Mock sample for your project: ApplicationInsightsManagementClient API

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

The HDInsight Management Client.

The Admin Commerce Management Client.

Scale unit node operation endpoints and objects.

Fabric location operation endpoints and objects.

The Admin Compute Disk Management Client.

Infrastructure role operation endpoints and objects.

Deployment Admin Client.

Deployment Admin Client.

This is the Amazon Rekognition API reference.

AzureBridge Admin Client.

Amazon Personalize is a machine learning service that makes it easy to add individualized recommendations to customers.

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

Drive operation endpoints and objects.

The Admin Fabric Management Client.

AWS Server Migration Service AWS Server Migration Service (AWS SMS) makes it easier and faster for you to migrate your on-premises workloads to AWS. To learn more about AWS SMS, see the following resources: AWS Server Migration Service product page AWS Server Migration Service User Guide

AWS Elemental MediaPackage VOD

The Application Migration Service service.

Amazon Mobile Analytics is a service for collecting, visualizing, and understanding app usage data at scale.

IoT Analytics allows you to collect large amounts of device data, process messages, and store them. You can then query the data and run sophisticated analytics on it. IoT Analytics enables advanced data exploration through integration with Jupyter Notebooks and data visualization through integration with Amazon QuickSight. Traditional analytics and business intelligence tools are designed to process structured data. IoT data often comes from devices that record noisy processes (such as temperature, motion, or sound). As a result the data from these devices can have significant gaps, corrupted messages, and false readings that must be cleaned up before analysis can occur. Also, IoT data is often only meaningful in the context of other data from external sources. IoT Analytics automates the steps required to analyze data from IoT devices. IoT Analytics filters, transforms, and enriches IoT data before storing it in a time-series data store for analysis. You can set up the service to collect only the data you need from your devices, apply mathematical transforms to process the data, and enrich the data with device-specific metadata such as device type and location before storing it. Then, you can analyze your data by running queries using the built-in SQL query engine, or perform more complex analytics and machine learning inference. IoT Analytics includes pre-built models for common IoT use cases so you can answer questions like which devices are about to fail or which customers are at risk of abandoning their wearable devices.