As we’ve established earlier in this post series, Data Science is a process, with quite a lot of repetitive elements. Many Data Science projects involve a familiar set of tasks to identify, clean and prepare data, before finding the best model for the scenario at hand. And despite the mystique around the whole profession, many Data Scientists spend a lot of time complaining about all this repetitive work. But any repetitive process is ripe for automation, and Data Science is no exception. Enter the field of “AutoML”.
You arrive, slightly frazzled, a few minutes after the agreed time for lunch. At the door, you thrust the obligatory bottle of wine into your mother-in-law’s hand and say that you’re sorry you’re late; the traffic was terrible. “Which way did you come?” pipes up your father-in-law from the hallway, and you immediately realise that you have made a grave error.
As you stand, dazed, nodding along while nursing a room-temperature glass of Pinot Grigio, you find yourself thinking, “didn’t I just have to endure a conversation like this with the CMO this week?”