As a company heavily invested in AI, Uber aims to leverage machine learning (ML) in product development and the day-to-day management of our business. In pursuit of this goal, our data scientists spend considerable amounts of time prototyping and validating powerful new types of ML models to solve Uber’s most challenging problems (e.g., NLP based smart reply systems, ticket assistance systems, fraud detection, and financial and marketplace forecasting). Once a model type is empirically validated to be best for the task, engineers work closely with data science teams to productionize and make it available for low latency serving at Uber-scale.
This cycle of prototyping, validating, and productionizing is central to ML innovation at Uber, and the less friction at each stage of this process, the faster Uber can innovate.