Mastering AI Development with Azure: A Comprehensive Guide to Creating and Integrating Plugins

Azure offers a rich set of tools and services for developing AI applications, from pre-built models to custom solutions. This guide will walk you through the essentials of building AI solutions on Azure, creating plugins, and leveraging various Azure services. We will provide coding examples that can serve as templates for both beginners and professionals.

Introduction to AI on Azure

Overview of Microsoft Azure

Microsoft Azure is a comprehensive cloud computing platform that provides a wide range of services for building, deploying, and managing applications. Azure’s AI capabilities are extensive, offering both pre-built and customizable solutions to meet various business needs. These services are designed to cater to different levels of expertise, making it easier for both beginners and professionals to develop AI applications.

Key AI Services and Tools Offered by Azure
1. Azure Machine Learning:
— A cloud-based service for building, training, and deploying machine learning models.
— Supports popular frameworks like TensorFlow, PyTorch, and scikit-learn.
— Features include automated machine learning (AutoML), MLOps for model lifecycle management, and integration with other Azure services.

2. Cognitive Services:
— A collection of APIs and SDKs for adding AI capabilities to applications without needing deep machine learning expertise.
— Categories include Vision, Speech, Language, and Decision.
— Services like Computer Vision, Text Analytics, Speech-to-Text, and Translator fall under this umbrella.

3. Azure Databricks:
— An Apache Spark-based analytics platform optimized for Azure.
— Facilitates big data analytics and machine learning.
— Provides collaborative notebooks and integrates with Azure Machine Learning for model training and deployment.

4. Azure Synapse Analytics:
— An integrated analytics service for big data and data warehousing.
— Combines data integration, data exploration, and data visualization capabilities.
— Supports advanced analytics and machine learning with built-in integration for Azure Machine Learning.

5. Azure Bot Services:
— A platform for building conversational AI applications and chatbots.
— Integrates with Cognitive Services for natural language understanding and generation.

6. Azure OpenAI Service:
— Provides access to powerful language models, such as GPT-3, for tasks like text generation, summarization, and translation.
— Enables developers to integrate advanced natural language processing capabilities into their applications.

Benefits of Using Azure for AI Development
1. Scalability:
— Azure provides scalable infrastructure that can handle large datasets and complex computations, making it suitable for both small projects and enterprise-level applications.

2. Integrated Ecosystem:
— Azure’s AI services are well-integrated with other Azure tools and services, providing a seamless experience for end-to-end development and deployment.

3. Ease of Use:
— Services like Cognitive Services and AutoML reduce the barrier to entry for AI development, allowing developers to leverage advanced AI capabilities without deep expertise.

4. Security and Compliance:
— Azure ensures robust security measures, including data encryption, identity and access management, and compliance with various regulatory standards.

5. Cost Management:
— Azure’s pay-as-you-go pricing model allows developers to manage costs effectively and scale resources as needed.

6. Support and Community:
— Extensive documentation, support resources, and a vibrant community make it easier to find help and share knowledge.

Setting Up Your Azure Environment

Step 1: Creating an Azure Account

1. Visit the Azure Website:
— Go to the Microsoft Azure website.
— Click on the “Start free” button.

2. Sign Up or Log In:
— If you already have a Microsoft account, you can log in with your existing credentials.
— If not, create a new Microsoft account.

3. Set Up Your Subscription:
— Azure offers a free tier with $200 in credits for the first 30 days and free services for 12 months.
— You will need to provide billing information, but you won’t be charged until your free credit is exhausted or you opt for a paid plan.
— Follow the on-screen instructions to set up your subscription.

Step 2: Creating and Managing Resources
1. Azure Portal:
— The Azure Portal is the primary interface for managing your Azure resources.
— Navigate to the Azure Portal and sign in with your Microsoft account.

2. Resource Groups:
— Resource groups help you organize and manage related Azure resources.
— To create a resource group, click on “Resource groups” in the left-hand menu, then click “Add”.
— Provide a name and select a region for your resource group, then click “Review + create” and “Create”.

3. Creating Resources:
— You can create various resources like virtual machines, storage accounts, and databases within your resource group.
— Click on “Create a resource” and select the type of resource you want to create.
— Follow the on-screen instructions to configure and deploy the resource.

Step 3: Setting Up Your Development Environment
1. Install Azure CLI:
— Azure CLI is a command-line tool for managing Azure resources.
— Download and install the Azure CLI for your operating system.
— Open a terminal or command prompt and run `az login` to authenticate with your Azure account.

2. Set Up Visual Studio Code:
— Visual Studio Code (VS Code) is a popular code editor with excellent support for Azure development.
— Download and install Visual Studio Code.
— Install the Azure extensions for VS Code:
— Azure Account
— Azure CLI Tools
— Azure App Service
— Azure Functions
— Azure Storage
— Azure Machine Learning

3. Configure Authentication:
— Set up service principals or managed identities for secure authentication and authorization of your applications.
— Create a service principal using Azure CLI:

az ad sp create-for-rbac --name "myServicePrincipal" --role contributor \
    --scopes /subscriptions/{subscription-id}/resourceGroups/{resource-group} \
    --sdk-auth

- Save the output, which includes credentials and authentication details, to a secure location.

Step 4: Exploring Azure DevOps
1. Azure DevOps:
— Azure DevOps provides a set of development tools for continuous integration and continuous deployment (CI/CD).
— Navigate to the Azure DevOps site and sign in with your Microsoft account.

2. Creating a Project:
— Create a new project in Azure DevOps to manage your code repositories, build pipelines, and release pipelines.
— Click on “New Project”, provide a name and description, and click “Create”.

3. Repositories and Pipelines:
— Use Azure Repos to manage your Git repositories.
— Set up build and release pipelines using Azure Pipelines to automate the deployment of your applications.

Azure AI and Machine Learning Services

Overview

Microsoft Azure provides a comprehensive suite of AI and machine learning services that cater to a wide range of applications and expertise levels. These services enable developers to build, train, and deploy AI models with ease. In this chapter, we will explore the key AI and machine learning services available on Azure, with a particular focus on Azure OpenAI.

Key AI and Machine Learning Services
1. Azure Machine Learning:
— Description: A cloud-based environment for training, deploying, and managing machine learning models.
— Features: Automated machine learning (AutoML), MLOps for lifecycle management, support for popular frameworks (TensorFlow, PyTorch), and integration with other Azure services.

