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.