az storage account create \
--name <your-storage-account-name> \
--resource-group <your-resource-group-name> \
--location <location> \
--sku Standard_LRS
The articles in the blog deals with implementing/Administration/Troubleshooting of SQL Server, Azure,GCP and Terraform I rarely write for a place to store my own experiences for future search but can hopefully help others along the way
az storage account create \
--name <your-storage-account-name> \
--resource-group <your-resource-group-name> \
--location <location> \
--sku Standard_LRS
The difference between Speech Recognition and Speaker Recognition lies in what they are trying to achieve. Let me break it down for you:
What it does:
Use Case:
Example:
What's the weather today?
, without caring about who said it.Azure Service:
What it does:
Use Case:
Two Types of Speaker Recognition:
Example:
Azure Service:
Aspect | Speech Recognition | Speaker Recognition |
---|---|---|
Purpose | Understand what is being said | Identify or verify who is speaking |
Focus | Converting speech to text | Recognizing the speaker’s identity |
Use Case | Virtual assistants, transcriptions | Voice-based authentication, security systems |
Azure Service | Speech-to-Text | Speaker Recognition API |
Example | Convert “Hello” to text | Identify if Alice said “Hello” |
Introduction: Generative AI is all about creating something new—whether it’s artwork, music, or synthetic data. However, the true power of generative AI lies in its ability to generate content that meets specific constraints and follows particular styles. This blog will help you understand the key concepts, styles, and constraints involved in generative AI, making it both powerful and practical. We will explore why these constraints and styles are important, practical use cases, and dive into techniques to easily remember the information. Whether you're an AI student or an aspiring architect, this guide has something valuable for you.
Table of Contents:
Introduction to Generative AI
Understanding Constraints in Generative AI
Styles in Generative AI Explained
Importance of Identifying Constraints and Styles
Real-World Use Cases of Constraints and Styles in AI
Azure Portal References for Generative AI
Practical Azure CLI Commands for Implementation
Memory Techniques for Easier Recall
Story-Based Technique
Conclusion
Generative AI involves machine learning models that create something new rather than simply identifying or classifying existing data. Common examples include chatbots like ChatGPT, image generators like DALL-E, and even tools for generating code. Generative AI is fundamentally creative, and its outputs can be customized with constraints and styles to suit specific needs.
Constraints in generative AI are like boundaries or rules that limit what can be generated. They guide the model to generate something specific instead of something random. Constraints can include things like:
A specific format (e.g., haiku instead of free verse).
Factual accuracy (e.g., keeping the facts correct in a summary).
Limitations on output length.
These constraints are essential for keeping the AI's output focused and useful.
Styles refer to the distinctive way the AI generates content. For instance, the model can generate text that imitates a famous author, or an image in a specific artistic style like cubism or realism. Style customization helps ensure that the AI's output aligns with the tone, formality, or aesthetics that the user wants.
Constraints and styles are crucial because they help generate relevant and appropriate content. Without constraints, the generated content may be too generic or even incorrect. Without style settings, the output might not match the user's needs or context. Identifying the proper constraints and styles ensures:
Accuracy in outputs like technical documents.
Brand consistency in generated marketing content.
Creative variety that matches the artistic requirements.
Healthcare Reports: Using constraints to ensure factual accuracy when generating patient data summaries.
Marketing: Using a particular brand style for creating social media posts.
Education: Creating educational content with constraints to match a specific curriculum.
Azure offers a powerful set of tools to implement generative AI. You can start with:
Azure OpenAI Service: To use GPT models for creating customized text outputs.
Azure Machine Learning: To build and deploy generative models.
Azure Cognitive Services: To infuse pre-built AI capabilities, like understanding constraints, into your app.
To use these services, log in to the Azure portal and search for "Azure OpenAI" or "Azure Machine Learning". You can set up and configure models directly from the portal.
