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.

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.

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.

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.

You can transform your search box into your sales rep—when you have the right tools. An impactful customer experience that drives purchases and loyalty isn't just about delivering what a customer says they want — it's about predicting and proactively serving up what they need. We might be able to imagine this work in a store with salespeople. But as organizations scale and customer interactions happen across digital and in-person mediums, their data grows.

Sometimes a hat is just a hat, the truth is just the truth, and the clearly most popular example of a category is plain to see. In this case, Ubuntu is the most popular Linux distribution currently available. With the operating system’s superior popularity also comes an amazing amount of community support.

Netdata’s new Anomaly Advisor feature lets you quickly identify potentially anomalous metrics during a particular timeline of interest. This results in considerably speeding up your troubleshooting workflow and saving valuable time when faced with an outage or issue you are trying to root cause.

Superwise is a monitoring platform that provides model observability for high-scale machine learning (ML) operations. Superwise provides teams with out-of-the-box (OOTB) metrics on their models’ production behavior, so they can effectively address drift, data quality issues, and other problems before they negatively impact business.