an important part of the modern data-driven landscape. They are a great way to improve the efficiency and productivity of your team. But what are feature stores? Does your organisation need one, and why? And when should you start building one?
A feature is a specific attribute of data that is useful for modelling. Features are usually built from aggregations sums, averages, minimums, maximums, and so on. These aggregations are then used to inform and enable a machine learning model to predict something. Uber’s ML engineering team built and popularised feature stores back in 2017 when they introduced Michelangelo, an ML-as-a-service platform, which made building, deploying and operating ML solutions a bearable process. Today you can find a wide range of competing feature stores, each with their own unique benefits and capabilities. For example, Google has one in Vertex AI and Amazon has one in SageMaker.
A feature store, then, is a centralised data management system that lets you store, manage and distribute features to ML models. Ultimately, a feature store improves the accuracy of your models, saves time and increases productivity. More importantly, it reduces the amount of time that data scientists spend on discovering and calculating features that are often repeated within the same company.