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I started studying for AI-102 AI Engineer Associate certification. I am adding the subjects that I am falling short on this blog to improve my knowledge now. First of the series is networking… :) Securing and setting up the network for Azure AI services involves several key steps to ensure that your resources are protected and accessible only to authorized users. Here’s a comprehensive guide: Step 1: Configure Virtual Networks Create a Virtual Network: In the Azure portal, create a virtual network (VNet) that will host your Azure AI services. Add Subnets: Define subnets within your VNet to segment your network and improve security. Step 2: Set Up Private Endpoints Create Private Endpoints: Use private endpoints to connect your Azure AI services to your VNet securely. This ensures that traffic between your VNet and Azure AI services remains within the Azure backbone network. Configure DNS: Update your DNS settings to resolve the private endpoint IP addresses. Step 3: Configure Network Security Groups (NSGs) Create NSGs: Apply NSGs to your subnets to control inbound and outbound traffic. Define rules to allow traffic only from trusted sources. Apply NSGs: Attach the NSGs to your subnets and network interfaces. Step 4: Enable Firewall Rules Deny All by Default: Configure your Azure AI services to deny all incoming traffic by default. Allow Specific Networks: Create rules to allow traffic from specific VNets, subnets, or IP address ranges. Step 5: Use Service Tags and Application Security Groups Service Tags: Use Azure service tags to simplify the management of NSG rules. Service tags represent a group of IP address prefixes for specific Azure services. Application Security Groups: Group VMs and define security policies based on application tiers. Step 6: Monitor and Audit Enable Monitoring: Use Azure Monitor to track the performance and health of your Azure AI services. Audit Logs: Enable and review audit logs to track access and changes to your resources. Example Configuration Here’s an example of how you might configure your network-security-group.yml for NSGs: ...

For the AI-102 exam, understanding the Azure OpenAI REST API reference is crucial. Here are the key areas you should focus on: Control Plane API Resource Management: Learn how to create, update, and delete Azure OpenAI resources using the control plane API 1. Deployment: Understand how to deploy models and manage deployments through Azure Resource Manager, Bicep, Terraform, and Azure CLI 1. Data Plane - Authoring API Fine-Tuning: Familiarize yourself with the API endpoints for fine-tuning models, uploading files, and managing ingestion jobs 1. Batch Operations: Study how to perform batch operations and model-level queries 1. Data Plane - Inference API Completions: Learn how to create completions for provided prompts using the inference API 1. Chat Completions: Understand the endpoints for chat completions, embeddings, and other inference capabilities 1. Authentication: Know the methods for authenticating API calls using API Keys or Microsoft Entra ID 1. REST API Versioning API Versions: Be aware of the versioning structure using the api-version query parameter, which follows the YYYY-MM-DD format 1. Example API Call Here’s an example of making a completion request: ...

Here are examples for each of the key areas of the Azure OpenAI REST API that are important for the AI-102 exam: Control Plane API Example: Creating an Azure OpenAI Resource PUT https://management.azure.com/subscriptions/{subscription-id}/resourceGroups/{resource-group}/providers/Microsoft.CognitiveServices/accounts/{account-name}?api-version=2024-10-01 Request Body: { "location": "eastus", "sku": { "name": "S0" }, "kind": "OpenAI", "properties": { "networkAcls": { "defaultAction": "Deny", "virtualNetworkRules": [ { "id": "/subscriptions/{subscription-id}/resourceGroups/{resource-group}/providers/Microsoft.Network/virtualNetworks/{vnet-name}/subnets/{subnet-name}" } ] } } } This example shows how to create an Azure OpenAI resource using the control plane API 1. ...

Overview This exercise demonstrates how to use the Azure AI Foundry portal’s prompt flow to build a custom chat app. The app leverages a generative AI model (via Azure OpenAI’s GPT-4 variant) to manage conversation by taking a user’s question and the chat history as inputs and generating an answer. The work is divided into several setup and deployment phases. Step-by-Step Summary Create an Azure AI Foundry Hub and Project ...

Because I would be deploying my RAG application along with my website, I decided to secure my embedding and chat endpoints. Yes, it is selfish but I am writing all these first for myself :). To keep things simple and local, I chose to use JWT tokens for authentication. My approach uses in-memory token generation and validation—no external dependencies or persistent storage required. This is a solid starting point, and you can always enhance it later as your needs grow. ...

Overview The exercise walks you through building a generative AI chat app using the Azure AI Foundry SDK. You deploy the gpt-4o model in the Azure AI Foundry portal and then create a client application that interacts with that model. Both Python and C# implementations are provided. Repository and Environment Setup Clone the repository and navigate to the correct folder: Python: rm -r mslearn-ai-foundry -f git clone https://github.com/microsoftlearning/mslearn-ai-studio mslearn-ai-foundry cd mslearn-ai-foundry/labfiles/chat-app/python C#: ...

Important Points: Deploying a Model in Azure AI Foundry: Sign in to the Azure AI Foundry portal. Search for and select the gpt-4o model. Create a project with customized settings (resource name, subscription, resource group, region). The project includes connections to Azure AI services and models. Creating a Client Application: Use Azure AI Foundry and Azure AI Model Inference SDKs to develop an application. Choose between Python or C# for development. Application Configuration: ...

Understanding AI can feel overwhelming with all the jargon, but it doesn’t have to be! Here’s a beginner-friendly guide to some of the most commonly used AI terms, each with a practical example: 1. Artificial Intelligence (AI) AI is a broad field of computer science focused on creating systems that can perform tasks requiring human-like intelligence, such as recognizing images, understanding speech, making decisions, and translating languages. Example: A virtual assistant like Siri or Alexa that can answer questions and control smart devices. 2. Machine Learning (ML) A subset of AI where computers learn patterns and make decisions from data. Unlike traditional programming, ML models improve their performance as they process more data. ...

Now studying more on Azure AI services, below is an in-depth look at how to map specific business requirements and use cases to the various Azure AI services. While the ultimate choice always depends on end-to-end needs—ranging from time-to-market and customization to scalability and regulatory constraints—here are some guiding principles: 1. Azure Cognitive Services When It Fits: Rapid Integration & Out-of-the-Box Capabilities: If your business needs to add AI capabilities quickly without developing and training custom models from scratch, Cognitive Services are ideal. They provide pre-built APIs for vision, speech, language, decision support, and more. Example use cases: ...

I started to study for AI102 Certification. Preparing for the AI-102 certification is an excellent way to deepen understanding of Azure AI services. Here’s a breakdown of the key services that is related with exam: Azure AI Services Overview Azure Cognitive Services – These are pre-built AI models that allow developers to integrate AI capabilities into applications. They include: Vision (e.g., Computer Vision, Face API) Speech (e.g., Speech-to-Text, Text-to-Speech, Translator) Language (e.g., Text Analytics, Language Understanding) Decision (e.g., Personalizer) Search (e.g., Azure AI Search) Azure Machine Learning – A comprehensive cloud-based service that helps in building, training, and deploying ML models efficiently. Key components include: ...