Feature stores are an important new concept in data science and machine-learning. They provide a way to organise much of the data preparation required when building a machine-learning model, and do it in a way that is repeatable and easy to deploy. They make data scientists more productive, and ease the path between research and production. We think they will be particularly useful when applied to time-series problems such as forecasting.

The bird’s-nest of code

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ML models need to be fed well-organised features

Most data scientists have been there. A new project starts with neatly organised datasets and a set of scripts to train models and generate predictions from new data. As the project progresses, things get more complicated: extra datasets get bolted on, existing data modified and transformed, new ideas tested. …

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