Short for “automated machine learning,” AutoML is an exciting trend that’s driving the “democratization” of data science mentioned in the introduction to this piece. Developers of autoML solutions aim to create tools and platforms that can be used by anyone to create their own ML apps. In particular, it’s aimed at subject matter experts whose specialized expertise and insights make them ideally placed to develop solutions to the most pressing problems in their particular fields but who often lack the coding knowledge needed to apply AI to those problems.
Quite often, a large portion of a data scientist’s time will be taken up with data cleansing and preparation – tasks that require data skills and are often repetitive and mundane. AutoML at its most basic involves automating those tasks, but it increasingly also means building models and creating algorithms and neural networks. The aim is that very soon, anyone with a problem they need to solve, or an idea they want to test, will be able to apply machine learning through simple, user-friendly interfaces that keep the inner workings of ML out of sight, leaving them free to concentrate on their solutions. 2022 is likely to see us take a big step closer to this being an everyday reality.