Operational machine learning is difficult to get right, especially when real-time data and online inference are involved — but it can be done. 

For Tide, a UK banking provider for small and medium businesses, the ability to detect fraudulent transactions and make credit approval decisions in real time is key to their success. 

Register today to hear the key challenges that Tide encountered with their first attempt at building a feature store — including difficulty adding features, unpredictable changes to production, and slow iteration times — and how they solved them. 

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⭐Check out some of the highlights⭐

Hendrik Brackmann, VP Data at Tide, will also share benefits realized by adopting Tecton, including: 

• Easy re-use of high-quality features in different models within the same domain

• Ease of backtesting so the team was able to estimate the holistic impact of changes before deploying them into production

• And 50% faster deployment time due to the use of pre-built feature types

There will also be a live Q&A after the presentation. 

Register today to save your spot!