Update 2024-10-21-alerts.md

Author: kimsauce

blog-service/2024-10-21-alerts.md

@@ -14,20 +14,12 @@ import useBaseUrl from '@docusaurus/useBaseUrl';
 
 <a href="https://help.sumologic.com/release-notes-service/rss.xml"><img src={useBaseUrl('img/release-notes/rss-orange2.png')} alt="icon" width="50"/></a>
 
-We are excited to announce the general availability of AI-driven alerts for metrics anomalies. This release extends our AI-driven alerting capabilities, previously available for logs-based monitors, to metrics-based monitors, enabling you to respond to critical incidents more effectively.
+We're excited to announce the general availability of AI-driven alerts for metrics anomalies, extending our AI-driven alerting to metrics-based monitors. This release helps reduce alert fatigue and enables faster incident resolution with automated playbooks.
 
-## Benefits
+### Key Features
 
-- **Reduces alert fatigue**. AI-driven anomaly detection minimizes irrelevant alerts, allowing customers to focus on critical incidents.
-- **Faster incident resolution**. Automated playbooks enable customers to reduce the mean time to resolution by automating repetitive tasks and gathering key diagnostic information.
+* **Advanced anomaly detection**. Uses 30 days of historical metrics data to establish baselines and detect critical anomalies.
+* **Customizable detection**. Configure detection based on specific criteria, like multiple anomalous data points within a time window.
+* **Playbook integration**. Automate responses by linking playbooks to streamline diagnosis and recovery.
 
-## Key features
-
-- **Advanced anomaly detection**. A built-in machine learning model analyzes at least 30 days of historical metrics data to establish baseline behavior, detect seasonality, and automatically tune detection parameters. This helps reduce noise from spikes while isolating critical issues.
-- **Configurable detection framework**. The "Cluster anomalies" detector allows users to configure incident detection, such as marking an incident when a defined number of anomalous datapoints occur within a time window.
-- **Playbook integration**. Customers can automate incident diagnosis and resolution workflows by associating playbooks with metrics monitors. Playbooks streamline responses by gathering diagnostic data, notifying collaborators, and automating recovery tasks.
-
-
-<!-- learn more link
-docs/alerts/monitors/create-monitor.md
--->
+[Learn more](/docs/alerts/monitors/create-monitor)