How might we improve the understandability of the model development lifecycle in order to empower analysts with varying levels of technical proficiency?

How ML Helps Bloomberg

Bloomberg is known for delivering information faster and more accurate than their competition. Time is money in the financial industry, so giving data analysts the ability to build ML models means that data will delivered to the terminal faster and more accurate than ever before.


Understanding the problem space

Machine learning is a complicated space. As a result, we spent the first half of our project conducting research to better understand how model building works and what non-technical users need to build high-performing models. Click on the button below to read about the insights we gathered and potential ideas that we came up with.

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Model Building Re-imagined

Because we are tackling such an innovative space, there were a lot of question marks surrounding how we were going to translate our validated ideas into something tangible that our analysts resonated with. Click on the button below to read about how we ended up with our final designs through many iterations and user testing sessions.

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