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This post was inspired by listening to the February 19, 2019, Kubernetes Podcast, “Ingress, with Tim Hockin.” The Kubernetes Podcast is turning out to be a very well done podcast overall, and well worth the listen. In the Ingress episode, the podcasters interview Tim Hockin who’s one of the original Kubernetes co-founders, a team lead on the Kubernetes predecessor Borg/Omega, and is still very active within the Kubernetes community such as chairing the Kubernetes Network Special Interest Group that currently own the Ingress resource specification.

SAN FRANCISCO, Calif. – April 11, 2019 – The Cloud Native Computing Foundation® (CNCF®), which sustains open source technologies like Kubernetes® and Prometheus™, today announced that Fluentd is its sixth project to graduate, following Kubernetes, Prometheus, Envoy, CoreDNS and containerd. To move from the maturity level of incubation to graduation, projects must demonstrate thriving adoption, a documented, structured governance process, and a strong commitment to community sustainability and inclusivity. Fluentd was created in 2011 by Sadayuki “Sada” Furuhashi, co-founder of Treasure Data, Inc.

New Relic’s Kevin Scaldeferri shares insider tips and tricks on how to use New Relic Query Language (NRQL) to understand and use your application data in powerful new ways. Source: newrelic.com

The famed 19th-century engineer may have been inspired by an early example of AI chicanery and hype In this six-part series, we explore that human history of AI—how innovators, thinkers, workers, and sometimes hucksters have created algorithms that can replicate human thought and behavior (or at least appear to). While itcan be exciting to be swept up by the ideaof superintelligent computers that have no need for human input, the true history of smart machines shows that our AI is only as good as we are.

Looking for a lightweight alternative search backend to Elasticsearch? Sonic is a search backend written in Rust. It aims for a low CPU footprint and uses around 30 MB of RAM. See its speed benchmarks and its search query features. Find out how to get started. According to the DB-Engines Ranking, Elasticsearch is currently in the top ten database management systems. Despite its popularity and usage, alternative tools exist for users who feel that Elasticsearch does not meet their specific use cases.

On Monday, February 11, CVE-2019-5736 was disclosed. This vulnerability is a flaw in runc, which can be exploited to escape Linux containers launched with Docker, containerd, CRI-O, or any other user of runc. But how does it work? Dive in! Processes interact with the operating system to perform a variety of operations (for example, reading and writing files, taking input, communicating on the network, etc.) via system calls, or syscalls.

Ever burgeoning digital data combined with impressive research has lead to a rising interest in Machine Learning or ML, which has further powered a vibrant ecosystem of technologies, frameworks, and libraries in the space. Scikit-learn sees high adoption from the tech community. The most probable reason is a powerful Python interface that allows tweaking of models across multiple parameters. MLlib and H2O should be considered when working with Spark. Spark does come with MLlib and has a higher level wrapper called SparkML that supports the same.

Kubernetes is the de facto standard for container orchestration and Vault by HashiCorp is the de facto standard for secrets management. Now the question is: how do you combine those technologies so that you can use secrets from your central Vault instance in your Kubernetes applications? One solution would be to use the AppRole auth method. Boostport provides a nice integration of AppRoles in Kubernetes. Another possibility is to use the Kubernetes auth method.

In this article we are going to create deep reinforcement learning agents that learn to make money trading Bitcoin. In this tutorial we will be using OpenAI’s gym and the PPO agent from the stable-baselines library, a fork of OpenAI’s baselines library. If you are not already familiar with how to create a gym environment from scratch, or how to render simple visualizations of those environments, I have just written articles on both of those topics.

While evaluating the performance of a server application, we eventually (and hopefully) run up against the most fundamental constraining factor: the network. Cloud providers tend to offer somewhat handwavy guidance on networking constraints, especially when compared to the exhaustive literature explaining the quotas for RAM, CPU, and I/O. While working on an unrelated stress test in EC2, we were surprised by some results that led us down the path of investigating EC2 network capacity claims, resulting in this writeup.