Create a solution that uses Azure Metrics Advisor, part of Azure Applied AI Services

Azure Metrics Advisor is a powerful tool that enables users to monitor and analyze metrics data in real-time. As part of the Azure Applied AI Services, it provides valuable insights into the health and performance of applications, services, and systems. In this article, we will explore how to create a solution using Azure Metrics Advisor and highlight its key features and capabilities.

Understanding Azure Metrics Advisor

Azure Metrics Advisor is an AI-powered service that helps in detecting, diagnosing, and fixing anomalies in your time series data. It leverages advanced algorithms and machine learning techniques to identify abnormal patterns and provides meaningful recommendations for resolving these issues.

Getting Started

To use Azure Metrics Advisor, you need to follow these steps:

1. Create a metrics advisor resource in the Azure portal.
2. Configure the resource to connect with your data sources, such as Application Insights, Azure Monitor, Azure Data Explorer, and more.
3. Create detection configurations to define the anomalies and anomalies severity that you want to track.
4. Start ingesting data into Metrics Advisor for analysis.

Throughout these steps, you can customize the configurations based on your specific needs.

Creating a Metrics Advisor Client

To interact with Azure Metrics Advisor, you need to create an instance of the `MetricsAdvisorClient` class from the Azure SDK. Here’s an example of how to do it in Python:

from azure.ai.metricsadvisor import MetricsAdvisorClient

# Create a Metrics Advisor client
client = MetricsAdvisorClient(endpoint="https://.cognitiveservices.azure.com/", credential=AzureKeyCredential(""))


Make sure to replace `` with the name of your Metrics Advisor service, and `` with your actual API key.

Creating a Detection Configuration

To track the anomalies you want to monitor, you can create a detection configuration. Here’s an example of how to create a detection configuration to monitor CPU usage in Azure Monitor:

from azure.ai.metricsadvisor.models import DetectionCondition, MetricAnomalyAlertConfig

# Define the detection configuration name and description
detection_config = client.create_detection_config(
name="CPU Usage",
metric_ids=[""],
description="Detect anomalies in CPU usage data",
alert_configuration=metric_alert_config
)


In this example, make sure to replace `` with the actual ID of the metric you want to monitor.

Ingesting Data

To perform anomaly detection, you need to ingest data into Metrics Advisor. Here’s an example of how to ingest data from a CSV file using the Python SDK:

from azure.ai.metricsadvisor.models import DataFeedSchema, DataFeedIngestionSettings, DataFeedSource, \
AzureApplicationInsightsDataFeedSource, IngestionStatus

file_path = ""
client.ingest_from_file(data_feed_id=data_feed.id, file_path=file_path)

Make sure to replace ``, ``, ``, and `` with the actual values.

Conclusion

Azure Metrics Advisor is a valuable tool that enables you to monitor and analyze metrics data with ease. With its intelligent anomaly detection and root cause analysis capabilities, you can quickly identify and resolve issues to ensure the smooth functioning of your applications and systems. By integrating Azure Metrics Advisor into your workflow, you can harness the power of AI to improve operational efficiency and deliver a better user experience.