2. Azure Cognitive Services:
— Description: A collection of APIs and SDKs for embedding AI capabilities into applications without requiring deep AI expertise.
— Categories: Vision (e.g., Computer Vision, Face API), Speech (e.g., Speech-to-Text, Text-to-Speech), Language (e.g., Text Analytics, Translator), and Decision (e.g., Personalizer).

3. Azure Databricks:
— Description: An Apache Spark-based analytics platform optimized for Azure.
— Features: Collaborative notebooks, integrated machine learning environment, and seamless integration with Azure Machine Learning for model training and deployment.

4. Azure Synapse Analytics:
— Description: An integrated analytics service that combines big data and data warehousing.
— Features: Data integration, exploration, and visualization capabilities, along with advanced analytics and machine learning support.

5. Azure Bot Services:
— Description: A platform for building, deploying, and managing conversational AI applications and chatbots.
— Features: Integration with Cognitive Services for natural language understanding and generation.

6. Azure OpenAI Service:
— Description: Provides access to powerful language models developed by OpenAI, such as GPT-3, for tasks like text generation, summarization, translation, and more.
— Features: Pre-trained models, easy integration via API, and support for custom fine-tuning.

Focus on Azure OpenAI Service
The Azure OpenAI Service allows developers to leverage the power of OpenAI’s language models in their applications. These models can understand and generate human-like text, making them suitable for a wide range of applications, from chatbots to content generation.

Key Features of Azure OpenAI Service:

1. Access to Advanced Models:
— Access to state-of-the-art models like GPT-3, which can perform various natural language processing tasks.

2. Pre-trained and Custom Models:
— Use pre-trained models for common tasks or fine-tune models on your own datasets for specialized applications.

3. Simple Integration:
— Easily integrate with applications through RESTful APIs.
— Support for popular programming languages and frameworks.

4. Scalability and Reliability:
— Hosted on Azure’s scalable and reliable infrastructure, ensuring high availability and performance.

5. Security and Compliance:
— Built-in security features and compliance with various regulatory standards to protect data and ensure privacy.

Example Use Cases:
1. Chatbots and Virtual Assistants:
— Develop conversational agents that can understand and respond to user queries in natural language.

2. Content Generation:
— Automatically generate articles, summaries, or creative content based on given prompts.

3. Translation and Summarization:
— Translate text between languages or summarize long documents into concise versions.

4. Sentiment Analysis and Text Classification:
— Analyze the sentiment of user feedback or classify text into different categories.

Using Azure OpenAI Service
1. Setting Up Azure OpenAI Service:
— Ensure you have an Azure account and a subscription.
— Navigate to the Azure Portal and search for “Azure OpenAI”.
— Create a new Azure OpenAI resource and follow the prompts to configure it.

2. Getting API Access:
— Once the service is set up, navigate to the “Keys and Endpoint” section to obtain your API key and endpoint URL.

3. Making API Calls:
— Use the API key and endpoint to make requests to the Azure OpenAI service.
— Below is an example of making a request using Python:

import requests

endpoint = "https://your-openai-endpoint.cognitiveservices.azure.com/"
api_key = "your-api-key"

headers = {
    "Content-Type": "application/json",
    "api-key": api_key
}

data = {
    "prompt": "Once upon a time, in a land far away,",
    "max_tokens": 50
}

response = requests.post(f"{endpoint}/v1/engines/davinci-codex/completions", headers=headers, json=data)
result = response.json()
print(result['choices'][0]['text'])

4. Fine-Tuning Models:
— To fine-tune a model, you need a dataset that contains the examples you want the model to learn from.
— Upload your dataset to Azure Blob Storage and use the Azure OpenAI fine-tuning API to train your custom model.

fine_tune_data = {
    "training_file": "https://your-storage-account.blob.core.windows.net/your-container/your-dataset.jsonl",
    "model": "davinci-codex"
}

response = requests.post(f"{endpoint}/v1/models/davinci-codex/fine-tune", headers=headers, json=fine_tune_data)
fine_tune_result = response.json()
print(fine_tune_result)

5. Deploying and Using Fine-Tuned Models:
— After fine-tuning, deploy the custom model and use it for inference.
— Update your API calls to use the fine-tuned model.

Summary

Azure offers a comprehensive set of AI and machine learning services that cater to various needs and expertise levels. The Azure OpenAI Service, in particular, provides access to powerful language models that can be integrated into applications for a wide range of natural language processing tasks. By leveraging these tools, developers can build sophisticated AI applications with ease.

Data Preparation and Management

Overview

Data preparation and management are critical steps in the machine learning workflow. Properly managed and preprocessed data ensures that AI models are trained effectively and produce accurate predictions. Azure provides a variety of services for storing, processing, and preparing data, making it easier to handle large datasets and complex data pipelines.

Key Azure Services for Data Preparation and Management
1. Azure Blob Storage:
— Description: Scalable object storage service for storing unstructured data.
— Use Cases: Storing raw data, intermediate processed data, and model artifacts.
— Features: Data lifecycle management, tiered storage, fine-grained access control.

Example:

from azure.storage.blob import BlobServiceClient, BlobClient, ContainerClient

connect_str = "your_connection_string"
blob_service_client = BlobServiceClient.from_connection_string(connect_str)

# Create a container
container_client = blob_service_client.create_container("mycontainer")

# Upload a file
blob_client = container_client.get_blob_client("myfile.csv")
with open("path/to/myfile.csv", "rb") as data:
    blob_client.upload_blob(data)

2. Azure Data Lake Storage:
— Description: Scalable data lake for storing large volumes of structured and unstructured data.
— Use Cases: Storing big data for analytics, machine learning, and data warehousing.
— Features: Hierarchical namespace, high throughput, security and compliance.

Example:

from azure.storage.filedatalake import DataLakeServiceClient

service_client = DataLakeServiceClient.from_connection_string("your_connection_string")

# Create a file system
file_system_client = service_client.create_file_system(file_system="myfilesystem")

# Create a file
file_client = file_system_client.create_file("myfile.csv")
with open("path/to/myfile.csv", "rb") as data:
    file_client.upload_data(data, overwrite=True)

3. Azure SQL Database and Azure Cosmos DB:
— Description: Managed relational database and NoSQL database services, respectively.
— Use Cases: Storing structured data, managing relational data, handling globally distributed data with low latency.
— Features: Automatic scaling, high availability, integrated security.

