware – and with the use of as little code as possible! This example makes use of simulation data generated using our own BPTK-Py Framework . We employ various Amazon Web Services applications for focusing on Analytics and ML rather than code and hardware/software.