To accommodate additional ML use cases, Uber evolved Michelangelo's application of the Apache Spark MLlib library for greater flexibility and extensibility.
At an April 2019 meetup on ML and AI at Uber Seattle, members of our engineering team discussed three different approaches to enhancing our ML ecosystem.
Logan Jeya, Product Manager, explains how Uber's machine learning platform, Michelangelo, makes it easy to deploy models that enable data-driven decision making.
Uber engineers offer two common use cases showing how we orchestrate machine learning model training in our data workflow engine.
Uber built Manifold, a model-agnostic visualization tool for ML performance diagnosis and model debugging, to facilitate a more informed and actionable model iteration process.
Uber built Michelangelo, our machine learning platform, in 2015. Three years later, we reflect our journey to scaling ML at Uber and lessons learned along the way.
Uber developed Michelangelo PyML to run identical copies of machine learning models locally in both real time experiments and large-scale offline prediction jobs.
Uber built the next generation of COTA by leveraging deep learning models, thereby scaling the system to provide more accurate customer support ticket predictions.
In this article, Uber Engineering introduces our Customer Obsession Ticket Assistant (COTA), a new tool that puts machine learning and natural language processing models in the service of customer care to help agents deliver improved support experiences.
To ring in the New Year, the Uber Engineering Blog shares some of our editor's picks for 2017.
In this article, we highlight how Uber leverages machine learning and artificial intelligence to tackle engineering challenges at scale.
Uber Engineering introduces Horovod, an open source framework that makes it faster and easier to train deep learning models with TensorFlow.
Uber Engineering built AthenaX, our open source streaming analytics platform, to bring large-scale event stream processing to everyone.