Example for Azure SQL Database:

import pyodbc

conn = pyodbc.connect('DRIVER={ODBC Driver 17 for SQL Server};SERVER=your_server.database.windows.net;DATABASE=your_database;UID=your_username;PWD=your_password')
cursor = conn.cursor()

# Create a table
cursor.execute('''
CREATE TABLE mytable (
    id INT PRIMARY KEY,
    name NVARCHAR(50),
    age INT
)
''')

# Insert data
cursor.execute('''
INSERT INTO mytable (id, name, age) VALUES (1, 'John Doe', 30)
''')
conn.commit()

Example for Azure Cosmos DB:

from azure.cosmos import CosmosClient, exceptions, PartitionKey

client = CosmosClient("your_cosmos_account_url", "your_cosmos_account_key")

# Create a database
database = client.create_database_if_not_exists(id="mydatabase")

# Create a container
container = database.create_container_if_not_exists(
    id="mycontainer",
    partition_key=PartitionKey(path="/id"),
    offer_throughput=400
)

# Insert data
container.create_item(body={"id": "1", "name": "John Doe", "age": 30})

4. Azure Data Factory:
— Description: Cloud-based data integration service for creating ETL (extract, transform, load) and ELT (extract, load, transform) workflows.
— Use Cases: Data migration, data transformation, orchestrating data workflows.
— Features: Visual interface, built-in connectors, integration with other Azure services.

Example:

from azure.identity import DefaultAzureCredential
from azure.mgmt.datafactory import DataFactoryManagementClient
from azure.mgmt.datafactory.models import *

credential = DefaultAzureCredential()
adf_client = DataFactoryManagementClient(credential, "your_subscription_id")

rg_name = "your_resource_group"
df_name = "your_data_factory"

# Create a data factory
df_params = Factory(location="East US")
df_resource = adf_client.factories.create_or_update(rg_name, df_name, df_params)

Data Preparation Workflow
1. Data Ingestion:
— Collect data from various sources such as databases, files, and APIs.
— Use Azure Data Factory to orchestrate data ingestion workflows.

2. Data Cleaning and Transformation:
— Clean and preprocess data to remove inconsistencies and prepare it for analysis.
— Use Azure Databricks or Azure Data Factory for data transformation tasks.

Example with Azure Databricks:

from pyspark.sql import SparkSession

spark = SparkSession.builder.appName("DataCleaning").getOrCreate()

# Load data
df = spark.read.csv("path/to/data.csv", header=True, inferSchema=True)

# Data cleaning
df_cleaned = df.dropna().dropDuplicates()

# Transformation
df_transformed = df_cleaned.withColumn("age", df_cleaned["age"].cast("int"))

# Save cleaned and transformed data
df_transformed.write.csv("path/to/cleaned_data.csv", header=True)

3. Data Storage and Management:
— Store cleaned and transformed data in Azure Blob Storage, Data Lake, SQL Database, or Cosmos DB.
— Use appropriate storage solutions based on data type, volume, and access patterns.

4. Data Analysis and Visualization:
— Use Azure Synapse Analytics or Azure Databricks for data analysis and visualization.
— Integrate with Power BI for interactive dashboards and reports.

Example with Azure Synapse Analytics:

from azure.synapse.spark import SynapseSpark

spark = SynapseSpark.builder.appName("DataAnalysis").getOrCreate()

# Load data from Data Lake
df = spark.read.csv("abfss://your-container@your-storage-account.dfs.core.windows.net/path/to/data.csv", header=True, inferSchema=True)

# Perform analysis
df_grouped = df.groupBy("category").agg({"amount": "sum"})

# Show results
df_grouped.show()

Best Practices for Data Preparation and Management

1. Data Quality:
— Ensure your data is clean, accurate, and consistent before using it for model training.
— Use tools like Azure Databricks for data cleaning and transformation.

2. Data Security:
— Protect your data by using encryption and access control mechanisms.
— Implement Azure RBAC (Role-Based Access Control) and use Azure Key Vault for managing secrets and keys.

3. Scalability:
— Choose scalable storage and processing solutions to handle large datasets efficiently.
— Use Azure Data Lake Storage and Azure Databricks for scalable data processing.

4. Automation:
— Automate repetitive data preparation tasks using Azure Data Factory and Azure Databricks.
— Set up automated data pipelines for continuous data integration and transformation.

5. Monitoring and Logging:
— Monitor your data pipelines and storage systems to detect and address issues promptly.
— Use Azure Monitor and Azure Log Analytics for monitoring and logging.

Summary

Azure provides a comprehensive set of tools and services for data preparation and management, making it easier to handle large datasets and complex data workflows. By leveraging these tools, you can ensure that your data is properly managed, cleaned, and transformed, enabling effective AI model training and deployment.

Building AI Models on Azure

Overview

Building AI models involves several key steps, from data preparation and model training to deployment and monitoring. Azure provides a comprehensive suite of tools and services to facilitate each of these steps, ensuring that you can build robust and scalable AI models. In this chapter, we will explore how to use Azure Machine Learning and other tools to train, evaluate, and deploy machine learning models.

Key Tools and Services for Building AI Models

1. Azure Machine Learning:
— Description: A cloud-based service for building, training, and deploying machine learning models.
— Features: Supports popular frameworks like TensorFlow, PyTorch, and scikit-learn; provides automated machine learning (AutoML), MLOps for model lifecycle management, and integration with other Azure services.

2. Azure Databricks:
— Description: An Apache Spark-based analytics platform optimized for Azure.
— Features: Collaborative notebooks, integrated machine learning environment, and seamless integration with Azure Machine Learning for model training and deployment.

3. Azure Cognitive Services:
— Description: Pre-built AI models for vision, speech, language, and decision-making tasks.
— Features: Easy integration through APIs, supports custom model training, and fine-tuning.

4. Azure Synapse Analytics:
— Description: An integrated analytics service for big data and data warehousing.
— Features: Data integration, exploration, and visualization capabilities, with built-in machine learning support.

Steps for Building AI Models on Azure
1. Data Preparation
- Azure Blob Storage:
— Store your raw and processed data.
— Use Azure Data Factory or Azure Databricks to preprocess and clean the data.

Example:

from azure.storage.blob import BlobServiceClient, BlobClient, ContainerClient

connect_str = "your_connection_string"
blob_service_client = BlobServiceClient.from_connection_string(connect_str)

# Upload a file
blob_client = blob_service_client.get_blob_client(container="mycontainer", blob="myfile.csv")
with open("path/to/myfile.csv", "rb") as data:
    blob_client.upload_blob(data)

2. Model Training
- Azure Machine Learning:
— Create an Azure Machine Learning workspace.
— Use the workspace to manage experiments, compute resources, and datasets.

