Splunk leverages AI in its monitoring tools

Just weeks after the announcement of Splunk IAI (Industrial Asset Intelligence), Splunk has revealed it will be enhancing machine learning across many of its products. This includes Splunk Enterprise, IT Service Intelligence, and User Behavior Analytics. Clearly, the company are using Spring 2018 as a period to build a solid foundation to future-proof their products.

Splunk has also added an 'Experiment Management Interface' to its Machine Learning Toolkit. This is a crucial update that will make tracking machine learning and AI 'experiments' much easier. It means that monitoring a range of issues will become much easier.

Splunk's goal here is to ensure a reduction in what it calls "event noise." The machine learning and AI algorithms will help to cut through the amount of data and information at users' disposal. It will allow them to identify the issues that are most business-critical. It's about more than just analytics - it's about the additional dimension that makes prioritization much more straightforward. That's what distinguishes what Splunk are doing compared to competitors. Typically, machine learning in BI software allows users to monitor issues, but doesn't have the capacity to place issues in a wider business context.

There are a wide range of applications for this technology. It could be used to identify security issues within a given system, application performance, or even operational management. Tim Tully, CTO, had this to say:

"Our latest wave of innovation is intended to arm customers with the tools needed to translate AI into actionable intelligence. While AI and machine learning often seem like unattainable and expensive pipe dreams, Splunk Cloud and Splunk Enterprise now make it easier and more affordable to monitor, analyze and visualize machine data in real time"

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Of course, while Tully's words contain an element of marketing-speak, made for a press release, it's worth noting that the goal here from Splunk's perspective is all about making AI and machine learning more accessible. Clearly the company knows what their customers want. This suggests, then, that for all the discussion around the machine learning revolution, there are still many businesses that regard machine learning as a considerable challenge.