Data downtime is highly costly for organizations and causes significant problems for immediate and downstream teams when a data ingestion pipeline breaks because of a bug, misconfiguration, traffic spike, network issue or malicious attack. Applying Machine Learning to your data in Splunk can help you find these warning signs in advance and enable your team to address potential issues before there’s a consequential impact.
In this webinar, we’ll showcase how machine learning can be used to improve the ‘getting data in’ experience with Splunk. Specifically, we will demonstrate how to set up an automated alerting system that detects unexpected downtimes or spikes in your data ingestion volumes.
Learn how to:
Leverage the power of machine learning search commands with SPL and MLTK
Set up an anomaly detection model for monitoring data inputs
Build a dashboard & implement alerting to enable real-time monitoring