AIOps on Azure: The Basics and a Quick Azure Monitor Tutorial

What Is AIOps? 

AIOps, or artificial intelligence for IT operations, refers to the application of machine learning and data science to automate and enhance IT operations. Systems ingest and analyze large volumes of telemetry, logs, events, and metrics from infrastructure, networks, and applications. These insights help teams detect anomalies, pinpoint root causes, remediate issues, and optimize resources without manual intervention. By combining big data, machine learning algorithms, and automation, AIOps improves incident response, performance monitoring, and capacity planning.

The primary objective of AIOps is to improve the reliability, efficiency, and agility of IT operations in increasingly complex environments. As deployments expand across cloud, hybrid, and on-premises infrastructure, traditional monitoring tools struggle to correlate events and identify actionable insights. AIOps addresses these challenges by delivering adaptive, self-learning systems that proactively prevent incidents, reduce mean time to resolution (MTTR), and support business continuity.

In this article:

• Typical AIOps Use Cases in Azure Deployments
• Key Azure Services That Enable AIOps
• Tutorial: Using Azure Monitor Issues and Investigations for AIOps 

Typical AIOps Use Cases in Azure Deployments 

Anomaly Detection and Proactive Alerting

AIOps platforms embedded in Azure monitor cloud infrastructure and application metrics at scale for unusual patterns that indicate potential issues. By applying machine learning models to time-series data, these systems can detect deviations from normal behavior more accurately than static thresholds. For example, sudden spikes in CPU utilization, response time, or failure rates are automatically flagged for investigation. This ensures that emerging problems are surfaced quickly, even for previously unseen failure modes or subtle anomalies.

Proactive alerting is a key advantage, enabling operations teams to respond before users experience disruptions. Azure AIOps solutions suppress alert noise by correlating related events and prioritizing incidents based on severity, impact, and historical context. The system can even suggest recommended next steps or automate notifications via integration with ITSM tools, so stakeholders are informed without manual triage.

Application Performance Monitoring and Root-Cause Analysis

AIOps tools support deep application performance monitoring by ingesting data from Azure App Insights, distributed tracing, and log sources. Machine learning models identify relationships between events, dependencies, and performance bottlenecks across services. When an incident occurs, AIOps surfaces probable causes by clustering outlying behaviors and tracking causal chains. This shortens investigation time, reducing guesswork when diagnosing database latency, slow API calls, or cascading failures across microservices.

Root-cause analysis accelerates resolution by presenting all relevant context within a single pane. AIOps systems visualize dependencies, code commits, deployments, configuration changes, or upstream incidents that contributed to the degradation. Operations teams can quickly navigate evidence, assign tasks, and implement targeted fixes, minimizing both downtime and the operational burden on engineers.

Resource/Capacity Forecasting and Autoscaling

Capacity planning in the cloud requires predicting workload trends and scaling resources efficiently. AIOps uses historical usage patterns and predictive analytics to forecast demand for compute, storage, and network throughput in Azure environments. Accurate predictions prevent both over-provisioning—which wastes cloud spend—and under-provisioning, which leads to performance degradation. The models continuously retrain as new data streams in, adapting to seasonal shifts, feature launches, or traffic bursts.

Autoscaling policies become more intelligent with AIOps. Instead of reacting to single point-in-time metrics, autoscaling actions are triggered based on predictive signals and anomaly detection. For example, a forecasted surge in user activity can preemptively trigger new VM or container instances, ensuring high availability and smooth user experience. This closed feedback loop between prediction and automated action brings cloud resource management closer to true autonomy.

Automated Incident Management and Remediation

AIOps extends beyond monitoring by automating incident management lifecycles. When Azure systems detect anomalies or service health issues, automated workflows classify, enrich, and route incidents with minimal manual effort. Integration with runbooks, serverless functions, or automation tools enables the system to attempt predefined remediations, such as restarting services or rolling back deployments, before escalating to human operators.