Here are some practical commands you can use:
To create a resource group for generative AI models:
az group create --name generativeAIResources --location eastus
To deploy an Azure OpenAI service instance:
az cognitiveservices account create --name MyOpenAIService --resource-group generativeAIResources --kind OpenAI --sku S0 --location eastus
8. Memory Techniques for Easier Recall
Imagine you’re an art gallery curator (the AI) trying to create an art exhibition. You receive two sets of instructions:
Constraint: The art must be only landscapes.
Style: The art should be in the style of impressionism.
You use these instructions to select only landscape paintings that match the impressionist style. This process is similar to how generative AI models work with constraints and styles to generate the final output.
Generative AI has immense potential, but its true power comes from understanding and applying constraints and styles effectively. By mastering these concepts, you can ensure that AI-generated content meets your specific needs—whether in business, healthcare, education, or art. Azure provides comprehensive tools and services to get started, from Azure OpenAI to Azure Machine Learning. With the right approach, you can harness the creative capabilities of generative AI in a structured, useful manner.
Remember: The secret to successful generative AI isn’t just generating anything; it’s generating the right thing in the right way.
In the ever-evolving world of AI, facial recognition has become an essential component across industries like security, retail, and personalized customer experiences. Azure AI Face Service provides various tools to not only detect faces but also perform one-to-one and one-to-many face matching, along with detailed facial analysis. Understanding the difference between these tools—such as Face Identification, Face Verification, Face Attributes, Face Landmarks, and more—is key to deploying effective solutions.
In this blog, we’ll explore these tools in detail, highlight their unique purposes, and provide practical Azure references and commands for real-world applications.
Azure AI Face Service offers various facial recognition features, from face detection and identification to detailed facial attribute analysis. Whether you’re working with security systems, customer identification, or surveillance, Azure Face Service provides tools to match faces, verify identities, and extract facial details.
Here are the main tools and features that Azure Face Service provides for facial recognition and analysis:
Purpose: Face ID assigns a unique identifier to each detected face, allowing you to track and compare faces across multiple images or datasets.
How it Works: Once a face is detected, the system generates a unique Face ID. You can use this ID to match the face against a database of other Face IDs for identification.
Example: Imagine a visitor enters the art gallery. The system assigns a unique Face ID to that person, making it easy to recognize them if they return.
Purpose: Face Attributes provide detailed information about a person's face, such as whether they are wearing glasses, headwear, or have a specific emotion like smiling or frowning.
How it Works: The system analyzes key facial attributes and outputs relevant data points, allowing you to extract detailed characteristics about each face.
Example: A visitor in the gallery wears glasses and a hat. The system detects these facial attributes and records this information, making it possible to identify people based on their accessories or expressions.
Purpose: Face Landmarks detect key points on a person’s face, including the location of their eyes, nose, mouth, and ears.
How it Works: By identifying facial landmarks, the system can better align faces for emotion detection or for applying filters like in social media apps.
Example: The system detects the key facial landmarks of a visitor, helping to identify emotions or track specific points for further analysis.
Purpose: Face Rectangle draws a bounding box around a detected face in an image, helping the system determine the location and size of the face.
How it Works: It provides the coordinates of the detected face but does not analyze any facial features.
Example: The system recognizes a visitor’s face and draws a rectangle around it to indicate where the face is located in the image. This is useful for basic face detection in surveillance.
Purpose: Face Identification performs one-to-many face matching. It allows you to compare one face against a group or database of faces to find a match.
How it Works: When a new face is detected, the system compares its Face ID with other Face IDs in the database and identifies if a match is found.
Example: A VIP guest enters the gallery. The system compares their face with the faces stored in the VIP database and quickly identifies them.
Purpose: Face Verification performs one-to-one face matching. It compares two faces to check if they belong to the same person.
How it Works: The system checks if two Face IDs match, confirming whether the two faces are of the same person.
Example: A visitor shows their ticket, and the system verifies if the face on the ticket matches the visitor’s face.
Purpose: Find Similar Faces is used for one-to-many matching, focusing on finding visually similar faces in a group, even if it doesn’t find an exact match.
How it Works: It takes a detected face and compares it against a group of stored faces to find the closest match based on visual similarity.
Example: The gallery manager wants to find people who look similar to a particular visitor. The system compares the visitor’s face against a stored database of other visitors and returns the closest matches.
