Uber is one of those organizations that rely heavily on data. Each day, millions of trips take place in 700 cities across the world, generating information on traffic, preferred routes, estimated times of arrival/delivery, drop-off locations, and more that enables Uber to deliver a smooth riding experience to its customers.

With access to the rich dataset coming from the cabs, drivers, and users, Uber has been investing in machine learning and artificial intelligence to enhance its business. Uber AI Labs consists of ML researchers and practitioners that translate the benefits of the state of the art machine learning techniques and advancements to Uber’s core business. From computer vision to conversational AI to sensing and perception, Uber has successfully infused ML and AI into its ride-sharing platform.

Since 2017, Uber has been sharing the best practices of building, deploying, and managing machine learning models. Some of the internal tools and frameworks used at Uber are built on top of popular open source projects such as Spark, HDFS, Scikit-learn, NumPy, Pandas, TensorFlow and XGBoost.

Let’s take a closer look at Uber’s projects in the ML domain.

Michelangelo – ML Platform as a Service

Michelangelo is a machine learning platform that standardized the workflows and tools across teams through an end-to-end system. It enabled developers and data scientists across the company to easily build and operate machine learning systems at scale.

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