  We present GalaxAI - a versatile machine learning toolbox for efficient and
interpretable end-to-end analysis of spacecraft telemetry data. GalaxAI employs
various machine learning algorithms for multivariate time series analyses,
classification, regression and structured output prediction, capable of
handling high-throughput heterogeneous data. These methods allow for the
construction of robust and accurate predictive models, that are in turn applied
to different tasks of spacecraft monitoring and operations planning. More
importantly, besides the accurate building of models, GalaxAI implements a
visualisation layer, providing mission specialists and operators with a full,
detailed and interpretable view of the data analysis process. We show the
utility and versatility of GalaxAI on two use-cases concerning two different
spacecraft: i) analysis and planning of Mars Express thermal power consumption
and ii) predicting of INTEGRAL's crossings through Van Allen belts.
