What is Azure Search?
Azure Search is an artificial intelligence (AI)-powered cloud search service that enables full-text searches within documents and images. It’s a managed service, and we’ll show you how to implement a search engine in a few steps. 
In addition to the ease of creating and managing the service, one of the main advantages is being able to integrate with other Azure services immediately. In this post, we will create a search engine and integrate with Azure Cognitive Services (specifically Vision API), all through the Azure Portal in a few minutes.
For an introductory tour of Azure Storage Solutions, check out Cloud Academy’s Introduction to Azure Storage Solutions. This intermediate-level course covers SQL offerings (SQL DB and third party offerings of MySQL), managed NoSQL databases (DocumentDB and MongoDB), managed Redis Cache service, Azure Backup (backup-as-a-service), Site Recovery (for handling disaster recovery), and StorSimple (a hybrid cloud storage solution).

To search for text on images and documents with Azure Search, here are the simple steps that we will follow: 

Create a Storage Account
Create a search engine
Test the operation of our service
Understand service considerations

1. Create a Storage Account
The first step for our cognitive search engine is to create a storage account in Azure, in which we will store the files we want to analyze.

We create our storage account by entering the requested parameters:

Once our account is created, within the Blob Storage section, we will create a container that will store our files.

To close this stage, and do the respective tests, we can search the internet for several documents and images that will be objects of our analysis and we will store them in our new container. For this case, we will upload the following image:

2. Create a search engine
Our storage account is already ready, but we must enable our search service with Azure Search.
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