Alternatively, when a mechanistic model is not available, it is possible to train a machine learning model on experimental data to predict yield or other reaction performance metrics given reaction conditions. If data is available in the form of a CSV file, this can be achieved in Summit using as little as two lines of code. Summit is the first package to offer a simple workflow for developing both of these types of Scheme 1: Benchmarks used in Summit for evaluating the performance of different machine learning strategies. A) Four dimensional optimisation of a S N Ar reaction between difluoronitrobenzene (1) and pyrrollidine (2). The objective is to maximise space time yield and minimise E-factor. B) Optimisation of a C-N cross coupling between aryl triflate (6) and aniline (7). The strategies must select one of three Buchwald catalysts and one of four organic bases for each experiment. Additionally, strategies must select the temperature, residence time, and base equivalents. The objectives are to maximise yield and minimise cost.