A technical overview of the Splunk App for Anomaly Detection, which uses machine learning to automatically configure anomaly detection jobs on time series data.

Along with the respective Splunk Enterprise version 9.1.0 and Splunk Cloud Version 9.0.2305 releases, Ingest Actions has launched a new set of features and capabilities that improve its usability and expand on configurability of data routed by Ingest Actions to S3.

At .conf23, we announced the preview release of Splunk AI Assistant - Splunk's first offering powered by generative AI.

On the heels of an exciting GA in March and the April announcement of its regional expansion, we are excited to share the latest updates to Splunk Edge Processor that will make it even easier for customers to have more flexibility and control over just the data you want, nothing more nothing less.

Splunk App for Anomaly Detection simplifies ML, making anomaly detection easy. It streamlines tasks, enabling ML integration in everyday workflows. Just load data, select the field, and click "Detect Anomalies."

With the proliferation of edge computing and the release of Splunk Edge Hub, partners have additional functionality to accelerate the detection, investigation and response of threats and issues that will inevitably occur in physical and industrial environments.

The Splunk App for Data Science and Deep Learning (DSDL) now has two new assistant features for Natural Language Processing. DSDL has been offering basic natural language processing (NLP) capabilities using the spaCy library.

The new Maintenance Dashboard in the Cloud Monitoring Console app aims to assist Splunk Cloud Platform admins in effectively managing maintenance tasks and staying informed about Splunk-initiated maintenance for improved operational efficiency.

We’ll explore how to monitor and manage your SVCs, and share tips for enhancing SVC usage.