Designing and Implementing Microsoft Azure AI Solutions is a practical, hands-on training program designed for professionals who want to develop advanced skills in building intelligent applications and AI solutions using Microsoft Azure. The course explores how Azure AI services, machine learning technologies, generative AI, and modern development tools can be combined to create scalable and business-ready AI solutions.

Designing and Implementing Microsoft Azure AI Solutions Course Overview

This course provides developers and AI engineers with the practical knowledge required to design, build, deploy, secure, and monitor artificial intelligence solutions on Microsoft Azure. Participants explore Microsoft Foundry, Azure AI services, Azure OpenAI models, generative AI, AI agents, computer vision, language and speech processing, Azure AI Search, and document intelligence.

The course emphasizes selecting suitable Azure services, integrating AI capabilities into applications through SDKs and REST APIs, grounding generative AI models with organizational data, and applying responsible AI principles throughout the solution lifecycle.

 

Duration 5 Days – 35 hrs. 

 

Learning Objectives

• Plan and design secure, scalable, and responsible Azure AI solutions.
• Select appropriate Microsoft Foundry services and AI models for different business requirements.
• Provision and manage Azure AI resources, projects, model deployments, and endpoints.
• Integrate Azure AI capabilities into applications using Python or C#, SDKs, and REST APIs.
• Build generative AI applications with Azure OpenAI models.
• Apply prompt-engineering techniques to improve model responses.
• Implement retrieval-augmented generation solutions grounded in enterprise data.
• Design and implement custom AI agents and agent-based workflows.
• Develop computer vision and image-analysis solutions.
• Build natural language processing, speech, translation, and conversational solutions.
• Create knowledge-mining and intelligent-search solutions with Azure AI Search.
• Extract structured information using Azure AI Document Intelligence and Azure Content Understanding.
• Apply authentication, access control, content safety, monitoring, and cost-management practices.
• Deploy, optimize, troubleshoot, and maintain production-ready Azure AI solutions.

 

Target Audience 

• AI engineers
• Software developers
• Cloud developers
• Application developers
• Solutions architects
• Data scientists transitioning into AI engineering
• Machine learning engineers
• DevOps engineers supporting AI applications
• Technical consultants responsible for Azure AI implementations
• IT professionals preparing to build enterprise AI solutions on Microsoft Azure

 

Prerequisites 

• Working knowledge of Microsoft Azure and its core services.
• Experience creating and managing Azure resources.
• Intermediate programming experience in Python or C#.
• Familiarity with REST APIs, JSON, and software development concepts.
• Basic understanding of artificial intelligence and machine learning concepts.
• Familiarity with authentication, authorization, and cloud security principles.
• Basic knowledge of source control and application deployment.
• Access to an Azure subscription with permission to create the required resources.

Course Outline 

Day 1 — Planning and Managing Azure AI Solutions

Module 1: Introduction to AI Solution Development on Azure

• Azure AI solution-development lifecycle
• Microsoft Foundry capabilities and architecture
• Azure AI services and supported workloads
• Generative AI, vision, language, speech, and information-extraction scenarios
• Selecting services and models based on solution requirements
• Data, networking, scalability, availability, and cost considerations

 Module 2: Creating and Deploying Microsoft Foundry Resources

• Creating Azure AI resources
• Configuring Foundry hubs and projects
• Exploring the model catalog
• Selecting and deploying AI models
• Configuring endpoints and deployment options
• Using Azure AI SDKs and REST APIs
• Container and edge-deployment considerations

 Module 3: Securing, Monitoring, and Governing AI Solutions

• Key-based and Microsoft Entra ID authentication
• Role-based access control and managed identities
• Protecting endpoints, credentials, and application secrets
• Network security and private connectivity
• Diagnostic settings, logging, and resource monitoring
• Usage, performance, quotas, and cost management
• Responsible AI principles and governance
• Content filtering, blocklists, prompt shields, and harm detection

  

Day 2 — Generative AI Solutions with Azure OpenAI

Module 4: Building Generative AI Applications

• Generative AI and large language model concepts
• Azure OpenAI models in Microsoft Foundry
• Model selection and deployment
• Tokenization, context windows, and model parameters
• Generating natural-language and code responses
• Working with multimodal models
• Integrating deployed models into applications