Example:

from azureml.core import Workspace, Dataset
from azureml.train.automl import AutoMLConfig
from azureml.core.experiment import Experiment

# Connect to the workspace
ws = Workspace.from_config()

# Load the dataset
dataset = Dataset.get_by_name(ws, name="your_dataset_name")

# Configure AutoML
automl_config = AutoMLConfig(
    task='classification',
    primary_metric='AUC_weighted',
    training_data=dataset,
    label_column_name='target',
    compute_target='your_compute_cluster',
    max_trials=10
)

# Create an experiment
experiment = Experiment(ws, "automl_classification")
run = experiment.submit(automl_config, show_output=True)

- Azure Databricks:
— Use Databricks notebooks for data exploration, feature engineering, and model training.
— Integrate with Azure Machine Learning for seamless model deployment.

Example:

from pyspark.sql import SparkSession

spark = SparkSession.builder.appName("ModelTraining").getOrCreate()

# Load data
df = spark.read.csv("path/to/data.csv", header=True, inferSchema=True)

# Feature engineering
df_transformed = df.withColumn("feature", df["column1"] * df["column2"])

# Train a model
from pyspark.ml.classification import LogisticRegression

lr = LogisticRegression(featuresCol="features", labelCol="label")
model = lr.fit(df_transformed)

# Save the model
model.save("path/to/save/model")

3. Model Evaluation
- Evaluate the Model:
— Use evaluation metrics such as accuracy, precision, recall, and F1 score to assess model performance.

Example:

from sklearn.metrics import classification_report

y_true = [0, 1, 1, 0, 1]
y_pred = [0, 1, 0, 0, 1]

report = classification_report(y_true, y_pred, target_names=['class 0', 'class 1'])
print(report)

- Hyperparameter Tuning:
— Use Azure Machine Learning’s HyperDrive to perform hyperparameter tuning.

Example:

from azureml.train.hyperdrive import HyperDriveConfig, RandomParameterSampling, choice

param_sampling = RandomParameterSampling({
    "learning_rate": choice(0.01, 0.1, 1.0),
    "batch_size": choice(16, 32, 64)
})

hyperdrive_config = HyperDriveConfig(
    run_config=automl_config,
    hyperparameter_sampling=param_sampling,
    primary_metric_name="accuracy",
    primary_metric_goal=PrimaryMetricGoal.MAXIMIZE,
    max_total_runs=10
)

hyperdrive_run = experiment.submit(hyperdrive_config)

4. Model Deployment
- Azure Machine Learning:
— Deploy the trained model as a web service.

Example:

from azureml.core import Model
from azureml.core.webservice import AciWebservice, Webservice

# Register the model
model = Model.register(workspace=ws, model_path="path/to/model", model_name="my_model")

# Define deployment configuration
aci_config = AciWebservice.deploy_configuration(cpu_cores=1, memory_gb=1)

# Deploy the model
service = Model.deploy(workspace=ws, name="my-service", models=[model], inference_config=inference_config, deployment_config=aci_config)
service.wait_for_deployment(show_output=True)

print(service.scoring_uri)

- Azure Kubernetes Service (AKS):
— Deploy models to AKS for scalable and high-performance inference.

Example:

from azureml.core.webservice import AksWebservice, AksCompute

# Create AKS cluster
aks_target = AksCompute.create(ws, name="myakscluster", agent_count=3, vm_size="Standard_D2_v2")

# Define AKS deployment configuration
aks_config = AksWebservice.deploy_configuration(cpu_cores=1, memory_gb=1)

# Deploy the model to AKS
service = Model.deploy(workspace=ws, name="my-aks-service", models=[model], inference_config=inference_config, deployment_config=aks_config, deployment_target=aks_target)
service.wait_for_deployment(show_output=True)

print(service.scoring_uri)

5. Model Monitoring and Management
- Azure Machine Learning:
— Monitor deployed models using Azure Machine Learning’s monitoring capabilities.
— Track model performance, detect data drift, and retrain models as needed.

Example:

from azureml.monitoring import ModelDataCollector

data_collector = ModelDataCollector(model_name, inputs="input_data", outputs="predictions")

def run(data):
    data_collector.capture(inputs=data, outputs=predictions)
    return predictions

Summary

Azure provides a comprehensive suite of tools and services for building, training, and deploying AI models. By leveraging Azure Machine Learning, Databricks, and other services, you can build robust and scalable AI solutions that meet your specific needs. From data preparation to model deployment and monitoring, Azure offers the necessary tools to manage the entire machine learning lifecycle.

Creating and Managing Plugins

Overview

Plugins are modular components that can enhance the functionality of your AI solutions by adding new capabilities, processing additional data types, or customizing outputs. In this chapter, we will cover how to develop custom plugins, integrate them with Azure services, and use Azure Functions for plugin execution.

Key Concepts and Tools
1. Plugins:
— Modular components that extend the functionality of your applications.
— Can be used for tasks like data preprocessing, feature extraction, and custom model inference.

2. Azure Functions:
— A serverless compute service that allows you to run event-driven code without managing infrastructure.
— Ideal for executing plugins and integrating them with other Azure services.

3. Integration with Azure Services:
— Plugins can be integrated with various Azure services like Azure Machine Learning, Blob Storage, and Cognitive Services.
— Azure Functions can trigger these integrations based on events or HTTP requests.

Developing Custom Plugins
1. Setting Up the Development Environment
- Ensure you have the necessary tools installed, including Azure CLI and Visual Studio Code.
— Create an Azure Function App to host your plugins.

