  We present an efficient machine learning based automated framework for the
fast tuning of single-electron pump devices into current quantization regimes.
It uses a sparse measurement approach based on an iterative active learning
algorithm to take targeted measurements in the gate voltage parameter space.
When compared to conventional parameter scans, our automated framework allows
us to decrease the number of measurement points by about an order of magnitude.
This corresponds to an eight-fold decrease in the time required to determine
quantization errors, which are estimated via an exponential extrapolation of
the first current plateau embedded into the algorithm. We show the robustness
of the framework by characterizing 28 individual devices arranged in a
GaAs/AlGaAs multiplexer array, which we use to identify a subset of devices
suitable for parallel operation at communal gate voltages. The method opens up
the possibility to efficiently scale the characterization of such multiplexed
devices to a large number of pumps.
