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Machine Learning

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How Is Machine Learning Used In AIOps?

When we think of computers, we typically think in terms of exactness. For example, if we ask a computer to do a numeric calculation and it gives us a result, we are 100% sure that the result is correct. And if we write an algorithm and it gives an incorrect result, we know we have coded improperly and it needs to be corrected. This exactness however, is not the case when dealing with Machine Learning. As a matter of fact, it is par for the course, that Machine Learning will be incorrect a percentage of the time.

How Netdata's Machine Learning works

Following on from the recent launch of our Anomaly Advisor feature, and in keeping with our approach to machine learning, here is a detailed Python notebook outlining exactly how the machine learning powering the Anomaly Advisor actually works under the hood. Or if you’d rather watch a video walkthrough of the notebook then check out below. Try it for yourself, get started by signing in to Netdata and connecting a node.

How Netdata's machine learning works

In this video we will walk though the Netdata Anomaly Advisor deepdive python notebook. The aim of this notebook is to explain, in detail, how the unsupervised anomaly detection in the Netdata agent actually works under the hood. No buzzwords, no magic, no mystery :) Try it for yourself, get started by signing in to Netdata and connecting a node. Once initial models have been trained (usually after the agent has about one hour of data, zero configuration needed), you'll be able to start exploring in the Anomaly Advisor tab of Netdata.

Debunking 4 Cybersecurity Myths About Machine Learning

Machine learning has infiltrated the world of security tooling over the last five years. That’s part of a broader shift in the overall software market, where seemingly every product is claiming to have some level of machine learning. You almost have to if you want your product to be considered a modern software solution. This is particularly true in the security industry, where snake oil salesmen are very pervasive and vendors typically aren’t asked to vigorously defend their claims.

PagerDuty and Arize: Integrations for ML Observability

Arize is an ML Observability platform aimed to detect, troubleshoot, and eliminate ML problems faster. Use Arize to monitor your production models and send alerts to PagerDuty when your models deviate from a certain threshold. Arize and Pagerduty help keep your teams in the loop, send more comprehensive metadata through alerts, and debug your models faster than ever before.

Using Grafana and machine learning to analyze microscopy images: Inside Theia Scientific's work

At GrafanaCONline 2022, Theia Scientific President, Managing Member, and Lead Developer Chris Field and Volkov Labs founder and CEO Mikhail Volkov — a Grafana expert — delivered a presentation about using Grafana and machine learning for real-time microscopy image analysis. Real-time microscopy image analysis involves capturing images on a microscope using a digital device such as a PC, iPad, or camera.

Machine Learning At The Forefront Of Telemental Health

Michael Stefferson received his PhD in Physics from the University of Colorado before deciding to make the jump into machine learning (ML). He spent the last several years as a Machine Learning Engineer at Manifold, where he first started working on projects in the healthcare industry. Recently, Stefferson joined the team at Cerebral as a Staff Machine Learning Engineer and hopes to leverage data to make clinical improvements for patients that will improve their lives in meaningful ways.

Arize integration with PagerDuty

Streamline Model Monitoring with Integrated Alerts Arize is an ML Observability platform aimed to detect, troubleshoot, and eliminate ML problems faster. Use Arize to monitor your production models and send alerts to PagerDuty when your models deviate from a certain threshold. Arize and PagerDuty help keep your teams in the loop, send more comprehensive metadata through alerts, and debug your models faster than ever before.

AI vs. Machine Learning vs. Deep Learning vs. Neural Networks: What's the Difference?

The continuous debate around artificial intelligence (AI) has led to a lot of confusion. There are many terms around it that appear to be similar, but when you take a closer look at them, that perception is not entirely accurate. For that reason, here we take our best shot and oppose AI vs. machine learning vs. deep learning vs. neural networks to set them apart once and for all. In short, we’ll look at how they all relate to each other, and what makes them different in their particular way.

Continuous Training and Deployment for Machine Learning (ML) at the Edge

Running machine learning (ML) inference in Edge devices close to where the data is generated offers several important advantages over running inference remotely in the cloud. These include real-time processing, lower cost, the ability to work without connectivity and with increased privacy.