Mock sample for your project: Security Center API

Use this API to manage the IoTSpaces service instances in your Azure subscription.

ARM Databricks

Creates an Azure Data Lake Analytics job client.

Personalizer Service is an Azure Cognitive Service that makes it easy to target content and experiences without complex pre-analysis or cleanup of past data. Given a context and featurized content, the Personalizer Service returns which content item to show to users in rewardActionId. As rewards are sent in response to the use of rewardActionId, the reinforcement learning algorithm will improve the model and improve performance of future rank calls.

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.

Azure Blueprints Client provides access to blueprint definitions, assignments, and artifacts, and related blueprint operations.

The service is used to perform ink layout and recognition of written words and shapes. Ink strokes passed to the service are recognized and organized into recognition results in the response

The HDInsight Job Client.

The Azure Data Catalog Resource Provider Services API.

The Admin Storage Management Client.

AWS Well-Architected Tool This is the AWS Well-Architected Tool API Reference. The AWS Well-Architected Tool API provides programmatic access to the AWS Well-Architected Tool in the AWS Management Console. For information about the AWS Well-Architected Tool, see the AWS Well-Architected Tool User Guide.

Amazon DevOps Guru is a fully managed service that helps you identify anomalous behavior in business critical operational applications. You specify the AWS resources that you want DevOps Guru to cover, then the Amazon CloudWatch metrics and AWS CloudTrail events related to those resources are analyzed. When anomalous behavior is detected, DevOps Guru creates an insight that includes recommendations, related events, and related metrics that can help you improve your operational applications. For more information, see What is Amazon DevOps Guru. You can specify 1 or 2 Amazon Simple Notification Service topics so you are notified every time a new insight is created. You can also enable DevOps Guru to generate an OpsItem in AWS Systems Manager for each insight to help you manage and track your work addressing insights. To learn about the DevOps Guru workflow, see How DevOps Guru works. To learn about DevOps Guru concepts, see Concepts in DevOps Guru.

Amazon Managed Service for Prometheus

The Azure SQL Database management API provides a RESTful set of web APIs that interact with Azure SQL Database services to manage your databases. The API enables users to create, retrieve, update, and delete databases, servers, and other entities.

You use the API to scan your content as it is generated. Content Moderator then processes your content and sends the results along with relevant information either back to your systems or to the built-in review tool. You can use this information to take decisions e.g. take it down, send to human judge, etc.
When using the API, images need to have a minimum of 128 pixels and a maximum file size of 4MB.
Text can be at most 1024 characters long.
If the content passed to the text API or the image API exceeds the size limits, the API will return an error code that informs about the issue.

The Azure Data Catalog Resource Provider Services API.

The Container Service 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.

The Container Service Client.

The Azure Storage Management API.

Creates an Azure Data Lake Analytics catalog client.