Module Introduction

Welcome to the next lesson: using Azure AI Services for enterprise applications. This module shifts focus from building and integrating AI capabilities to operating them reliably at scale. You’ll learn practical operational skills to secure, monitor, and deploy Azure AI Services so your solutions run securely, perform well, and remain cost‑efficient in production environments. You will cover three core areas that matter for production-grade AI:

Authenticate and secure AI services
- Manage API keys and secrets safely
- Store secrets centrally with Azure Key Vault
- Use Azure Active Directory and managed identities to enforce least‑privilege access
- Implement role‑based access control (RBAC) and network isolation via private endpoints and VNETs
Monitor and optimize AI usage
- Track metrics and usage to understand cost and performance drivers
- Collect logs and traces with Azure Monitor, Log Analytics, and Application Insights
- Analyze latency, error rates, and throughput to guide autoscaling and cost optimization
Deploy AI services in containers
- Containerize models and inference components using Docker best practices
- Run containers locally, in Azure Container Instances (ACI), or on Azure Kubernetes Service (AKS)
- Use Azure Container Registry (ACR) or private registries for controlled image distribution
- Plan for edge deployments and air‑gapped or private‑network scenarios where public endpoints are not available

Why this matters: securing access, instrumenting services early, and using containers for predictable deployments are essential to running AI in production—whether you support global enterprise systems, regulated industries, or edge devices. Key concepts and examples at a glance:

Focus Area	Primary Goal	Example Azure Services
Authentication & Security	Protect credentials and limit access	Azure Key Vault, Azure AD, Managed Identities, Private Endpoints, VNETs
Monitoring & Optimization	Observe behavior and control costs	Azure Monitor, Log Analytics, Application Insights
Containerized Deployment	Package and run inference reliably	Docker, ACR, ACI, AKS

A presentation slide titled "Learning Objectives." It lists three numbered goals: Authenticate and secure AI services; Monitor and optimize AI usage; and Deploy AI services in containers.

Security and monitoring are foundational. Prefer managed identities and Key Vault over long-lived keys, and instrument your services early so you can measure and optimize before traffic grows.

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Working with Azure AI Services

Securing Azure AI Services

Introduction

Introduction to AI and Azure AI Services

Get Started with Azure AI Services

Using Azure AI Services for Enterprise Applications

Analyzing Videos

Analyzing Text

Translating Text

Develop a Question Answering Solution

Develop a Conversational Language Understanding App

Custom Classification and Named Entity Extraction

Speech Recognition, Translation, and Synthesis

Get Started with Azure OpenAI Service

Develop Apps with Azure OpenAI Service

Apply Prompt Engineering

Implement Retrieval Augmented Generation (RAG) with Azure OpenAI Service

Implementing an Intelligent Search Solution

Create a Custom Skill for Azure AI Search

Creating a Knowledge Store

Develop a Document Intelligence Solution

Analyze and Manipulate Images

Detecting Faces with the Azure AI Vision

Custom Vision Models with Azure AI Custom Vision

Module Introduction

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