# Install Azure Functions Core Tools
npm install -g azure-functions-core-tools@3 --unsafe-perm true

# Create a new Function App
func init MyFunctionApp --javascript
cd MyFunctionApp
func new --name MyPluginFunction --template "HTTP trigger"

2. Creating a Basic Plugin
- Define the functionality of your plugin. For example, a plugin to preprocess text before feeding it to an AI model.

module.exports = async function (context, req) {
    const inputText = req.body.text;
    const cleanedText = cleanText(inputText);
    context.res = {
        body: { cleanedText }
    };
};

function cleanText(text) {
    // Example cleaning process
    return text.toLowerCase().replace(/[^\w\s]/gi, '');
}

3. Deploying the Plugin with Azure Functions
- Deploy your Function App to Azure.

az login
az account set --subscription "your-subscription-id"

# Create a resource group
az group create --name MyResourceGroup --location eastus

# Create a storage account
az storage account create --name mystorageaccount --location eastus --resource-group MyResourceGroup --sku Standard_LRS

# Create a Function App
az functionapp create --resource-group MyResourceGroup --consumption-plan-location eastus --runtime node --functions-version 3 --name MyFunctionApp --storage-account mystorageaccount

# Deploy the Function App
func azure functionapp publish MyFunctionApp

4. Integrating Plugins with Azure Services
- Use Azure Functions to interact with other Azure services such as Azure Machine Learning or Cognitive Services.

Example: Integrating with Azure Machine Learning

const axios = require('axios');

module.exports = async function (context, req) {
    const inputText = req.body.text;
    const cleanedText = cleanText(inputText);

    const amlResponse = await callAzureMLService(cleanedText);

    context.res = {
        body: { cleanedText, amlResponse }
    };
};

function cleanText(text) {
    // Example cleaning process
    return text.toLowerCase().replace(/[^\w\s]/gi, '');
}

async function callAzureMLService(text) {
    const endpoint = "https://your-azureml-endpoint";
    const apiKey = "your-api-key";

    const response = await axios.post(endpoint, { text }, {
        headers: { 'Authorization': `Bearer ${apiKey}` }
    });

    return response.data;
}

5. Creating and Using a Plugin for Data Preprocessing
- Develop a plugin that performs data preprocessing, such as normalizing numerical values or encoding categorical features.

Example: Normalizing Numerical Values

module.exports = async function (context, req) {
    const data = req.body.data;
    const normalizedData = normalizeData(data);
    context.res = {
        body: { normalizedData }
    };
};

function normalizeData(data) {
    const min = Math.min(...data);
    const max = Math.max(...data);
    return data.map(value => (value - min) / (max - min));
}

6. Using Azure Functions to Trigger Plugin Execution
- Configure Azure Functions to trigger plugin execution based on events or HTTP requests.

Example: HTTP Trigger

module.exports = async function (context, req) {
    const data = req.body.data;
    const processedData = processData(data);
    context.res = {
        body: { processedData }
    };
};

function processData(data) {
    // Your processing logic here
    return data.map(value => value * 2);
}

Example: Blob Trigger

const { BlobServiceClient } = require('@azure/storage-blob');

module.exports = async function (context, blob) {
    const processedData = processBlob(blob);
    context.log('Blob processed:', processedData);
};

function processBlob(blob) {
    // Your processing logic here
    return blob.toString().toUpperCase();
}

Summary

Creating and managing plugins on Azure allows you to extend the capabilities of your AI solutions, enabling customized processing and functionality. By leveraging Azure Functions and integrating with other Azure services, you can build scalable and efficient plugins that enhance your AI workflows.

Real-World Use Cases and Examples

Overview

This chapter explores real-world use cases and examples of AI solutions built on Microsoft Azure. These examples illustrate how various industries leverage Azure’s AI and machine learning services to solve complex problems, improve efficiency, and innovate.

Use Case 1: Image Recognition in Healthcare
Problem: Automating the diagnosis of medical images, such as X-rays and MRIs, to identify abnormalities and assist doctors in making accurate diagnoses.
Solution: Use Azure’s Cognitive Services and Machine Learning to build and deploy an image recognition model.

1. Data Collection and Preparation:
— Collect and annotate a dataset of medical images.
— Store the images in Azure Blob Storage.

Example:

from azure.storage.blob import BlobServiceClient

connect_str = "your_connection_string"
blob_service_client = BlobServiceClient.from_connection_string(connect_str)

# Create a container
container_client = blob_service_client.create_container("medical-images")

# Upload an image
blob_client = container_client.get_blob_client("image1.jpg")
with open("path/to/image1.jpg", "rb") as data:
    blob_client.upload_blob(data)

2. Training an Image Recognition Model with Azure Machine Learning:
— Use Azure Machine Learning to train a custom image classification model.

Example:

from azureml.core import Workspace, Dataset, Experiment
from azureml.train.automl import AutoMLConfig

# Connect to workspace
ws = Workspace.from_config()

# Create dataset
datastore = ws.get_default_datastore()
dataset = Dataset.File.from_files((datastore, 'medical-images/'))

# Define AutoML settings
automl_config = AutoMLConfig(
    task='image-classification',
    training_data=dataset,
    label_column_name='label',
    compute_target='your_compute_cluster',
    iterations=10
)

# Run experiment
experiment = Experiment(ws, "image-classification")
run = experiment.submit(automl_config, show_output=True)

3. Deploying the Model:
— Deploy the trained model as a web service using Azure Machine Learning.

Example:

from azureml.core import Model
from azureml.core.webservice import AciWebservice, Webservice

# Register the model
model = Model.register(workspace=ws, model_path="outputs/model.pkl", model_name="medical_image_model")

# Define deployment configuration
aci_config = AciWebservice.deploy_configuration(cpu_cores=1, memory_gb=1)

# Deploy the model
service = Model.deploy(workspace=ws, name="medical-image-service", models=[model], inference_config=inference_config, deployment_config=aci_config)
service.wait_for_deployment(show_output=True)

4. Making Predictions:
— Use the deployed model to analyze new medical images and predict abnormalities.

Example:

import requests

url = "http://medical-image-service.azurewebsites.net/score"
image_path = "path/to/new_image.jpg"

with open(image_path, "rb") as image:
    response = requests.post(url, files={"file": image})

print(response.json())

Use Case 2: Natural Language Processing in Customer Support
Problem: Automating responses to customer queries by understanding and processing natural language.

Solution: Use Azure’s Cognitive Services and Machine Learning to build a conversational AI chatbot.
1. Building a Dialogflow Agent:
— Create and configure a Dialogflow agent for customer support.

