AMAZON RELEASES CONTAINER MONITORING FOR AMAZON ECS, EKS, AND KUBERNETES VIA CLOUDWATCH
Recently, Amazon announced that customers can now monitor, isolate, and diagnose their containerized applications and microservices environments using Amazon CloudWatch Container Insights. Cloud Insights is a part of Amazon CloudWatch, a fully-managed monitoring and observability service in AWS targeted for DevOps engineers, developers, site reliability engineers (SREs), and IT managers. Source: infoq.com
MICROSOFT PLANS TO REARCHITECT AZURE STACK BY MAKING IT CONTAINER-BASED
Microsoft’s ‘Project Saturn’ is an effort to rearchitect its Azure Stack hybrid computing platform and, possibly one day, to allow key Azure services and APIs to run anywhere. Source: zdnet.com
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 additional cloud providers, managed Kubernetes offerings, and managed cloud services (DBaaS, Big Data, and more). It’s a busy time for us
Read moreA 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 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.
Read moreUBER BECAME BIG BY IGNORING LAWS (AND IT PLANS TO KEEP DOING THAT)
Uber’s ascent to the largest rideshare company in the world was fueled by a recurring cycle in which it blatantly ignored state and local laws, became entrenched and widely used in a community, and then tried to use its largesse to change the laws it was breaking. Failing that, the company simply paid slap-on-the-wrist fines and continued as normal or petulantly left cities rather than comply with laws designed to regulate it. That Uber regularly broke laws to cement its frontrunner status is not a controversial statement, it’s a fact.
Read moreTHIS $30 DEVICE TURNS THE COLD OF OUTER SPACE INTO RENEWABLE ENERGY
The sun can be a powerful source of renewable energy, but so can the night sky. Now, a team of scientists have created a device that turns the cold of space into enough electricity to power an LED light. As described in a paper published on Thursday in the journal Joule, the device is based off of a thermoelectric generator that creates electricity from the difference in temperature between a ‘hot side’ and a ‘cold side.’
Read moreUNDERSTANDING 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 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.
Read moreTHREE 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 and better, we leverage machine learning (ML).
Read moreREIMAGINING 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 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.
Read moreINTRODUCING 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, networks are fundamentally complex systems, and their training and operation is still poorly understood.
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