In this era, machine learning is important. Machine learning helps in business Management operations and understanding customer behaviors. It also helps in the development of new products. Every leading company is shifting towards machine learning. Companies like Amazon, Facebook, Google, and of course Nastel Technologies, prioritize machine learning as their central part. Let’s see how 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.

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.

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.

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.

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.

The Machine Learning team at Splunk has been hard at work over the last several months preparing for a few exciting launches at.conf22, held just a few weeks ago. Splunk customers want to leverage machine learning (ML) in their environments, but many aren’t sure how to use it, or even how to get started.

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.

Machine learning has garnered a lot of attention in the past few years. The reason behind this might be the high amount of data from applications, the ever-increasing computational power, the development of better algorithms, and a deeper understanding of data science. We have already talked about artificial intelligence (AI) in a previous blog post. In this opportunity, we will learn about machine learning, what it is and how it works with examples and ITSM applications.