Review: DataRobot aces automated machine learning

Data science is nothing if not tedious, in ordinary practice. The initial tedium consists of finding data relevant to the problem you're trying to model, cleaning it, and finding or constructing a good set of features. The next tedium is a matter of attempting to train every possible machine learning and deep learning model to your data, and picking the best few to tune. Then you need to understand the models well enough to explain them; this is especially important when the model will be helping to make life-altering decisions, and when decisions may be reviewed by regulators. Finally, you need to deploy the best model (usually the one with the best accuracy and acceptable prediction time), monitor it in production, and improve (retrain) the model as the data drifts over time.