Tag: Machine Learning Toolkit

In this blog we will talk about some strategies for monitoring your models in ITSI for model drift. This is the idea that the predictive models will become less accurate over time as the rules that were generated originally no longer match the data they are applied to.

Get a personal view from a new PM on the Splunk machine learning (ML) team. We touch on the experience of being a totally remote new-hire and first impressions of the ML portfolio.

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).

During .conf20 we presented alongside BMW Group the way a predictive testing strategy can enable better process efficiency in automotive manufacturing. We also introduced briefly which machine learning tools and analytical techniques were useful within the given situation.

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.

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.

As we are trying to commoditize machine learning through our MLTK smart workflows, this article outlines another example of an MLTK smart workflow, designed to help improve the usability of the predictive capabilities in ITSI.

In this blog post, we’ll explore an ML-powered solution using the Splunk Machine Learning Environment to detect fraudulent credit card transactions in real time. Using out-of-the-box Splunk capabilities, we’ll walk you through how to ingest and transform log data, train a predictive model using open source algorithms, and predict fraud in real-time against transaction events.

An introduction to SMLE Labs and a showcase of the various ML capabilities at a high level by walking you through the environment, step-by-step.