Many of you are familiar with Splunk’s Machine Learning Toolkit (MLTK) and the Deep Learning Toolkit (DLTK) for Splunk and have started working with either one to address security, operations, DevOps or business use cases. A frequently asked question that I often hear about MLTK is how to organize the data flow in Splunk Enterprise or Splunk Cloud.

As soon as the ITOps technician is ready to grab a cup of coffee, a zing comes along as an alert. Cling after zing, the technician has to respond to so many alerts leading to fatigue. The question is why can’t systems be smart enough to predict bugs and fix them before sending an alert to them. And, imagine what happens when these ITOps personnel have to work with a complex and hybrid cloud of IT systems and applications. They will dive into alert fatigue.

The artificial neuron was first hypothesized in the 1930s, but only in the last decade have we seen the widespread application of artificial neural networks and machine learning to everyday technologies. Broadly speaking, machine learning describes a technical discipline defined by computer algorithms that improve automatically through experience and the use of data. These days, the combination of machine learning and "big data" power an increasing number of digital tools that we interact with daily.

As we’ve shown in a previous blog, search-based detection rules and Elastic’s machine learning-based anomaly detection can be a powerful way to identify rare and unusual activity in cloud API logs. Now, as of Elastic Security 7.13, we’ve introduced a new set of unsupervised machine learning jobs for network data, and accompanying alert rules, several of which look for geographic anomalies.

Kubeflow is the open-source machine learning toolkit on top of Kubernetes. Kubeflow translates steps in your data science workflow into Kubernetes jobs, providing the cloud-native interface for your ML libraries, frameworks, pipelines and notebooks. Read more about Kubeflow

Building successful machine learning (ML) production systems requires a specialized re-interpretation of the traditional DevOps culture and methodologies. MLOps, short for machine learning operations, is a relatively new engineering discipline and a set of practices meant to improve the collaboration and communication between the various roles and teams that together manage the end-to-end lifecycle of machine learning projects.

We’re excited to share that the Deep Learning Toolkit App for Splunk (DLTK) is now available in version 3.6 for Splunk Enterprise and Splunk Cloud. The latest release includes: Let’s get started with the new operational overview dashboard which was built using Splunk’s brand new dashboard studio functionality which I highly recommend checking out. You can learn more about it in this recent tech talk which you can watch on demand.