Splunk is continuing to invest in machine learning capabilities to help customers detect and react to their machine data faster than before, touting enhancements across its suite of products for IT and security pros.
The new AI features have been added to all of the core Splunk products, including: Splunk Enterprise, Splunk Cloud, Splunk IT Service Intelligence (ITSI), Splunk User Behaviour Analytics (UBA) and the Splunk Machine Learning Toolkit.
In Splunk Cloud and Splunk Enterprise 7.1 the vendor has added an updated metrics engine to help monitor and alert on numeric data points, such as CPU speeds, available hard disk space and readings from IoT devices.
The monitoring and analytics solution Splunk ITSI has also been upgraded with AI tools for predicting outages and machine learning to reduce event noise and prioritise events that could make the most impact on business performance.
The Splunk UBA updates "include new machine learning models and enhancements to existing models to help identify and address time-sensitive security problems and insider threats more quickly," the vendor says.
The machine learning toolkit (MLT) has also been updated to include a new 'experiment management' interface to view, control, evaluate and monitor the status of machine learning experiments, as well as new pre-packaged models for pattern recognition and determining the best predictors for training machine learning models.
Splunk also announced that it's enhancing integrations with open source software and cloud-native technologies such as Kafka, Kubernetes and Docker.
"Splunk Connect for Kafka will improve our investigation of web activity, performance and security use cases," said John Swanson, security incident response manager, GitHub. "We're now able to ingest large, near-real-time data streams and are consuming terabytes of logs from our Kafka cluster into Splunk Enterprise every day."
This all comes a few weeks after Splunk announced its first IoT specific solution too, called Industrial Asset Intelligence, which uses a lot of machine learning to help customers do predictive maintenance on assets.
The changes are effective immediately, so customers should be able to play with the new features straight away.
The vendor started talking about bringing machine learning capabilities across its platform as far back as September 2016, including automated anomaly and pattern recognition, smarter alerting and predictive actions into ITSI, ES and UBA, as well as launching a free machine learning toolkit.
Now customers are leveraging these features to better automate and mitigate anomalies in their machine data.
Jonathan Silberlicht, senior director, network service management at T-Mobile said as part of the press release: “With Splunk Enterprise we can ensure our customers get the best experience possible when they’re activating a phone, making a call or paying a bill."
“For example, with the new Splunk Connect for Kafka, we are expanding our real-time analytics capabilities, in turn empowering our front line to make better informed decisions when serving customers. We plan to continue to rely on Splunk Enterprise, Splunk IT Service Intelligence and Splunk Machine Learning to help us scale at un-carrier speed.”
Then there is Hyatt hotels, where Cesar Mendoza, application development manager, strategic systems and innovation said: “Hyatt uses machine learning in Splunk Enterprise to predict when and where we should act fast or plan differently to best serve our customers.”
“We used the free Splunk Machine Learning Toolkit to benchmark typical Wi-Fi usage from customers across hotel sites, and used that baseline to spot low traffic. We immediately contacted our wireless service provider to correct connection issues before our customers had to call us. We’re using artificial intelligence through Splunk to more proactively serve our customers in this way every day.”