With great choice comes great responsibility. One of the most frequent questions we encounter when speaking about anomaly detection is how do I choose the best approach for identifying anomalies in my data? The simplest answer to this question is one of the dark arts of data science: Exploratory Data Analysis (EDA).

Given the current circumstances and the pressure medical staff and hospitals are facing in general, access to information is now more critical than ever. Optimising the process of medical exams and enabling alerts and notifications in real-time has become essential.

Splunk can be used to ingest machine-learning service information from services like AWS recognition, what does that look like and how can you set it up?

Splunk is providing in-product guidance right at your fingertips to help you accomplish your goals without navigating away from the product. Learn more in this blog post.

Get a sneak peek on two private beta products — AR app for HoloLens, a solution for a hands-free experience, and a Splunk SDK to allow you to securely incorporate Splunk data into your custom apps.

This blog is the first in a mini-series of blogs where we aim to explore and share various aspects of our security team’s mindset and learnings. In this post, we will introduce you to how our own security and threat research team develops the latest security detections using ML.

One of the new necessities we came across several times was that the clients were willing to get a sport bets fraud risk scoring model to be able to quickly detect fraud. For that purpose, I designed a data pipeline to create a sport bets fraud risk scoring model based on anomaly detection algorithms built with Probability Density Function powered by Splunk’s Machine Learning Toolkit.

To help our customers extract the most value from their IT Service Intelligence (ITSI) deployments, Splunker Greg Ainslie-Malik created this blog series. Here he presents a number of techniques that have been used to get the most out of ITSI using machine learning.

How can you use statistical analysis to identify whether you have an unusual number of events, and how can similar techniques be applied to non-numeric data to see if descriptions and sourcetype combinations appear unusual? Read all about it in this blog.