Prometheus anomaly detection

With an increase in the number of applications being deployed on Red Hat OpenShift, there is a strong need for application monitoring. A number of these applications are monitored via Prometheus metrics, resulting in an accumulation of a large number of time-series metrics stored in a TSDB (time series database). Some of these metrics can have anomalous values, which may indicate issues in the application, but it is difficult to identify them manually. To address this issue, we came up with an AI-based approach of training a machine-learning model on these metrics for detecting anomalies.

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Sentiment analysis with machine learning

When developing a new technology, it really helps if you are also a user of that new tech. This has been an approach of Red Hat around artificial intelligence and machine learning — develop openly on one hand, exchanging knowledge across the organization to use the same tools in the other hand to work on interesting business problems. All while keeping a two-way exchange to and from the open source commons.

This is the sort of left-hand/right-hand move that data scientist Oindrilla Chatterjee began using as part of a project she originally started during an internship, then later in a full-time role at Red Hat. Chatterjee and her team are looking at how to do sentiment analysis using machine learning on a dataset consisting of customer and partner surveys regarding a service offering.

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Diagnosing apps with AI

A well-known tactic for figuring out how to identify the root cause of a problem that has caused an outage in a production environment is to go back and see what the environment has been doing so far. Through the analysis of logs, developers and operators alike can determine usage information that ideally reveal what’s wrong with a given application or how it can be improved to work better.

In the early days of logging, there wasn’t a great deal of activity going on, so it was possible for a human being (or two) to examine such logs and figure out what was up. It didn’t hurt that the logs were not only sparse in content, but also not terribly complicated in terms of what they reported. Alerts such as “Help, my processor is melting” really didn’t take a lot to figure out how to fix. Applications now are more distributed and that further complicates the situation. But over time, logs got far more voluminous and more detailed in what they were reporting.

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How Open Data Hub learners become the teachers

The prospect of true machine learning is a tangible goal for data scientists and researchers. It has been long known that the platform on which such ML apps can run have to be fast and hyper efficient so that learning can be that much faster. This is the motivation for Red Hat engineers in the Office of the CTO who are working to optimize such an open source platform: Open Data Hub.

Open Data Hub is built on Red Hat OpenShift Container Platform, Ceph Object Storage, and Apache Kafka/Strimzi integrated into a collection of open source projects to enable a machine-learning-as-a-service platform. That’s a lot of components to be integrated, and to ensure that their contributions to Open Data Hub perform well, Red Hat engineers have taken the step of creating an Internal Data Hub within Red Hat as a proving ground and learning environment.

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Machine Learning with Open Source Infrastructure

As machine learning becomes more interesting to technology companies, it is hardly surprising that a company like Red Hat is going to approach the challenges of this aspect of artificial intelligence with an open source methodology in mind.

The immediate benefits to open source machine learning tools are plain as day to anyone familiar with how open source works: lower cost, more flexibility, no vendor lock-in… you know, the usual.

But dig a little deeper and it quickly becomes apparent that open source means more for cutting-edge software than just a faster way to get cheaper software. 

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Exploring Unsupervised Deep Learning

The concept of artificial intelligence, which seemed so much like science fiction a few decades ago, has made real, practical inroads in producing results that organizations can find useful. What’s making those results happen, though, isn’t esoteric pie-in-the-sky theory: it’s creating statistical models that have been trained to make decisions. And trained a lot.

Artificial intelligence itself is a term that, for now, has had less of a focus than the more results-oriented machine learning, where a computer system is given input and output data and then is directed to infer the mathematical rules that govern the transformation of that data.

“It’s like pointing a program to look at the solar system and then have it figure out the laws of motion that govern a planetary system,” explained Sanjay Arora.

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Building a Scalable TensorFlow Twitter Bot for Red Hat Summit

Red Hat’s AI Center of Excellence and PerceptiLabs wanted a way to demonstrate a TensorFlow model to the public during the 2019 Red Hat Summit. The plan was for this model to take images as input, and then respond with the likelihood of a Red Hat fedora being in that image. Here’s what we learned during Red Hat Summit.

This application, which we called Fedora Finder Bot, would be featured during Red Hat CTO Chris Wright’s keynote, where PerceptiLabs demoed their AI platform.

Our initial solution for this objective would be a Twitter bot that receives tweets or direct messages and replies with the output from the TensorFlow model. Twitter being a public service, we felt it could make the model available to a large number of users, so that any user could just tweet to the bot with a picture and the bot would respond with the model’s output.

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Anomaly Detection on OpenStack Logs Using Machine Learning

(There’s a great  new conference in the U.S., DevConf.US, returning in 2019 to Boston University (15 to 17 Aug). This highly-technical conference is interested in drawing a diverse group of speakers and attendees, with a specific emphasis on people who are new to speaking and tech conferences in general. Only in its second year, DevConf.US builds on the successful decade-spanning run of DevConf.CZ in Brno, CZ.

This is a session from DevConf.US 2018. The call for proposals to present at DevConf.US 2019 is now open.)

In this session from the CentOS Dojo held as part of DevConf.US, OpenStack technical support engineers Madhur Gupta and Shatadru Bandyopadhyay talk about how to use machine learning for anomaly detection on OpenStack logs. Once an anomaly is detected in the logs, it can be used to automate further action, while helping in root cause analysis.

The challenge with anomaly detection in OpenStack in the first place is that it generates a significant quantity of logs, even in relatively simple production setups. How do you ingest and detect anomalies in all that data?

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Red Hat’s Open Source AI Vision

Analytics, Machine Learning, and AI represent a fundamental transformation that over the coming decade will affect every aspect of society, business, and industry. It will fundamentally change, how we interact with computers – and how we develop, maintain, and operate systems. It’s impact will be visible in our part of the universe much sooner than for the analog world. This deeply affects both open source in general, as well as Red Hat, its ecosystem, and customer base.

In this video from the inaugural DevConf.US 2018, Daniel Riek who leads the AI Center of Excellence in Red Hat Office of the CTO, talks about this coming change.

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Transforming IT Operations: A Roadmap

Digital transformation is more than just a fancy buzzword. With 85 percent of Global 2000 CEOs believing in digital innovation as a driver of business success, it is estimated that nearly $2.1 trillion will be invested in digital transformation technologies in 2019.

According to Mary Johnston Turner, Director, Management Software BU Evangelism,  the drivers to digital transformation are going to play a significant role in driving IT decision-making for the near-term future. Turner outlined the significant driving factors in her 2018 Summit breakout session “Transforming IT Ops: The future of IT automation & management.”

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