Let’s put these features into action by imagining you’re setting up a smart AI-based visitor tracking system for an art gallery. The system tracks visitors, identifies returning customers, and verifies VIPs for personalized experiences.
Now that you understand the tools, let’s look at how to use them on the Azure platform.
bashaz cognitiveservices account create --name <account-name> --resource-group <resource-group> --kind Face --sku S1 --location <location>
bashaz face identify --person-group-id <group-id> --face-id <face-id>
bashaz face verify --face-id1 <face-id1> --face-id2 <face-id2>
These commands will help you identify, verify, and track faces using the Azure CLI.
Azure AI Face Service offers a range of powerful tools for face detection, identification, verification, and analysis. Whether you need to recognize faces in a crowd, verify identities, or analyze facial features, understanding the purpose of each tool—Face ID, Face Attributes, Face Landmarks, Face Rectangle, Face Identification, Face Verification, and Find Similar Faces—is essential for building effective solutions. By integrating these tools with practical Azure commands and portal features, you can create scalable, real-world facial recognition systems that enhance security, personalization, and efficiency.
With this guide, you now have a comprehensive understanding of how Azure Face Service tools work and how to deploy them effectively in various applications.
To design an application that uses the Azure OpenAI REST API for a DALL-E model to generate images and display thumbnails on a webpage, we can break down the process step by step. This will include setting up the necessary Azure resources, interacting with the API, and displaying the image URLs from the result element in a table.
Before you create and use the OpenAI service, you’ll need a resource group to organize your resources.
dalle-images-rg
.dalle-images-rg
).dalle-openai-service
.After the Azure OpenAI service is created:
Now that you have your API key and Endpoint, you can start making requests to generate images using the DALL-E model.
Here’s an example cURL command to generate an image via the DALL-E model using the Azure OpenAI REST API:
bash
curl -X POST "https://<your-endpoint>.openai.azure.com/openai/deployments/<your-deployment-name>/images/generations?api-version=2022-12-01" \
-H "Content-Type: application/json" \
-H "api-key: <your-api-key>" \
-d '{
"prompt": "A futuristic city skyline at sunset",
"n": 2,
"size": "1024x1024"
}'
The name of the Azure OpenAI resource,
the name of the DALL-E 3 model deployment,
and the API version to be used are the three required header properties for HTTP requests
<your-endpoint>
, <your-deployment-name>
, and <your-api-key>
with your actual endpoint, deployment name, and API key from Step 3."prompt"
field is the description you want DALL-E to use to generate the image.-d
sends the JSON data (prompt
, n
, and size
) in the body of the POST request to the Azure OpenAI API.prompt
), how many images to generate (n
), and what size the images should be (size
).Without the -d
parameter, no data would be sent in the request body, and the API would not know what images you want to generate.
The API will return a JSON response that includes the URLs of the generated images. The image URLs are located in the result element.
Example JSON response:
json
{
"id": "generation-id",
"created": 1682950238,
"data": [
{
"result": {
"urls": [
"https://example.com/image1.png",
"https://example.com/image2.png"
]
}
}
]
}
result.urls
array.Once you’ve retrieved the image URLs, you can display them as thumbnails in a table on your webpage using HTML.
html
<!DOCTYPE html>
<html>
<head>
<title>Generated Images</title>
</head>
<body>
<h1>DALL-E Generated Thumbnails</h1>
<table border="1">
<tr>
<th>Thumbnail</th>
</tr>
<tr>
<td><img src="https://example.com/image1.png" alt="Image 1" width="100"></td>
</tr>
<tr>
<td><img src="https://example.com/image2.png" alt="Image 2" width="100"></td>
</tr>
</table>
</body>
</html>
https://example.com/image1.png
and https://example.com/image2.png
with the URLs from the result element in the JSON response.width="100"
ensures the image is displayed as a thumbnail.Once your webpage is ready, you can deploy it to a cloud platform like Azure Static Web Apps, GitHub Pages, or even host it locally for testing.
