We train and deploy various ML algorithms to personalize the user experience in every part of the Flo app. While our first models were trained and served in a custom way, it quickly became hard to manage all the complex datasets and entities we deal with.
Therefore we’ve adopted the Tecton Feature Store. Tecton is one of the most advanced Feature Stores on the market. It allows to quickly explore and experiment with new features and datasets offline, while making it easy to serve the exact same data in real-time in a high-load environment.
The key benefits of using the Tecton Feature Store:
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A clear and unified feature definition framework - less place for bugs and inconsistency between training on historical data and real-time serving
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The feature definition code is covered with tests, the feature availability and freshness is monitored
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Online data serving is easy to run and manage - every feature service becomes an API endpoint that is launched with a single command
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Time-travel - we’re able to obtain the exact historical values of every feature we store and serve - this makes it easy to debug models retrospectively
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Features are reusable in multiple problems - we can collaborate across different domains, while keeping the same standards for ML engineering for the whole company
Tecton runs on our own AWS infrastructure, using Spark and DynamoDB as offline and online storages accordingly. Tecton was our first step on our way to a scalable, reliable and efficient ML infrastructure at Flo.