Machine Learning Toolkit Blogs

Splunk DLTK supports Docker as well as Kubernetes and OpenShift as container environments. In this article, we will go through the setup for using DLTK 3.3 and Amazon EKS as a kubernetes environment.

A guided walk through of how to choose the best Splunk ML workflow for your needs!

In this blog, we deploy Splunk’s built-in Streaming ML algorithms to detect anomalous patterns in error logs in real-time. Breaking it down into simple steps, we walk you through how to use out-of-the box Splunk capabilities to ingest logs, pre-process the data, apply real-time ML, and visualize results.

We're diving into three interesting new algorithmic approaches available with the latest version 3.4 of the Deep Learning Toolkit (DLTK) App for Splunk.

In this blog, we’re going to look at how causal inference can be used to understand in more detail what the biggest influencing factors are across a dataset.

In this blog, we will describe how to apply feature engineering to some example datasets taken from higher education organisations, which can then be used to generate predictive models in the Smart Education Insights app.

This blog outlines how we have turned our #EUvsVirus project into reality with the new Smart Education Insights App for Splunk.

We’re thrilled to announce that we have packaged the new app to simplify ticket remediation with ML-Powered Analysis – the Smart Ticket Insights App for Splunk – and it is now live on Splunkbase! Find out more about it here.

Built on top of the Splunk Machine Learning Toolkit (MLTK), Smart Workflows are designed to easily guide you through the process of developing a machine learning model with a few click-and-select options - regardless of your prior experience with model building. Learn more about them in this blog post.