Splunk turns to machine learning to help customers get the most from their operational data

Splunk turns to machine learning to help customers get the most from their operational data

Splunk continues to turn to machine learning to differentiate its platform of monitoring tools from rivals, and deliver valuable insights to customers

Doug Merritt  - Splunk CEO

Doug Merritt - Splunk CEO

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.

Read next: New Relic unveils Applied Intelligence service that detects and resolves software issues using AI

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.

So while there are already smart alerting and predictive features in these products, Splunk found the scale customers were running at meant that improvement to its data ingestion capability was a priority this year.

More specifically, new machine learning features have been incorporated across the rest of Splunk's portfolio, so Splunk IT Service Intelligence (ITSI) version 3.0, Splunk User Behaviour Analytics (UBA) version 4.0 have all been upgraded with some machine learning capabilities.

The new features include:

● Splunk IT service intelligence (ITSI) 3.0: A new machine learning feature which automatically identifies potential issues and prioritises restoration of business-critical services by analysing dependencies and events.

● Splunk user behaviour analytics (UBA) 4.0: The new version UBA enables data scientists to write and load their own machine learning algorithms into the platform to generate custom anomalies and threats to better monitor for insider attacks or new threats. This capability is available through Spunk's new software developers kit (SDK).

● Splunk Machine Learning Toolkit: Splunk updated the Machine Learning Toolkit it launched for data scientists to build algorithms that predict future IT, security and business issues last year. Recent updates to the toolkit include a visual interface for creating and managing models, public APIs for custom algorithms, a new data preparation tool and a Spark integration. The toolkit is still in private Beta.

● Splunk Essentials for anti-fraud: Splunk has launched a new free app to help users identify and investigate anomalies that may signal different types of fraud.

● Splunk Insights for AWS cloud monitoring: Provides customers with analytics for monitoring their AWS cloud usage. This feature is available through the Amazon Marketplace as an Amazon Machine Image (AMI).

● Splunk Insights for ransomware: Splunk Insights for ransomware is an offering priced per user that provides organisations with real-time insights for proactive assessment and rapid investigation of potential ransomware threats.

Read next: Splunk brings machine learning capabilities into its tools and launches toolkit for customer's own algorithms

Splunk also announced two new products now in preview at .conf2017:

● Project Waitomo: A new infrastructure monitoring solution that "unifies logs and metrics, delivering integrated machine learning for alerts, trends and investigation".

● Project Nova: An API-based logging-as-a-service offering, targeting developers and DevOps practitioners.

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