Enabling Offline Inferences at Uber Scale

Enabling Offline Inferences at Uber Scale

摘要

At Uber we use data from user support interactions to identify gaps in our products and create better, more delightful experiences for our users. Support interactions with customers include information about broken product experiences, any technical or operational issues faced, and even their general sentiment towards the product and company. Understanding the root cause of a broken product experience requires additional context, such as details of the trip or the order. For example, the root cause for a customer issue about a delayed order might be due to a bad route given to the courier. In this case, we would want to attribute the poor customer experience to courier routing errors so that the Maps team can fix the same.

Initially, we had manual agents review a statistically significant sample from resolved support interactions. They would manually verify and label the resolved support issues and assign root cause attribution to different categories and subcategories of issue types. We wanted to build a proof-of-concept (POC) that automates and scales this manual process by applying ML and NLP algorithms on the semi-structured or unstructured data from all support interactions, on a daily basis.

This article describes the approach we took and the end-to-end design of our data processing and ML pipelines for our POC, which optimized the ease of building and maintaining such high scale offline inference workflows by engineers and data scientists on the team.