 Module 5: Prompt Engineering and Model Optimization

• Designing system and user prompts
• Prompt templates and reusable prompt patterns
• Zero-shot, few-shot, and structured prompting
• Controlling response format, relevance, and creativity
• Grounding prompts with contextual information
• Reducing hallucinations and improving response quality
• Evaluating model and prompt performance
• Tracing, monitoring, and collecting user feedback
• Fine-tuning and model-customization considerations

 Module 6: Retrieval-Augmented Generation

• Retrieval-augmented generation architecture
• Preparing and chunking organizational data
• Creating embeddings and vector representations
• Implementing vector and hybrid search
• Grounding model responses with retrieved content
• Adding citations and source attribution
• Managing conversation history and context
• Securing enterprise data in generative AI applications

  

Day 3 — Agentic, Language, and Speech Solutions

Module 7: Developing Agentic AI Solutions

• AI agent concepts and business use cases
• Microsoft Foundry Agent Service
• Configuring agent resources, instructions, and models
• Connecting agents to tools and enterprise data
• Implementing function and tool calling
• Managing conversations, threads, and agent state
• Designing workflows with multiple agents
• Testing, optimizing, securing, and deploying agents

 Module 8: Implementing Natural Language Processing

• Azure AI Language capabilities
• Sentiment analysis and opinion mining
• Key-phrase extraction and language detection
• Named-entity recognition and personally identifiable information detection
• Text classification and custom named-entity recognition
• Summarization
• Question-answering solutions
• Multilingual language-processing scenarios

 Module 9: Implementing Speech and Translation Solutions

• Speech-to-text and text-to-speech
• Speech translation
• Voice configuration and speech synthesis
• Custom speech considerations
• Azure AI Translator
• Text and document translation
• Custom translation models
• Integrating speech and translation into applications

  

Day 4 — Computer Vision and Intelligent Document Processing

Module 10: Implementing Computer Vision Solutions

• Azure AI Vision capabilities
• Image analysis and visual-feature extraction
• Optical character recognition
• Object detection and image classification
• Spatial and video-analysis scenarios
• Facial analysis and responsible-use considerations
• Custom vision models
• Integrating vision capabilities into applications

 Module 11: Implementing Document Intelligence Solutions

• Azure AI Document Intelligence architecture
• Selecting prebuilt document models
• Extracting text, tables, fields, and structure
• Building custom extraction models
• Training, testing, and publishing custom models
• Creating composed models
• Processing documents programmatically
• Confidence scores, validation, and exception handling

 Module 12: Implementing Content Understanding

• Azure Content Understanding capabilities
• Processing documents, images, audio, and video
• Designing analyzers and output schemas
• Extracting entities, tables, attributes, and media content
• Classification and summarization
• Building multimodal content-processing pipelines
• Integrating extracted information into downstream applications

 

 Day 5 — Knowledge Mining, Integration, and Operationalization

Module 13: Implementing Azure AI Search

• Azure AI Search architecture
• Creating search services, indexes, and data sources
• Configuring indexers and skillsets
• Enriching content with built-in and custom skills
• Implementing keyword, semantic, vector, and hybrid search
• Filtering, sorting, faceting, and query syntax
• Knowledge-store projections
• Security trimming and access control
• Integrating Azure AI Search with generative AI applications

 Module 14: Integrating and Deploying End-to-End AI Solutions

• Designing an end-to-end Azure AI architecture
• Integrating AI services with applications
• Managing configuration, endpoints, and credentials
• Implementing resilient API calls and error handling
• Scaling AI workloads
• Containerizing AI applications and services
• Incorporating AI resources into CI/CD pipelines
• Managing model and application versions
• Production deployment considerations

 Module 15: Operating and Improving Azure AI Solutions

• Monitoring model, endpoint, and application performance
• Collecting traces, logs, metrics, and user feedback
• Evaluating generative AI output quality and safety
• Identifying performance and resource bottlenecks
• Managing capacity, quotas, latency, and costs
• Troubleshooting common integration and deployment issues
• Maintaining models, indexes, prompts, and data sources
• Applying continuous improvement and responsible AI governance