Splunk continues to turn to machine learning to differentiate its platform of monitoring tools from rivals, and deliver valuable insights to customers.
Machine data specialist Splunk is looking to do more of the heavy lifting when it comes to helping customers analyse and act on their ever-increasing amounts of data by continuing to add machine learning capabilities into its range of solutions.
Richard Campione, Splunk’s new chief product officer said: “Machine learning is critical to customer success and to the evolution of Splunk. Our seamlessly integrated capabilities open up machine learning to everyone, enabling our customers to better predict future outcomes and more efficiently analyse their data.”
New features were announced at Splunk’s annual .conf2017 user conference in Washington D.C. this week. The company announced it is incorporating machine learning into its platform the same time last year in Orlando, promising customers automated anomaly and pattern recognition, smarter alerting and predictive actions.
Since then its rival New Relic, which focuses on application performance monitoring, has launched a range of AI-features for its platform.
As well as these machine learning capabilities, Splunk has tweaked its metrics engine for the 7.0 release of its Enterprise platform and Splunk Cloud, allowing users to collect, explore, visualise and publish insights 20 to 200 times faster than before, according to Campione.