Mock sample for your project: MonitorManagementClient API

API spec for Microsoft.Security (Azure Security Center) resource provider

An API for QnAMaker Service

These APIs allow end users to operate on Azure Machine Learning Workspace resources.

APIs for managing software update configurations.

IP pool operation endpoints and objects.

The Microsoft Azure management API provides create, read, update, and delete functionality for Azure MariaDB resources including servers, databases, firewall rules, VNET rules, log files and configurations with new business model.

Azure Enterprise Knowledge Graph Service is a platform for creating knowledge graphs at scale.

Azure Event Hubs client

Azure Application Insights client for Annotations for a component.

Logical subnet operation endpoints and objects.

The Container Service Client.

Azure Maintenance Management Client

The Azure Customer Insights management API provides a RESTful set of web services that interact with Azure Customer Insights service to manage your resources. The API has entities that capture the relationship between an end user and the Azure Customer Insights service.

The Disk Resource Provider Client.

Azure Cosmos DB Database Service Resource Provider REST API

The Container Service Client.

The Admin Backup Management Client.

The Anomaly Detector API detects anomalies automatically in time series data. It supports two kinds of mode, one is for stateless using, another is for stateful using. In stateless mode, there are three functionalities. Entire Detect is for detecting the whole series with model trained by the time series, Last Detect is detecting last point with model trained by points before. ChangePoint Detect is for detecting trend changes in time series. In stateful mode, user can store time series, the stored time series will be used for detection anomalies. Under this mode, user can still use the above three functionalities by only giving a time range without preparing time series in client side. Besides the above three functionalities, stateful model also provide group based detection and labeling service. By leveraging labeling service user can provide labels for each detection result, these labels will be used for retuning or regenerating detection models. Inconsistency detection is a kind of group based detection, this detection will find inconsistency ones in a set of time series. By using anomaly detector service, business customers can discover incidents and establish a logic flow for root cause analysis.

Amazon CloudWatch monitors your Amazon Web Services (Amazon Web Services) resources and the applications you run on Amazon Web Services in real time. You can use CloudWatch to collect and track metrics, which are the variables you want to measure for your resources and applications. CloudWatch alarms send notifications or automatically change the resources you are monitoring based on rules that you define. For example, you can monitor the CPU usage and disk reads and writes of your Amazon EC2 instances. Then, use this data to determine whether you should launch additional instances to handle increased load. You can also use this data to stop under-used instances to save money. In addition to monitoring the built-in metrics that come with Amazon Web Services, you can monitor your own custom metrics. With CloudWatch, you gain system-wide visibility into resource utilization, application performance, and operational health.

API for using the Azure IoT Hub Device Provisioning Service features.