Sep 23–26, 2019

From raw data to informed intelligence: democratizing data science and ML at Uber

Atul Gupte (Uber Technologies Inc.), Nikhil Joshi (Uber)
2:05pm2:45pm Wednesday, September 25, 2019
Location: 1A 21/22
Secondary topics:  Data, Analytics, and AI Architecture, Transportation and Logistics

Who is this presentation for?

Data Scientists, Engineers, Data Analysts, Product Managers

Level

Intermediate

Description

At Uber, we’re changing the way people think about transportation. As an integral part of the logistical fabric in 600+ cities across 65 countries around the world, we’re using technology to give people what they want, when they want it. So whether it’s a ride, a sandwich, or a package, our systems are tirelessly optimizing every part of the journey to ensure each experience is nothing short of magical.

To do this, teams across Uber depend on data to power every insight and inform every decision. People working with data at Uber bring various skills and proficiencies to the table: some advanced users know exactly what they’re looking for while others are learning to explore various techniques to wrangle and make sense of data. But they all have one thing in common: they want to make intelligent data-driven decisions.

Uber has been at the leading edge of democratizing data science and seamlessly productizing machine learning since 2015. Every aspect of the Uber experience is powered by data and ML – everything from in-app ETAs, menu recommendations, and map labeling to driver dispatch and customer support. In this talk, we’ll explore how we’ve approached building data science tools by working with our most advanced users to democratize complex techniques for less-technical people. From there, we’ll dive into why we believe it’s important to offer seamless pathways to productionizing ML models with minimal effort so that teams can leverage this to power various experiences. Finally, we’ll talk about several impactful examples, and discuss a few key items in particular:

- Python Model Serving at Scale (PyML)
- Hyperparameter Optimization (AutoTune)
- Deep Learning in the Cloud (GCP)
- Knowledge Feed of Notebooks, Dashboards
- Challenges of reliability and monitoring at Uber’s scale
- Concrete use cases in production and new ones coming down the pike

Prerequisite knowledge

Basic Machine Learning and Data Science concepts

What you'll learn

In this talk, we’ll discuss how we think about building data science tools and machine learning platforms that allow teams to be more productive and work better together. Specifically, we’ll explore how we’re democratizing complex techniques for less-technical users, and providing easy, seamless access to production-grade tooling and infrastructure that allows all teams at Uber to leverage the power of advanced machine learning to optimize key systems like in-app ETAs, menu recommendations, and map labeling to driver dispatch and customer support
Photo of Atul Gupte

Atul Gupte

Uber Technologies Inc.

Atul Gupte is a Product Manager on the Product Platform team at Uber. He holds a BS in Computer Science from the University of Illinois at Urbana-Champaign. At Uber, he helps drive product decisions to ensure Uber’s data science teams are able to achieve their full potential, by providing access to foundational infrastructure, stable compute resources & advanced tooling to power Uber’s global ambitions. Previously, at Zynga, he spent time building some of the world’s leading social games and also helped build out the company’s mobile advertising platform.

Photo of Nikhil Joshi

Nikhil Joshi

Uber

Nikhil is a Product Management lead at Uber. His team manages the big data storage and analytics portfolio at the company,

Even before Uber, Nikhil helped customer wrangle data in companies like EMC, Pivotal, and Yahoo.

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