Example:

from google.cloud import dialogflow_v2 as dialogflow

client = dialogflow.AgentsClient()
project_agent_path = client.project_path('YOUR_PROJECT_ID')

agent = {
    "display_name": "customer_support_agent",
    "default_language_code": "en",
    "time_zone": "America/Los_Angeles",
}

response = client.set_agent(request={"agent": agent})

2. Creating Intents and Training:
— Define intents for common customer queries and train the agent.

Example:

intent_client = dialogflow.IntentsClient()
parent = intent_client.agent_path('YOUR_PROJECT_ID')

training_phrases = [
    dialogflow.Intent.TrainingPhrase(parts=[{"text": "How do I reset my password?"}]),
    dialogflow.Intent.TrainingPhrase(parts=[{"text": "Where is my order?"}]),
]

message = dialogflow.Intent.Message(text={"text": ["You can reset your password by clicking on the 'Forgot Password' link."]})

intent = dialogflow.Intent(
    display_name="password_reset",
    training_phrases=training_phrases,
    messages=[message],
)

response = intent_client.create_intent(request={"parent": parent, "intent": intent})

3. Integrating with Customer Support System:
— Deploy the Dialogflow agent and integrate it with your existing customer support system.

Example:

session_client = dialogflow.SessionsClient()
session = session_client.session_path('YOUR_PROJECT_ID', 'unique_session_id')

text_input = dialogflow.TextInput(text="How do I reset my password?", language_code="en")
query_input = dialogflow.QueryInput(text=text_input)

response = session_client.detect_intent(request={"session": session, "query_input": query_input})
print(response.query_result.fulfillment_text)

4. Analyzing Customer Sentiment:
— Use the Azure Text Analytics API to analyze customer sentiment from interactions.

Example:

from azure.ai.textanalytics import TextAnalyticsClient
from azure.core.credentials import AzureKeyCredential

endpoint = "https://your-text-analytics.cognitiveservices.azure.com/"
key = "your-text-analytics-key"

text_analytics_client = TextAnalyticsClient(endpoint=endpoint, credential=AzureKeyCredential(key))

documents = ["I am very happy with your service!", "I am not satisfied with the product."]

response = text_analytics_client.analyze_sentiment(documents=documents)
for doc in response:
    print(f"Sentiment: {doc.sentiment}, Confidence Scores: {doc.confidence_scores}")

Use Case 3: Predictive Maintenance in Manufacturing
Problem: Predicting equipment failures to reduce downtime and maintenance costs.
Solution: Use Azure Machine Learning and Azure Synapse Analytics to build a predictive maintenance model.

1. Data Collection and Storage:
— Collect equipment sensor data and store it in Azure Blob Storage.

Example:

from azure.storage.blob import BlobServiceClient

connect_str = "your_connection_string"
blob_service_client = BlobServiceClient.from_connection_string(connect_str)

# Create a container
container_client = blob_service_client.create_container("sensor-data")

# Upload sensor data
blob_client = container_client.get_blob_client("data.csv")
with open("path/to/data.csv", "rb") as data:
    blob_client.upload_blob(data)

2. Training a Predictive Model with Azure Machine Learning:
— Use Azure Machine Learning to train a predictive maintenance model.

Example:

from azureml.core import Workspace, Dataset, Experiment
from azureml.train.automl import AutoMLConfig

# Connect to workspace
ws = Workspace.from_config()

# Create dataset
datastore = ws.get_default_datastore()
dataset = Dataset.Tabular.from_delimited_files(path=(datastore, 'sensor-data/data.csv'))

# Define AutoML settings
automl_config = AutoMLConfig(
    task='classification',
    training_data=dataset,
    label_column_name='failure',
    compute_target='your_compute_cluster',
    iterations=10
)

# Run experiment
experiment = Experiment(ws, "predictive-maintenance")
run = experiment.submit(automl_config, show_output=True)

3. Deploying the Model:
— Deploy the trained model as a web service using Azure Machine Learning.

Example:

from azureml.core import Model
from azureml.core.webservice import AciWebservice, Webservice

# Register the model
model = Model.register(workspace=ws, model_path="outputs/model.pkl", model_name="predictive_maintenance_model")

# Define deployment configuration
aci_config = AciWebservice.deploy_configuration(cpu_cores=1, memory_gb=1)

# Deploy the model
service = Model.deploy(workspace=ws, name="predictive-maintenance-service", models=[model], inference_config=inference_config, deployment_config=aci_config)
service.wait_for_deployment(show_output=True)

4. Using the Predictive Model for Maintenance Decisions:
— Use the deployed model to predict equipment failures and schedule maintenance accordingly.

Example:

import requests

url = "http://predictive-maintenance-service.azurewebsites.net/score"
data = {"sensor_readings": [0.1, 0.5, 0.3, 0.4, 0.2]}

response = requests.post(url, json=data)
print(response.json())

Summary

These real-world use cases demonstrate the versatility and power of Azure’s AI and machine learning services across various industries. By leveraging tools like Azure Machine Learning, Cognitive Services, and Synapse Analytics, businesses can build innovative solutions to address complex challenges, improve efficiency, and drive growth.

Coding Examples and Templates

Overview

This chapter provides practical coding examples and templates for various AI tasks using Microsoft Azure. These examples are designed to help both beginners and experienced developers quickly get started with building and deploying AI models. The examples cover data preparation, model training, deployment, and predictions.

Example 1: Image Classification with Azure Machine Learning
Objective: Train and deploy an image classification model using Azure Machine Learning.

1. Data Preparation
— Upload images to a Google Cloud Storage bucket.

from azure.storage.blob import BlobServiceClient, BlobClient, ContainerClient

connect_str = "your_connection_string"
blob_service_client = BlobServiceClient.from_connection_string(connect_str)

# Create a container
container_client = blob_service_client.create_container("image-classification-data")

# Upload images
blob_client = container_client.get_blob_client("image1.jpg")
with open("path/to/image1.jpg", "rb") as data:
    blob_client.upload_blob(data)

2. Create and Import Dataset

from azureml.core import Workspace, Dataset

# Connect to the workspace
ws = Workspace.from_config()

# Register the dataset
datastore = ws.get_default_datastore()
dataset = Dataset.File.from_files((datastore, 'image-classification-data/'))
dataset = dataset.register(workspace=ws, name='image_classification_data')

3. Training the Model

from azureml.train.automl import AutoMLConfig
from azureml.core.experiment import Experiment

# Define the AutoML configuration
automl_config = AutoMLConfig(
    task='image-classification',
    primary_metric='accuracy',
    training_data=dataset,
    label_column_name='label',
    compute_target='your_compute_cluster',
    iterations=10
)

# Create and run the experiment
experiment = Experiment(ws, "image_classification_experiment")
run = experiment.submit(automl_config, show_output=True)

4. Deploying the Model

from azureml.core import Model
from azureml.core.webservice import AciWebservice, Webservice

# Register the model
model = Model.register(workspace=ws, model_path="outputs/model.pkl", model_name="image_classification_model")

# Define the deployment configuration
aci_config = AciWebservice.deploy_configuration(cpu_cores=1, memory_gb=1)

# Deploy the model
service = Model.deploy(workspace=ws, name="image-classification-service", models=[model], inference_config=inference_config, deployment_config=aci_config)
service.wait_for_deployment(show_output=True)

print(service.scoring_uri)

5. Making Predictions

import requests

url = "http://image-classification-service.azurewebsites.net/score"
image_path = "path/to/new_image.jpg"

with open(image_path, "rb") as image:
    response = requests.post(url, files={"file": image})

print(response.json())

Example 2: Text Classification with Azure Machine Learning
Objective: Train and deploy a text classification model using Azure Machine Learning.

