Uber’s Data Platform in 2019: Transforming Information to Intelligence
You’re seeing information for Japan . To see local features and services for another location, select a different city. Show more Uber’s busy 2019 included our billionth delivery of an Uber Eats order, 24 million miles covered by bike and scooter riders on our platform, and trips to top destinations such as the Empire State Building, the Eiffel Tower, and the Golden Gate Bridge. Behind the scenes
GitHub - eugeneyan/applied-ml: 📚 Papers & tech blogs by companies sharing their work on data science & machine learning in production.
Curated papers, articles, and blogs on data science & machine learning in production. ⚙️ Figuring out how to implement your ML project? Learn how other organizations did it: How the problem is framed 🔎(e.g., personalization as recsys vs. search vs. sequences) What machine learning techniques worked ✅ (and sometimes, what didn't ❌) Why it works, the science behind it with research, literature, and
How LinkedIn, Uber, Lyft, Airbnb and Netflix are Solving Data Management and Discovery for Machine Learning Solutions
How LinkedIn, Uber, Lyft, Airbnb and Netflix are Solving Data Management and Discovery for Machine Learning Solutions When comes to machine learning, data is certainly the new oil. The processes for managing the lifecycle of datasets are some of the most challenging elements of large scale machine learning solutions. Data ingestion, indexing, search, annotation, discovery are some of the aspects r
Marmaray: An Open Source Generic Data Ingestion and Dispersal Framework and Library for Apache Hadoop
Marmaray: An Open Source Generic Data Ingestion and Dispersal Framework and Library for Apache Hadoop Connecting users worldwide on our platform all day, every day requires an enormous amount of data management. When you consider the hundreds of operations and data science teams analyzing large sets of anonymous, aggregated data, using a variety of different tools to better understand and maintain
Databook: Turning Big Data into Knowledge with Metadata at Uber
You’re seeing information for Japan . To see local features and services for another location, select a different city. Show more From driver and rider locations and destinations, to restaurant orders and payment transactions, every interaction on Uber’s transportation platform is driven by data. Data powers Uber’s global marketplace, enabling more reliable and seamless user experiences across our
Uber’s Big Data Platform: 100+ Petabytes with Minute Latency
Data / ML, EngineeringUber’s Big Data Platform: 100+ Petabytes with Minute LatencyOctober 17, 2018 / Global Uber is committed to delivering safer and more reliable transportation across our global markets. To accomplish this, Uber relies heavily on making data-driven decisions at every level, from forecasting rider demand during high traffic events to identifying and addressing bottlenecks in our
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Monitoring Data Quality at Scale with Statistical Modeling
Joshua Shinavier – The Knowledge Graph Conference
Building an Enterprise Knowledge Graph at Uber: Lessons from Reality
LeapTalk Dr. Muthusrinivasan on Uber Knowledge Graph