This will allow you to generate images using DALL-E and display them in a clean, user-friendly format on a webpage.
In today's fast-paced business environment, gaining insights from unstructured data such as customer reviews can be a game-changer. Azure AI Search offers an easy-to-implement, scalable solution for transforming raw data into meaningful information. In this blog, we will go through a detailed step-by-step guide to building an Azure AI Search solution, including setting up a storage account, creating a container, and uploading a JSON file to Azure Blob Storage.
We will also show how to use Azure AI Search skillsets to perform Sentiment Analysis and Key Phrase Extraction on customer reviews.
Azure AI Search allows you to index large datasets and apply AI-powered enrichments. With this service, you can build scalable and intelligent search solutions that provide actionable insights from your data.
In this example, we will take a dataset of customer reviews stored in Azure Blob Storage and use Sentiment Analysis and Key Phrase Extraction skills to enrich the data.
To start, you need a storage account where we will store the customer reviews in JSON format.
customerreviewsstorage
).bashaz storage account create --name customerreviewsstorage --resource-group <your-resource-group> --location eastus --sku Standard_LRS
Once your storage account is created, you need to create a container and upload the customer review data in JSON format.
customer-reviews
and set the public access level to Private.json
[
{
"customerId": "12345",
"review": "The delivery was fast and the service was excellent!",
"date": "2024-09-30"
},
{
"customerId": "67890",
"review": "The product was not as described, and delivery was delayed.",
"date": "2024-09-29"
}
]
To create a container and upload the JSON file using Azure CLI, use the following commands:
bash
# Create a container in your storage account
az storage container create --name customer-reviews --account-name customerreviewsstorage
# Upload the JSON file to the container
az storage blob upload --container-name customer-reviews --name reviews.json --file ./reviews.json --account-name customerreviewsstorage
Next, we’ll create an Azure AI Search service.
bashaz search service create --name <your-search-service-name> --resource-group <your-resource-group> --sku standard
Now, let’s define an index that will store the enriched customer review data.
customerId
(String, key field)review
(String, searchable)date
(DateTime, sortable and filterable)json
{
"name": "customer-reviews-index",
"fields": [
{ "name": "customerId", "type": "Edm.String", "key": true },
{ "name": "review", "type": "Edm.String", "searchable": true },
{ "name": "date", "type": "Edm.DateTimeOffset", "sortable": true, "filterable": true }
]
}
We will now create a Data Source that connects to the Azure Blob Storage where our customer review JSON file is stored.
bashaz search datasource create --name customer-reviews-datasource --service-name <your-search-service-name> --container-name customer-reviews --storage-account customerreviewsstorage
We will now create a Skillset that applies Sentiment Analysis and Key Phrase Extraction to our customer reviews.
json
{
"name": "customer-reviews-skillset",
"skills": [
{
"@odata.type": "#Microsoft.Skills.Text.SentimentSkill",
"name": "sentiment-analysis-skill",
"context": "/document/review",
"outputs": [
{
"name": "score",
"targetName": "sentimentScore"
}
]
},
{
"@odata.type": "#Microsoft.Skills.Text.KeyPhraseExtractionSkill",
"name": "keyphrase-extraction-skill",
"context": "/document/review",
"outputs": [
{
"name": "keyPhrases",
"targetName": "keyPhrases"
}
]
}
]
}
The Indexer will extract data from Blob Storage, apply the skillset, and store the enriched data in the index.
json
{
"name": "customer-reviews-indexer",
"dataSourceName": "customer-reviews-datasource",
"targetIndexName": "customer-reviews-index",
"skillsetName": "customer-reviews-skillset",
"schedule": { "interval": "PT2H" }
}
bashaz search indexer create --name customer-reviews-indexer --service-name <your-search-service-name> --datasource-name customer-reviews-datasource --target-index customer-reviews-index --skillset-name customer-reviews-skillset
Once the indexer runs, the customer reviews will be enriched with sentiment scores and key phrases. You can now query the index using the Azure AI Search REST API.
bash
POST https://<your-search-service-name>.search.windows.net/indexes/customer-reviews-index/docs/search?api-version=2021-04-30-Preview
Content-Type: application/json
api-key: <your-api-key>
{
"search": "*",
"filter": "sentimentScore gt 0.9",
"select": "customerId, review, sentimentScore, keyPhrases"
}
This query will return all customer reviews with a sentimentScore greater than 0.9.