1. Data Preparation
— Prepare a CSV file with text data and labels.
— Upload the CSV file to an Azure Blob Storage container.

from azure.storage.blob import BlobServiceClient

connect_str = "your_connection_string"
blob_service_client = BlobServiceClient.from_connection_string(connect_str)

# Create a container
container_client = blob_service_client.create_container("text-classification-data")

# Upload CSV file
blob_client = container_client.get_blob_client("data.csv")
with open("path/to/data.csv", "rb") as data:
    blob_client.upload_blob(data)

2. Create and Import Dataset

from azureml.core import Workspace, Dataset

# Connect to the workspace
ws = Workspace.from_config()

# Register the dataset
datastore = ws.get_default_datastore()
dataset = Dataset.Tabular.from_delimited_files(path=(datastore, 'text-classification-data/data.csv'))
dataset = dataset.register(workspace=ws, name='text_classification_data')

3. Training the Model

from azureml.train.automl import AutoMLConfig
from azureml.core.experiment import Experiment

# Define the AutoML configuration
automl_config = AutoMLConfig(
    task='classification',
    primary_metric='accuracy',
    training_data=dataset,
    label_column_name='label',
    compute_target='your_compute_cluster',
    iterations=10
)

# Create and run the experiment
experiment = Experiment(ws, "text_classification_experiment")
run = experiment.submit(automl_config, show_output=True)

4. Deploying the Model

from azureml.core import Model
from azureml.core.webservice import AciWebservice, Webservice

# Register the model
model = Model.register(workspace=ws, model_path="outputs/model.pkl", model_name="text_classification_model")

# Define the deployment configuration
aci_config = AciWebservice.deploy_configuration(cpu_cores=1, memory_gb=1)

# Deploy the model
service = Model.deploy(workspace=ws, name="text-classification-service", models=[model], inference_config=inference_config, deployment_config=aci_config)
service.wait_for_deployment(show_output=True)

print(service.scoring_uri)

5. Making Predictions

import requests

url = "http://text-classification-service.azurewebsites.net/score"
data = {"text": "Your text data for classification."}

response = requests.post(url, json=data)
print(response.json())

Example 3: Predictive Analytics with Azure Synapse Analytics
Objective: Build and deploy a predictive model using Azure Synapse Analytics.

1. Data Preparation
— Store your dataset in a Synapse table.

from azure.synapse.spark import SynapseSpark

spark = SynapseSpark.builder.appName("PredictiveAnalytics").getOrCreate()

# Load data
df = spark.read.csv("path/to/data.csv", header=True, inferSchema=True)

# Save data to Synapse table
df.write.synapsesql("your_synapse_table", mode="overwrite")

2. Training the Model

from azureml.core import Workspace, Dataset, Experiment
from azureml.train.automl import AutoMLConfig

# Connect to the workspace
ws = Workspace.from_config()

# Register the dataset
datastore = ws.get_default_datastore()
dataset = Dataset.Tabular.from_delimited_files(path=(datastore, 'path/to/data.csv'))
dataset = dataset.register(workspace=ws, name='predictive_analytics_data')

# Define the AutoML configuration
automl_config = AutoMLConfig(
    task='regression',
    primary_metric='r2_score',
    training_data=dataset,
    label_column_name='target',
    compute_target='your_compute_cluster',
    iterations=10
)

# Create and run the experiment
experiment = Experiment(ws, "predictive_analytics_experiment")
run = experiment.submit(automl_config, show_output=True)

3. Deploying the Model

from azureml.core import Model
from azureml.core.webservice import AciWebservice, Webservice

# Register the model
model = Model.register(workspace=ws, model_path="outputs/model.pkl", model_name="predictive_analytics_model")

# Define the deployment configuration
aci_config = AciWebservice.deploy_configuration(cpu_cores=1, memory_gb=1)

# Deploy the model
service = Model.deploy(workspace=ws, name="predictive-analytics-service", models=[model], inference_config=inference_config, deployment_config=aci_config)
service.wait_for_deployment(show_output=True)

print(service.scoring_uri)

4. Making Predictions

import requests

url = "http://predictive-analytics-service.azurewebsites.net/score"
data = {"features": [0.1, 0.5, 0.3, 0.4, 0.2]}

response = requests.post(url, json=data)
print(response.json())

Templates for AI Tasks
1. Data Loading Template

def load_data_from_blob_storage(container_name, file_path, connection_string):
    from azure.storage.blob import BlobServiceClient
    import pandas as pd

    blob_service_client = BlobServiceClient.from_connection_string(connection_string)
    blob_client = blob_service_client.get_blob_client(container=container_name, blob=file_path)
    download_stream = blob_client.download_blob()
    data = download_stream.readall()

    df = pd.read_csv(pd.compat.StringIO(data.decode('utf-8')))
    return df

data = load_data_from_blob_storage('your-container', 'path/to/data.csv', 'your-connection-string')
print(data.head())

2. Model Training Template

from azureml.core import Workspace, Experiment
from azureml.train.automl import AutoMLConfig

def train_model(workspace_config, dataset_name, experiment_name, compute_cluster, task, label_column, iterations=10):
    ws = Workspace.from_config(workspace_config)
    dataset = ws.datasets[dataset_name]

    automl_config = AutoMLConfig(
        task=task,
        primary_metric='accuracy',
        training_data=dataset,
        label_column_name=label_column,
        compute_target=compute_cluster,
        iterations=iterations
    )

    experiment = Experiment(ws, experiment_name)
    run = experiment.submit(automl_config, show_output=True)
    return run

run = train_model('config.json', 'dataset_name', 'experiment_name', 'compute_cluster', 'classification', 'label', 10)

