Root cause analysis empowers you to prevent issues from recurring that were revealed by your monitoring IT systems and online applications including eCommerce sites. See Elastic engineers walk you through applying four AIOps capabilities and accelerate MTTR by automatically categorizing logs, explaining log rate spikes, visually inspecting anomalous components in their context, and correlating slow or failed transactions with potential root causes.

Comprehensive security requires multiple layers of threat protection. Sophisticated threats exploit idiosyncrasies in your environment. Unsupervised machine learning identifies patterns of normal activity from your data, and therefore can catch attacks that standard approaches to threat hunting, such as pre-defined rules, are likely to miss. This video explains how machine learning adds a layer to your threat protection, and how interactive tools offered in the Elastic Security solution accelerate the investigation of security incidents.

With digitization adopted in many industries, real-time data from manufacturing and operational equipment can be used to monitor and optimize operation - by applying data-driven modeling including machine learning. Learn how you can ingest sensor data from industrial processes and operational equipment into Elastic, build monitoring dashboards and set up automated alerts in Kibana, and apply predictive modeling to optimize your operations (OT).

Watch this video for a detailed walkthrough on instrumenting the pet clinic app with Elastic Observability''s Java APM agent on Azure App Service.

Learn how to add enriched information (new fields) to help analysis from ingested log data in Elastic Observability.

Log files and system logs have been a treasure trove of information for administrators and developers for decades. But with more moving parts and ever more options on where to run modern cloud applications, keeping an eye on logs and troubleshooting problems have become increasingly difficult. Watch this video to learn how to go beyond tail -f and process custom and unstructured logs with Elastic.

Elastic's machine learning helps support several easy-to-use features to help determine root cause analysis for logs. This includes anomaly detection and log categorization, which are easy-to-use features aiding in analysis without the need to understand or know about machine learning.

Monitoring business revenues and managing change in revenue with Elastic Machine Learning anomaly detection.

Finding matches when using a taxonomy is a common problem. A notable challenge is mapping a user’s query to the entity (or results) expected when searching for an entity inside a catalog mapping. Functional textual search models tend to rely on exact match or partial match, but both can lead to a frustrating experience when users aren’t familiar with the domain. Basic models often fail to support user typos, synonyms, acronyms, and/or hyponyms/hypernyms. Learn how to tackle these challenges and make search more intuitive when using a taxonomy.

The new Elasticsearch Query Language is a flexible, powerful, and robust query expression language to interrogate data. In this session learn how ESQL provides a superior query UX, a piped query language with join capabilities that fundamentally transforms and expands the analytics and data processing of Elasticsearch.