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How Uber deploys machine learning-as-a-service with its internal Michelangelo tool

Scott Carey | Sept. 15, 2017
Uber has shared some details on how it deploys machine learning in-house using a tool called Michelangelo.

"On the Michelangelo platform, the UberEats data scientists use gradient boosted decision tree regression models to predict this end-to-end delivery time."

So the model crunches through time of day, delivery location, average meal prep time for the last seven days and and near-realtime meal prep data to make an accurate prediction.

"Models are deployed across Uber's data centres to Michelangelo model serving containers and are invoked via network requests by the UberEats microservices. These predictions are displayed to UberEats customers prior to ordering from a restaurant and as their meal is being prepared and delivered."

 

What next?

The Uber engineering team are constantly tweaking Michelangelo and are currently working on a feature called AutoML.

"This will be a system for automatically searching and discovering model configurations (algorithm, feature sets, hyper-parameter values, etc.) that result in the best performing models for given modelling problems," the blog explains.

"The system would allow data scientists to specify a set of labels and an objective function, and then would make the most privacy-and security-aware use of Uber's data to find the best model for the problem. The goal is to amplify data scientist productivity with smart tools that make their job easier."

The team is also working on a model visualisation tool to help with debugging deep learning models. It also wants to build out its online learning portal for machine learning to further democratise the use of the technique across the organisation.

 

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