Extract insights from visual data on Azure Eğitimi

Visual data is becoming a critical asset for modern organisations. This course explores how to build intelligent applications that can interpret, analyse, and reason over images and documents using Azure AI services and multimodal models.

We believe organisations that can combine data, AI, and cloud technologies will unlock faster and more accurate decision-making. This course focuses on applying multimodal and agent-based AI patterns to extract structured insights from visual inputs and integrate them into real-world workflows. Learners will gain practical experience in designing solutions that combine visual understanding with language models, enabling applications that move from perception to action within Azure environments.

Participants should have:

• Basic programming experience in a language such as Python
• A general understanding of cloud computing or artificial intelligence concepts
• Familiarity with data handling and application development workflows is beneficial
• No prior experience in computer vision is required

Target audience

This course is designed for:

• Developers building intelligent and data-driven applications
• AI engineers working with multimodal and generative AI solutions
• Technical professionals implementing Azure-based AI services
• Teams looking to integrate visual data into decision-making workflows

By the end of this course, learners will be able to:

• Build applications that analyse and reason over visual data using Azure AI services
• Combine image and document inputs with language models for enhanced understanding
• Implement multimodal and agent-based workflows for AI applications
• Extract structured information from images and documents
• Design pipelines that ground AI responses in visual and document context
• Apply design patterns for orchestrating tools and services in Azure AI solutions

Introduction to visual and multimodal AI

• Overview of visual AI and its role in modern applications
• Understanding multimodal models and their capabilities
• Key use cases for image and document intelligence
• Challenges in processing and interpreting visual data

Working with Azure AI services for visual data

• Introduction to Azure AI vision and document intelligence services
• Processing images and extracting metadata
• Analysing documents for structured and unstructured information
• Integrating visual services into application workflows

Multimodal models for images and documents

• Combining visual and language inputs in AI systems
• Understanding embeddings for multimodal data
• Enhancing context through cross-modal reasoning
• Use cases for multimodal generative AI applications

Structured information extraction from visual inputs

• Techniques for extracting entities and key-value pairs from documents
• Processing forms, receipts, and structured layouts
• Handling unstructured visual data sources
• Improving accuracy through preprocessing and validation

Grounding AI responses in visual data

• Principles of grounding in multimodal AI systems
• Linking model outputs to visual context
• Reducing hallucinations through grounded responses
• Designing systems that maintain traceability to source data

Agent-based orchestration for visual workflows

• Introduction to agent-based AI patterns
• Orchestrating tools and services for visual data processing
• Designing workflows that combine reasoning and action
• Integrating APIs and external tools into AI pipelines

Designing decision-making workflows using visual data

• Building end-to-end pipelines for insight extraction
• Connecting visual analysis to business decision processes
• Automating actions based on extracted insights
• Monitoring and improving workflow performance over time

Exams and assessments

There are no formal exams included in this course. Learners will complete interactive knowledge checks and practical exercises to reinforce their understanding of multimodal AI concepts and visual data processing techniques.

Hands-on learning

This course includes:

• Guided labs using Azure AI services for image and document analysis
• Practical exercises in multimodal model integration
• Scenario-based tasks focused on real-world visual data challenges
• Instructor-led discussions on designing production-ready AI workflows