From raw data to informed intelligence: Democratizing data science and ML at Uber
Who is this presentation for?
- Data scientists, engineers, data analysts, and product managers
Uber is changing the way people think about transportation. As an integral part of the logistical fabric in 600+ cities across 65 countries around the world, its using technology to give people what they want, when they want it. So whether it’s a ride, a sandwich, or a package, its 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. And 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.
Atul Gupte and Nikhil Joshi explore how Uber has approached building data science tools by working with its most advanced users to democratize complex techniques for less-technical people. They then dive into why Uber believes it’s important to offer seamless pathways to productionizing ML models with minimal effort so teams can leverage this to power various experiences. And you’ll hear several impactful examples. Atul and Nikhil dive into a few key items in particular, including Python model serving at scale (PyML), hyperparameter optimization (autotune), deep learning in the cloud (Google Cloud Platform [GCP]), knowledge feed of notebooks and dashboards, the challenges of reliability and monitoring at Uber’s scale, and concrete use cases in production and new ones coming down the pike.
- A basic understanding of machine learning and data science
What you'll learn
- Learn about about building data science tools and machine learning platforms that allow teams to be more productive and work better together
- Discover how Uber democratizes complex techniques for less-technical users and provides easy, seamless access to production-grade tooling and infrastructure that allows all teams to leverage the power of advanced ML to optimize key systems
Atul Gupte is a product manager on the product platform team at Uber, where 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, and advanced tooling to power Uber’s global ambitions. Previously, he built some of the world’s most beloved games (CityVille, Looney Tunes Dash!, and Words with Friends) and designed mobile advertising systems that serve over 1B ads per day and power revenues of ~$200M per year. Atul is a technologist at heart and firmly believes in the power of technology to deliver experiences that enrich lives and delight millions globally. He’s always found fulfillment by creating such avenues that serve as force multipliers and enable others to achieve their full potential. He holds a BS in computer science from the University of Illinois at Urbana-Champaign.
Nikhil is a product management lead at Uber. His team manages the big data storage and analytics portfolio. Previously, Nikhil helped wrangle customer data in companies like EMC, Pivotal, and Yahoo.
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