Splunk investing in AI upgrades across portfolio
Splunk has announced expanded artificial intelligence (AI) capabilities across its product portfolio to help customers boost their profitability, performance and security.
Splunk has also expanded integration capabilities with open source software and cloud-native technologies as part of its ongoing commitment to provide a true, open machine data platform for customers.
“Organisations frequently consume high amounts of staff time and resources to monitor, analyse and respond to IT operational alerts, says Splunk chief technology officer Tim Tully.
“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. 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.”
Splunk Cloud and Splunk Enterprise 7.1 deliver AI through machine learning to help customers monitor, search and alert on the critical information organisations need to accelerate their business.
These latest releases include an updated metrics engine to power customers’ ability to monitor and alert on numeric data points - from CPU speeds and available hard disk space in a complex IT environment, to temperature readings in Internet of Things (IoT) devices and sensors.
According to Splunk, the latest versions are also the only enterprise-class data analytics solutions that can ingest petabytes of data per day, as well as search, monitor and alert on that data in real time.
With these enhancements, users are better positioned to make sense of their machine data to predict future IT, security and business outcomes.
To expand its open technology ecosystem, Splunk introduced new data integrations with open source software projects and cloud-native technologies including Apache Kafka and Kubernetes.
Splunk is also announcing a new experiment management interface for its Machine learning Toolkit (MLTK) hoping to make it easier to view, control, evaluate and monitor the status of machine learning experiments.
The latest Splunk MLTK also includes new algorithms for identifying patterns and determining the best predictors for training machine learning models.
New solutions focused on the IoT and other use cases are helping Splunk customers solve defined challenges.
Splunk Essentials series are free, easy-to-install applications that show users exactly how to use Splunk Enterprise and Splunk Cloud to address mission-critical use cases.
Splunk Security Essentials covers use cases such as brute force detection, malware and compliance, while Splunk Security Essentials for Fraud Detection covers use cases such as healthcare insurance billing and wire transfer fraud.