Tag: Machine Learning
Introducing EvoGrad: A Lightweight Library for Gradient-Based Evolution
Uber AI Labs releases EvoGrad, a library for catalyzing gradient-based evolution research, and Evolvability ES, a new meta-learning algorithm enabled by this library.
Gaining Insights in a Simulated Marketplace with Machine Learning at Uber
Uber's Marketplace simulation platform leverages ML to rapidly prototype and test new product features and hypotheses in a risk-free environment.
No Coding Required: Training Models with Ludwig, Uber’s Open Source Deep Learning Toolbox
Uber AI's Piero Molino discusses Ludwig's origin story, common use cases, and how others can get started with this powerful deep learning framework built on top of TensorFlow.
Improving Uber’s Mapping Accuracy with CatchME
CatchMapError (CatchMe) is a system that automatically catches errors in Uber's map data with anonymized GPS traces from the driver app.
Accessible Machine Learning through Data Workflow Management
Uber engineers offer two common use cases showing how we orchestrate machine learning model training in our data workflow engine.
Data Science at Scale: A Conversation with Uber’s Fran Bell
We spoke to Data Science Director Fran Bell about machine learning at Uber and what she finds most challenging—and rewarding—about her work.
Uber Open Source: Catching Up with Fritz Obermeyer and Noah Goodman from the Pyro...
We spoke with Fritz Obermeyer and Noah Goodman, Pyro project co-leads, about the potential of open source AI software at Uber and beyond.
Introducing Ludwig, a Code-Free Deep Learning Toolbox
Uber AI developed Ludwig, a code-free deep learning toolbox, to make deep learning more accessible to non-experts and enable faster model iteration cycles.
Manifold: A Model-Agnostic Visual Debugging Tool for Machine Learning at Uber
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.
POET: Endlessly Generating Increasingly Complex and Diverse Learning Environments and their Solutions through the...
Uber AI Labs introduces the Paired Open-Ended Trailblazer (POET), an algorithm that leverages open-endedness to push the bounds of machine learning.
Open Source at Uber: Meet Alex Sergeev, Horovod Project Lead
We sat down with Horovod project lead, Alex Sergeev, to discuss his path to open source and what most excites him about the future of Uber's distributed deep learning framework.
How to Get a Better GAN (Almost) for Free: Introducing the Metropolis-Hastings GAN
Metropolis-Hastings Generative Adversarial Networks (GANs) leverage the discriminator to pick better samples from the generator after ML model training is done.
Collaboration at Scale: Highlights from Uber Open Summit 2018
Uber hosted its first Open Summit on November 15, inviting the open source community to learn about our open source projects from the engineers who use them every day. Check out highlights from the day, including keynotes from the Linux Foundation's Jim Zemlin and Uber AI's Zoubin Ghahramani.
Experience in AI: Uber Hires Jan Pedersen
Uber welcomes Jan Pedersen as a Distinguished Scientist to our Uber AI group, where he will bring his extensive experience to our efforts in improving artificial intelligence and machine learning.
NVIDIA: Accelerating Deep Learning with Uber’s Horovod
Horovod, Uber's open source distributed deep learning system, enables NVIDIA to scale model training from one to eight GPUs for their self-driving sensing and perception technologies.
My Journey from Working as a Fabric Weaver in Ethiopia to Becoming a Software...
Samuel Zemedkun reflects on his immigrant experience and how his part-time driving through the Uber platform funded his education and inspired his decision to join the company.
Michelangelo PyML: Introducing Uber’s Platform for Rapid Python ML Model Development
Uber developed Michelangelo PyML to run identical copies of machine learning models locally in both real time experiments and large-scale offline prediction jobs.
Applying Customer Feedback: How NLP & Deep Learning Improve Uber’s Maps
To improve our maps, Uber Engineering analyzes customer support tickets with natural language processing and deep learning to identify and correct inaccurate map data.
Improving Driver Communication through One-Click Chat, Uber’s Smart Reply System
One-click chat, the Uber driver app's smart reply system, leverages machine learning to make in-app messaging between driver-partners and riders more seamless.
Introducing Petastorm: Uber ATG’s Data Access Library for Deep Learning
Uber's Advanced Technologies Group introduces Petastorm, an open source data access library enabling training and evaluation of deep learning models directly from multi-terabyte datasets in Apache Parquet format.
