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In this article, we will explain what is AIOps, its advantages and in the specific case of Azure Platform what services are available for its adoption and implementation.
According to Gartner: “AIOps is the application of machine learning and data science to IT operations. AIOps platforms combine big data and ML functionality to enhance and partially replace all primary IT operation functions, including availability and performance monitoring, event correlation and analysis, and IT service management and automation.”
Although you may think the term AIOps is something new, in reality, it has been used for several years in many virtualization and cloud platforms, in the specific case of Azure, Microsoft has special AIOps teams that collaborate and work together with other Microsoft research teams that develop AI solutions for the management and operation of cloud services.
What is AIOps in simple terms?
AIOps stand for Artificial Intelligence for IT Operations or Artificial Intelligence Operations Services and these systems can help companies and organizations by using these technologies to monitor, detect and proactively resolve issues in their IT systems more efficiently.
Basically, AIOps systems can predict problems using advanced machine learning algorithms and data analysis to automate the detection and resolution of problems in complex technological environments.
What kind of problems can we solve with AIOps?
The AIOps platforms enabling continuous insights across IT Operations Monitoring (ITOM) to solve problems related to situations like detection of anomalies, diagnosis of causes, prediction of behaviours and optimization.
This table shows an overview of these problems:
| PROBLEMS | CHALLENGES | TECHNIQUES |
| Detection | Diverse requirements, noisy data, high dimensions, lack of labelled data, … | Time-series anomaly detection Log-based anomaly detection Multi-dimensional change detection… |
| Diagnosis | Diverse causes, complex service dependency, scattered knowledge, … | Log patter mining Correlation analysis Dependency graph diagnosis… |
| Prediction | Highly imbalance class, fast system evolution, unpredictable behavior changes, … | Context/dependency-aware prediction Automated feature engineering Extremely-imbalanced data prediction… |
| Optimization | Huge problem space, large scale data, complex constraints and tradeoffs, … | Multi- constraints/objective optimization DL-based combinatorial search Optimization under prediction uncertainty… |
Ref: Cloud Intelligence/AIOps – Infusing AI into Cloud Computing Systems – Microsoft Research
From a technical point of view, applying AIOps and all the power of machine learning with data collection and metrics from Azure services, you can, for example, identify problems and predict what will happen in the future and improve user experience.
Some of the advantages when we apply AIOps are:
- Increase predictability
- Early detection of anomalies
- Root cause identification
- Enhance troubleshooting efficiency
- Improve system reliability
- Improve customer experience
- Facilitate collaboration
- Regulatory compliance
- Faster time to resolution:
- Proactive capacity management
- Risk mitigation
- Continuous monitoring and learning:
- Performance optimization:
- Prioritization of efforts
- Confidence in change management
- Improved incident response
AI Metrics Advisor and Azure Monitor
In the specific case of the Azure platform, we can use services like Azure Monitor and Metrics Advisor that allow us to detect and analyze anomalies with alerts and intelligent recognition of metric patterns.
With Azure AI Metrics Advisor, powered by AI Anomaly Detector and now part of Azure Applied AI Services with the new experience of Metrics Advisor Studio we can incorporate AI-driven monitoring capabilities to proactively manage incidents without the need for expertise in machine learning.
Some of these capabilities and features are:
- Anomaly detection
- Anomaly alerts
- Data ingestion
- Data exploration
- Root cause analysis
- Real time monitoring
- Predictive analysis
- Diagnostics and insights
- Cost Optimizations
With these features collectively make Azure AI Metrics Advisor a powerful tool for monitoring, analysing, and optimizing various aspects of an organization’s operations.
You can see AI Metrics in action here: Metrics Advisor Demo
On the other hand, with Azure Monitor you can detect and mitigate potential issues using AIOps and machine learning given that Azure Monitor comes equipped with integrated AIOps functionalities that offer insights and automate data-driven tasks, including forecasting capacity utilization and auto-scaling, pinpointing and analysing application performance concerns, and identifying unusual behaviours in virtual machines, containers, and various resources.
These capabilities enhance your IT monitoring and operations without necessitating expertise in machine learning or additional investments.
You can check some of these built-in AIOps capabilities here:
- Application Map Intelligent view
- Application Smart detection
- Detect anomalies using KQL time series analysis and ML functions
- Dynamic thresholds for metric alerting
- Log Analytics Insights
- Predictive autoscale
References
How To Get Started with AIOps (gartner.com)
Azure AI Metrics Advisor | Microsoft Azure
Advancing Azure service quality with artificial intelligence: AIOps | Microsoft Azure
Announcing Azure Monitor AIOps Alerts with Dynamic Thresholds | Microsoft Azure
AIOps and machine learning in Azure Monitor – Azure Monitor | Microsoft Learn
Cloud Intelligence/AIOps – Infusing AI into Cloud Computing Systems – Microsoft Researchopendatascience.com/aiops-with-azure-metrics-advisor/
