Crossplane v0.3 – accelerating support for more clouds and managed services

New developer guide, CLI tooling and enhanced out-of-tree Infra Stacks (GCP, AWS, Azure) enables the community to add support for more cloud providers, managed Kubernetes offerings, and fully-managed cloud services that can be hosted in your cloud of choice. The momentum keeps rolling forward with Crossplane community engagement around extending Crossplane to add support for […]

A standard way of managing configurations for multiple environments (and clouds)

This article intended to share ideas and solutions to address some challenges related to Configuration Management, especially in the cloud environment. Hope you find this read helpful. The approach described in this article was conceptualized a few years back, then implemented and used across many, many projects to build configuration management components for production-grade systems […]

Understanding Convolutional Neural Networks

A Convolutional Neural Network (CNN) is a class of deep, feed-forward artificial neural networks most commonly applied to analyzing visual imagery. The architecture of these networks was loosely inspired by biological neurons that communicate with each other and generate outputs dependent on the inputs. Though work on CNNs started in the early 1980s, they only […]

Three Approaches to Scaling Machine Learning with Uber Seattle Engineering

Uber’s services require real-world coordination between a wide range of customers, including driver-partners, riders, restaurants, and eaters. Accurately forecasting things like rider demand and ETAs enables this coordination, which makes our services work as seamlessly as possible. In an effort to constantly optimize our operations, serve our customers, and train our systems to perform better […]

Reimagining Experimentation Analysis at Netflix

Another day, another custom script to analyze an A/B test. Maybe you’ve done this before and have an old script lying around. If it’s new, it’s probably going to take some time to set up, right? Not at Netflix. Suppose you’re running a new video encoding test and theorize that the two new encodes should […]

Introducing LCA: Loss Change Allocation for Neural Network Training

Neural networks (NNs) have become prolific over the last decade and now power machine learning across the industry. At Uber, we use NNs for a variety of purposes, including detecting and predicting object motion for self-driving vehicles, responding more quickly to customers, and building better maps. While many NNs perform quite well at their tasks, […]

Replay in biological and artificial neural networks

Our waking and sleeping lives are punctuated by fragments of recalled memories: a sudden connection in the shower between seemingly disparate thoughts, or an ill-fated choice decades ago that haunts us as we struggle to fall asleep. By measuring memory retrieval directly in the brain, neuroscientists have noticed something remarkable: spontaneous recollections, measured directly in […]

AWS power outage with data loss

On August 31st, 2019, an Amazon AWS US-EAST-1 datacenter in North Virginia experienced a power failure at 4:33 AM, which led to the datacenter’s backup generators to kick on. Unfortunately, these generators started failing at approximately 6:00 AM , which led to 7.5% of the EC2 instances and EBS volumes becoming unavailable. ‘1:30 PM PDT […]

How Lyft Creates Hyper-Accurate Maps from Open-Source Maps and Real-Time Data

At Lyft, our novel driver localization algorithm detects map errors to create a hyper-accurate map from OpenStreetMap (OSM) and real-time data. We have fixed thousands of map errors in OSM in bustling urban areas. Later in the post, we share a sample of the detected map errors in Minneapolis with the OSM Community to improve […]