Automated remediation reduces response times and operational toil. Failed remediations or recurring incidents are logged, analyzed, and incorporated into continuous learning systems for better outcomes over time. AIOps not only accelerates resolution of predictable problems but also enables teams to focus on higher-value engineering work by taking repetitive response actions off their plate.

Key Azure Services That Enable AIOps 

Azure Monitor Logs and Metrics Pipelines

Azure Monitor aggregates logs and metrics from nearly all Azure services, infrastructure, and connected applications. Data is streamed through a robust pipeline that ensures high-availability and scaling for large environments. Sources include agent-based VM telemetry, platform metrics, application logs, and diagnostic traces. Azure Monitor normalizes, enriches, and indexes this telemetry so downstream systems—including AIOps tools—can query, analyze, and correlate events in real-time.

Having a single source of truth is vital for effective anomaly detection and automation. Monitor’s integration with various data sources—including on-premise and other cloud providers through API ingestion—broadens the scope of monitoring and enables centralized AIOps governance. Well-structured pipelines facilitate flexible data retention, cost control, and support for custom metrics, making it feasible to run machine learning analyses across diverse operational data.

Log Analytics Workspaces and Data Models

The foundation of Azure’s analytics capabilities lies in Log Analytics workspaces. These centralized repositories store structured and unstructured telemetry gathered across Azure resources. Within a workspace, data is organized using schemas, custom tables, and Kusto Query Language (KQL), making it straightforward to parse, search, and analyze logs at scale. Advanced queries and aggregations uncover patterns, anomalies, or outliers relevant to AIOps use-cases.

Log Analytics provides the substrate for building and training machine learning models on operational data. By correlating signals from different applications, environments, and time periods, teams can establish baselines and develop adaptive alerting rules. Flexible data models make it easier to tie tuning parameters and organizational context (such as tags, ownership, or tiers) directly to operational insights.

Azure Metrics Advisor and Anomaly Detection Features

Azure Metrics Advisor is an AI-powered service for scalable anomaly detection on time-series data, with a focus on operational metrics and business KPIs. Built on Azure Cognitive Services, it applies statistical and machine learning algorithms to differentiate meaningful anomalies from noise. Metrics Advisor handles seasonality, trend shifts, and multi-dimension correlations, reducing false positives in high-volume environments.

With easy integration into existing Azure Monitor pipelines and support for custom metrics, Metrics Advisor enables real-time monitoring with minimal configuration. Actionable alerts, root-cause visualizations, and incident grouping streamline the remediation process. Teams can deploy its features to monitor everything from infrastructure health to business transactions, giving AIOps systems a robust core for intelligent event detection.

Integration with Azure Resource Health and Service Topology

Integration with Azure Resource Health allows AIOps solutions to monitor the real-time status of Azure resources, providing granular insights beyond basic availability checks. Resource Health tracks conditions like degraded performance, planned maintenance, and platform outages, offering rich data for incident correlation and capacity planning. When combined with service topology views, this information enables more precise root-cause analysis by identifying how problems propagate across dependencies.

Service topology mapping visually connects resources, networks, and applications, giving operators a holistic understanding of operational risk and failure domains. AIOps algorithms use this context to prioritize alerts, suppress noise from symptomatic incidents, and deliver remediations that target underlying causes rather than surface symptoms. The result is faster resolution, better incident response, and greater resilience for Azure workloads.

Tutorial: Using Azure Monitor Issues and Investigations for AIOps 

Azure Monitor issues and investigations (preview) introduce AIOps capabilities to streamline alert triage and problem resolution. These features use an AI-powered observability agent that automates issue creation, root-cause analysis, and guided troubleshooting for Azure resources. 

This tutorial walks through the key components, workflows, and usage patterns to incorporate issues and investigations into your AIOps strategy. Instructions are adapted from the Azure documentation.

Understanding Issues and Investigations

• An issue is a structured representation of a service-related problem. It aggregates related alerts, diagnostics, and telemetry to provide a centralized view of a potential incident. Each issue includes metadata like severity, status, and impact time, and is linked to one or more resources.
• An investigation is an AI-driven analysis performed by the observability agent. It scans telemetry from up to two hours prior to the issue’s impact time to identify anomalies and generate findings—summarized insights that highlight potential causes and next steps. Each finding includes supporting evidence such as metric deviations, log anomalies, resource changes, and related alerts.

