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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 additional cloud providers, managed Kubernetes offerings, and managed cloud services (DBaaS, Big Data, and more). It’s a busy time for us

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 and applications. This problem is quite common and we have seen it over the years not only in cloud-based deployments and environments but also in the local type of deployments, similar to “3 blades in the rack next room”. This problem is applicable to any deployment with more than 1 environment in the picture, like DEV, QA, STG, PROD and so on. And the problem is, as you probably have guessed, the configuration data and its management.

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 became popular with recent technology advancements and computational capabilities that allow the processing of large amounts of data and the training of sophisticated algorithms in a reasonable amount of time.

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 and better, we leverage machine learning (ML).

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 reduce play delay, a metric describing how long it takes for a video to play after you press the start button. You can look at ABlaze (our centralized A/B testing platform) and take a quick look at how it’s performing.

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, networks are fundamentally complex systems, and their training and operation is still poorly understood.

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 the brain, often occur as very fast sequences of multiple memories. These so-called ‘replay’ sequences play out in a fraction of a second–so fast that we’re not necessarily aware of the sequence.

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 At 4:33 AM PDT one of ten data centers in one of the six Availability Zones in the US-EAST-1 Region saw a failure of utility power.

Without warning on an early August evening a significant proportion of the electricity grid in the UK went dark. It was still daylight so the disruption caused was not as large as it might have been, but it does highlight how we take a stable power grid for granted. The story is a fascinating one of a 76-second chain of unexpected shutdown events in which individual systems reacted according to their programming, resulted in a partial grid load shedding — what we might refer to as a shutdown.

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 the quality of the map.