  Solar flare prediction plays an important role in understanding and
forecasting space weather. The main goal of the Helioseismic and Magnetic
Imager (HMI), one of the instruments on NASA's Solar Dynamics Observatory, is
to study the origin of solar variability and characterize the Sun's magnetic
activity. HMI provides continuous full-disk observations of the solar vector
magnetic field with high cadence data that lead to reliable predictive
capability; yet, solar flare prediction effort utilizing these data is still
limited. In this paper, we present a machine-learning-as-a-service (MLaaS)
framework, called DeepSun, for predicting solar flares on the Web based on
HMI's data products. Specifically, we construct training data by utilizing the
physical parameters provided by the Space-weather HMI Active Region Patches
(SHARP) and categorize solar flares into four classes, namely B, C, M, X,
according to the X-ray flare catalogs available at the National Centers for
Environmental Information (NCEI). Thus, the solar flare prediction problem at
hand is essentially a multi-class (i.e., four-class) classification problem.
The DeepSun system employs several machine learning algorithms to tackle this
multi-class prediction problem and provides an application programming
interface (API) for remote programming users. To our knowledge, DeepSun is the
first MLaaS tool capable of predicting solar flares through the Internet.