3. Model Deployment Template

from azureml.core import Workspace, Model
from azureml.core.webservice import AciWebservice, Webservice

def deploy_model(workspace_config, model_path, model_name, service_name, cpu_cores=1, memory_gb=1):
    ws = Workspace.from_config(workspace_config)

    model = Model.register(workspace=ws, model_path=model_path, model_name=model_name)
    aci_config = AciWebservice.deploy_configuration(cpu_cores=cpu_cores, memory_gb=memory_gb)
    service = Model.deploy(workspace=ws, name=service_name, models=[model], inference_config=inference_config, deployment_config=aci_config)
    service.wait_for_deployment(show_output=True)
    return service.scoring_uri

scoring_uri = deploy_model('config.json', 'path/to/model.pkl', 'model_name', 'service_name', 1, 1)
print(scoring_uri)

4. Prediction Template

import requests

def make_prediction(service_url, input_data):
    response = requests.post(service_url, json=input_data)
    return response.json()

prediction = make_prediction('http://your-service.azurewebsites.net/score', {"data": "your_input_data"})
print(prediction)

Summary

These coding examples and templates provide a practical starting point for various AI tasks on Azure. By leveraging these templates, you can quickly build, deploy, and manage AI models for image classification, text classification, and predictive analytics.

Troubleshooting and Support

Overview

In this final chapter, we will cover troubleshooting techniques for common issues you might encounter while developing AI solutions on Microsoft Azure. Additionally, we will provide guidance on how to effectively use Azure support resources to resolve problems and get assistance.

Common Issues and Solutions
1. Authentication and Permissions Issues
— Symptom: Receiving “permission denied” or “unauthorized” errors when accessing Azure services.
— Solution:
— Ensure your service principal or user account has the necessary IAM roles and permissions.
— Verify that the `AZURE_SUBSCRIPTION_ID`, `AZURE_CLIENT_ID`, `AZURE_CLIENT_SECRET`, and `AZURE_TENANT_ID` environment variables are set correctly.

Example:

az ad sp create-for-rbac --name "myServicePrincipal" --role contributor \
    --scopes /subscriptions/{subscription-id}/resourceGroups/{resource-group} \
    --sdk-auth

2. Quota Exceeded Errors
— Symptom: Receiving errors indicating that you have exceeded your resource quotas.
— Solution:
— Check your current quota usage in the Azure Portal under the “Quotas” section.
— Request a quota increase if necessary:

Example:

az vm list-usage --location eastus --output table
az support ticket create --problem-class "Quota" --problem-sub-class "Compute" --quota-request "Standard_DS3_v2"

3. Model Training Issues
— Symptom: Model training jobs failing or taking too long to complete.
— Solution:
— Check the logs for your training job in the Azure Machine Learning portal to identify the cause of the failure.
— Ensure that your training data is properly formatted and that there are no missing or corrupted files.
— Use distributed training and appropriate machine types to speed up the training process.

Example:

from azureml.core import ScriptRunConfig
from azureml.core.runconfig import MpiConfiguration

distributed_job = ScriptRunConfig(
    source_directory='.',
    script='train.py',
    compute_target='gpu-cluster',
    distributed_job_config=MpiConfiguration(node_count=4)
)

run = experiment.submit(distributed_job)
run.wait_for_completion(show_output=True)

4. Deployment Issues
— Symptom: Model deployment failing or endpoint not responding as expected.
— Solution:
— Verify that the model artifact is correctly uploaded and accessible.
— Check for any errors in the deployment logs.
— Ensure that the endpoint configuration matches the requirements of your model.

Example:

from azureml.core import Webservice

service = Webservice(workspace=ws, name="my-service")
logs = service.get_logs()
print(logs)

5. Prediction Issues
— Symptom: Predictions are inaccurate or inconsistent.
— Solution:
— Ensure that the input data format matches the expected format for your model.
— Preprocess input data consistently with the preprocessing steps used during model training.
— Use Azure Machine Learning’s model monitoring tools to track model performance and detect anomalies.

Example:

from azureml.monitoring import ModelDataCollector

data_collector = ModelDataCollector(model_name, inputs="input_data", outputs="predictions")

def run(data):
    data_collector.capture(inputs=data, outputs=predictions)
    return predictions

Leveraging Azure Support Resources
1. Documentation and Tutorials
— Microsoft Azure Documentation: Comprehensive guides and reference materials for all Azure services.
— Microsoft Azure Documentation
— Microsoft Learn: Interactive tutorials and learning paths for Azure.
— Microsoft Learn

2. Community Forums and Stack Overflow
— Microsoft Q&A: A forum where you can ask questions and share knowledge with other Azure users.
— Microsoft Q&A
— Stack Overflow: Use the `azure` tag to find answers to common questions and issues.
— Azure on Stack Overflow

3. Support Plans
— Basic Support: Free support for billing and subscription management.
— Developer Support: Paid support plan with business hours access to technical support and guidance.
— Standard Support: 24/7 technical support with faster response times.
— Professional Direct Support: Highest level of support with a dedicated account manager and comprehensive services.

- Compare support plans and choose the one that best fits your needs:
— Azure Support Plans

4. Contacting Support
— Opening a Support Ticket: Use the Azure Portal to open a support case for technical issues or billing inquiries.
— Azure Portal Support

- Phone and Email Support: Available for Developer, Standard, and Professional Direct support plan customers.

5. Training and Certification
— Microsoft Learn: Free, interactive learning paths and modules to help you gain proficiency in Azure.
— Microsoft Learn
— Microsoft Certification: Certification exams to validate your expertise and skills in Azure.
— Microsoft Certification

Summary

By following these troubleshooting tips and leveraging Azure’s support resources, you can effectively resolve issues and ensure the smooth operation of your AI solutions. Accessing the right support and training resources will also help you stay up-to-date with best practices and advancements in AI development on Azure.

Conclusion

This comprehensive guide has covered the essential aspects of building AI solutions on Microsoft Azure, from setting up your environment and utilising various AI services to developing custom plugins, real-world use cases, coding examples, best practices, and troubleshooting. By following these guidelines, you can leverage Azure’s powerful tools and services to build innovative, efficient, and scalable AI solutions.

If you have any specific questions or need further assistance, feel free to reach out to Azure support or consult the additional resources provided.

Thank you for following along, and best of luck with your AI projects on Azure!