In this blog, we walked through the complete process of setting up a search solution using Azure AI Search. We created a storage account, added customer reviews in JSON format, and enriched them with Sentiment Analysis and Key Phrase Extraction using Azure AI Search’s Skillset feature.
By following these steps, you can build a powerful search and insight-driven solution for your data. Explore further by adding more skills, such as language detection or custom AI models, to gain even deeper insights!
Feel free to try it out and expand on this solution to meet your specific business needs.
Title: Securely Access Secrets with Azure Key Vault: A Step-by-Step Guide
Azure Key Vault plays a critical role in securing sensitive data and secrets (like API keys, certificates, and passwords) by storing them securely in the cloud. Azure services like Virtual Machines (VMs) and Function Apps can securely access these secrets using managed identities, reducing the risk of accidental credential exposure.
In this blog, we will walk through the key concepts and practical steps to securely store and access secrets using Azure Key Vault, managed identities, and Azure Role-Based Access Control (RBAC). We will explore practical Azure CLI commands, coding examples, and how to implement security policies in Azure.
Azure Key Vault is a cloud-based service that allows you to securely store and access secrets, such as API keys, passwords, certificates, and other sensitive information. It ensures that the application secrets are not stored within the app, thus reducing security risks.
Let’s start by creating an Azure Key Vault using Azure CLI.
bash
# Step 1: Create a resource group
az group create --name MyResourceGroup --location eastus
# Step 2: Create an Azure Key Vault
az keyvault create --name MyKeyVaultName --resource-group MyResourceGroup --location eastus
Once the Key Vault is created, you can store your secrets securely, such as an API key.
bash
# Store a secret in the Key Vault
az keyvault secret set --vault-name MyKeyVaultName --name "MyAPIKey" --value "12345"
Managed Identity is a service that helps securely access resources without the need to manage credentials. Let’s enable managed identity on an Azure Virtual Machine or Function App.
bash
# Enable Managed Identity on a Virtual Machine
az vm identity assign --name MyVM --resource-group MyResourceGroup
In this step, we will set access policies to allow the Virtual Machine or Function App to access the secrets stored in the Key Vault.
bash# Set access policy to allow VM to get and list secrets
az keyvault set-policy --name MyKeyVaultName --resource-group MyResourceGroup --object-id <VM's Object ID> --secret-permissions get list
To access secrets from Azure Key Vault using a Python script, follow the example below. This script uses DefaultAzureCredential
to authenticate and retrieve a secret.
python
from azure.identity import DefaultAzureCredential
from azure.keyvault.secrets import SecretClient
# Key Vault URL
key_vault_url = "https://MyKeyVaultName.vault.azure.net/"
# Authenticate and retrieve the secret
credential = DefaultAzureCredential()
client = SecretClient(vault_url=key_vault_url, credential=credential)
# Access the secret
retrieved_secret = client.get_secret("MyAPIKey")
print(f"Secret value: {retrieved_secret.value}")
To remember the steps of working with Azure Key Vault, use the mnemonic "Create-Store-Manage-Access":
Imagine you are a treasure hunter and the Azure Key Vault is your secure treasure chest. To access it:
Let’s say you are developing an Azure Function that calls a third-party API using an API key. Instead of hardcoding the API key in your code (which is risky), you store the key in Azure Key Vault and access it securely using a managed identity. This way, even if your code is shared, the API key is never exposed, ensuring strong security for your application.
Azure Key Vault, combined with managed identity and Azure RBAC, provides a secure, scalable, and efficient solution to store and manage sensitive data. Following the steps and best practices discussed, you can safeguard secrets while enabling your applications to securely access them without managing credentials. Whether you're using Virtual Machines, Function Apps, or any other service, Azure Key Vault simplifies security management in the cloud.