2 years ago

Category: Business, Data, machine-learning

Tecton, the enterprise feature store for machine learning (ML), has launched low-latency streaming pipelines to its feature store this week, giving its users the ability to build real-time ML applications with ultra-fresh features to the order of sub-100 milliseconds. A feature, in ML, is data that a machine learning model can train on and infer a signal from, while the feature store is the interface between that data and the model.

It is this final level of complexity that Tecton is addressing this week with its newest capability, which it says automates the process of transforming streaming and real-time data, including time aggregations, into ML features in less than one second.

Their goal now is to bring this same sort of capability to companies that don’t want to make that same investment.

If you have a fraud detection application, you need to know in that very moment, ‘How many transactions has this credit card been used for over the last five minutes as of right now?’ Not as of a minute ago or as of five minutes ago. It’s much, much easier to support and serve features that are old, where you can easily calculate in the background, but as you can imagine, that’s not good enough. You really need to know in the moment in which you’re making a prediction, how many transactions have happened as of right now, because if you don’t know that information, you’re basically throwing away a really important signal that is required in order to accurately predict whether this transaction is a fraudulent transaction or not.”