The talk focuses on a generic, machine-learning approach that addressed the primary bottlenecks of mustering data, automating analyzer calibration, and tracking data and model performance over time. The gain in efficiency has been considerable, and the fact that the approach does not disturb any of the legacy (i.e., no changes or alterations to any analyzer or software in place) made deployment simple. The result is a standardized procedure for doing calibrations that adheres to best practices, archives all data and models with easy access in mind, and delivers models in any format.