Uber released Ludwig, our open source, code-free deep learning toolbox, in February 2019, introducing the world to one of the easiest ways to get started building machine learning models. The simplicity and the declarative nature of Ludwig’s model definition …
The post Ludwig v0.2 Adds New Features and Other Improvements to its Deep Learning Toolbox appeared first on Uber Engineering Blog.
Tools that enable fast and flexible experimentation democratize and accelerate machine learning research. Take for example the development of libraries for automatic differentiation, such as Theano, Caffe, TensorFlow, and PyTorch: these libraries have been instrumental in …
The post Introducing EvoGrad: A Lightweight Library for Gradient-Based Evolution appeared first on Uber Engineering Blog.
Intelligent conversational agents have evolved significantly over the past few decades, from keyword-spotting interactive voice response (IVR) systems to the cross-platform intelligent personal assistants that are becoming an integral part of daily life.
Along with this growth comes the need …
The post Introducing the Plato Research Dialogue System: A Flexible Conversational AI Platform appeared first on Uber Engineering Blog.
At Uber, we use marketplace algorithms to connect drivers and riders. Before the algorithms roll out globally, Uber fully tests and evaluates them to create an optimal user experience that maps to our core marketplace principles.
To make product …
The post Gaining Insights in a Simulated Marketplace with Machine Learning at Uber appeared first on Uber Engineering Blog.
Machine learning models perform a diversity of tasks at Uber, from improving our maps to streamlining chat communications and even preventing fraud.
In addition to serving a variety of use cases, it is important that we make machine learning …
The post No Coding Required: Training Models with Ludwig, Uber’s Open Source Deep Learning Toolbox appeared first on Uber Engineering Blog.
At Uber, we apply neural networks to fundamentally improve how we understand the movement of people and things in cities. Among other use cases, we employ them to enable faster customer service response with natural language models and lower wait …
The post Deconstructing Lottery Tickets: Zeros, Signs, and the Supermask appeared first on Uber Engineering Blog.
Zoubin Ghahramani is Uber’s Chief Scientist and the Head of AI.
The ease and simplicity of Uber’s platform is built on fundamental advances in science and technology. Teams across Uber are committed to developing the most advanced scientific techniques in …
The post Introducing the Uber Research Publications Site appeared first on Uber Engineering Blog.
Machine learning (ML) pervades many aspect of Uber’s business. From responding to customer support tickets, optimizing queries, and forecasting demand, ML provides critical insights for many of our teams.
Our teams encountered many different challenges while incorporating …
The post Accessible Machine Learning through Data Workflow Management appeared first on Uber Engineering Blog.
This article was originally published on The LF Deep Learning Foundation Blog.
Horovod, a distributed deep learning framework created by Uber, makes distributed deep learning fast and easy-to-use. Horovod improves the speed, scale, and resource allocation for …
The post Horovod Adds Support for PySpark and Apache MXNet and Additional Features for Faster Training appeared first on Uber Engineering Blog.
Over the past several years, artificial intelligence (AI) has become an integral component of many enterprise tech stacks, facilitating faster, more efficient solutions for everything from self-driving vehicles to automated messaging platforms. On the AI spectrum, deep probabilistic programming, a …
The post Uber Open Source: Catching Up with Fritz Obermeyer and Noah Goodman from the Pyro Team appeared first on Uber Engineering Blog.