Triggering an Investigation

There are two primary ways to start an investigation:

• From the Azure Portal: Navigate to Monitor > Alerts, select an alert, then choose Investigate (preview).
• From an Alert Email: Alert notifications include a link to Investigate, which opens the Azure portal, creates an issue, and automatically starts the investigation.

To run an investigation, the subscription hosting the resource must be associated with an Azure Monitor Workspace (AMW). If not already configured, you’ll be prompted to select or create an AMW during your first investigation.

Example: Using the REST API to Associate an AMW

You can associate a subscription with an Azure Monitor Workspace using the Azure Management REST API. This is required to enable issue creation and investigations for resources in that subscription.

Create or update an association:

GET https://management.azure.com/subscriptions/<subscription_id>/providers/microsoft.monitor/settings/default?api-version=2025-06-03-preview
Host: management.azure.com
Authorization: Bearer <bearerToken>

View an existing association:

DELETE https://management.azure.com/subscriptions/<subscription_id>/providers/microsoft.monitor/settings/default?api-version=2025-06-03-preview
Host: management.azure.com
Authorization: Bearer <bearerToken>

Interpreting Investigation Results

Once the observability agent completes its analysis, up to five findings are displayed:

• Summary: A plain-language description of the issue.
• Explanation: The likely cause based on observed anomalies.
• Next Steps: Suggested actions for deeper analysis or remediation.
• Supporting Data: Evidence including logs, metrics, exceptions, diagnostic insights, and related alerts.

For application-level problems, the observability agent surfaces log-based findings such as failed transactions, exception patterns, and trace messages. Each includes transaction examples and problem IDs, with explanations to guide further investigation.

AIOps on Azure with Selector

Selector is an AIOps platform that helps organizations analyze operational signals across complex hybrid environments, including Microsoft Azure deployments. By ingesting telemetry from Azure services, applications, infrastructure, and network systems, Selector enables operations teams to correlate signals across domains and investigate incidents with greater context.

Azure environments often generate large volumes of telemetry through services such as Azure Monitor, Log Analytics, and Application Insights. While these services provide valuable operational data, troubleshooting incidents across distributed cloud systems can still require significant manual investigation. Selector addresses this challenge by correlating signals from Azure telemetry pipelines alongside data from other infrastructure and observability systems.

Key capabilities include:

• Cross-domain event correlation: Selector analyzes alerts, metrics, logs, configuration changes, and topology data simultaneously. By identifying relationships between events, the platform groups related signals into a unified incident context, helping teams focus on underlying causes instead of investigating individual alerts.
• Operational digital twin for Azure environments: Selector builds and maintains a continuously updated model of infrastructure relationships, including dependencies between services, resources, and network paths. This operational digital twin allows teams to visualize how incidents propagate across Azure services and connected systems.
• Context-aware anomaly detection: Machine learning models detect unusual behavior across Azure workloads while incorporating operational context such as dependencies and topology relationships. This reduces false positives and helps surface meaningful anomalies earlier.
• Accelerated root cause analysis: By correlating telemetry across Azure services, applications, infrastructure, and network systems, Selector helps teams quickly identify the most likely source of an incident and reduce Mean Time to Resolution (MTTR).
• Natural-language operational queries: Selector Copilot allows engineers to query operational data using plain English through collaboration tools such as Slack or Microsoft Teams. Teams can explore incidents, dependencies, and telemetry without manually navigating multiple dashboards.

By combining Azure telemetry with cross-domain correlation and AI-powered investigation capabilities, Selector helps organizations improve incident response, reduce alert noise, and maintain reliable performance across cloud and hybrid environments.

Selector is helping organizations move beyond legacy complexity toward clarity, intelligence, and control. Stay ahead of what’s next in observability and AI for network operations: 

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