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Structure function data provide insight into the nucleon quark distribution.They are relatively straightforward to extract from the world's vast, andgrowing, amount of inclusive lepto-production data. In turn, structurefunctions can be used to model the physical processes needed for planning andoptimizing future experiments. In this paper a machine learning algorithmcapable of predicting, using a unique set of parameters, the $F_2$ structurefunction, for four-momentum transfer $0.055 \leq Q^2 \leq 800.0$ GeV$^2$ andfor Bjorken $x$ from $2.8 \times 10^{-5}$ to the pion threshold is presented.The model was trained and reproduces the hydrogen and the deuterium data at the7~\% level, comparable with the average uncertainty of the experimental data.Extending the model to other nuclei or expanding the kinematic range arestraightforward. The model is at least ten times faster than existing structurefunctions parameterizations, making it an ideal candidate for event generatorsand systematic studies.

inclusiveAI: A machine learning representation of the $F_2$ structure function over all charted $Q^2$ and $x$ range