Specifically, the company updated Splunk Enterprise, Splunk Cloud, Splunk IT Service Intelligence (ITSI), Splunk User Behavior Analytics (UBA), and a new Experiment Management Interface for its Machine Learning Toolkit (MLTK).
Splunk said the new MLTK interface makes it easier to view, control, evaluate and monitor the status of machine learning experiments. The toolkit also includes new algorithms for identifying patterns and determining the best predictors for training machine learning models.
Splunk's algorithms are focused on investigations for security incidents, alerting, predictive tools for operations and maintenance, business optimization for demand, inventory, and analysis of historical data. Splunk says the machine learning advancements rely on these algorithms to help customers better monitor, investigate, and build intelligence with their data.
Meantime, Splunk also updated its User Behavior Analytics platform with new machine learning models and improvements to existing models that aim to help customers spot security threats more rapidly.
Splunk also expanded its integration capabilities with open source software and cloud-native technologies from Apache Kafka, Kubernetes and Docker.
"Splunk's new AI enhancements, including the ability to correlate metrics and activity data, enable customers to get answers from their machine data more efficiently,"said Splunk CTO Tim Tully. "Our latest wave of innovation is intended to arm customers with the tools needed to translate AI into actual intelligence."