Introduction to AI in Azure (AI-901T00-A) Training in Greece

Artificial intelligence is rapidly changing how modern applications are designed, developed, and scaled. When combined with cloud technologies, AI can help organisations solve real business problems more quickly and build smarter digital solutions.

This one-day course introduces the foundational concepts of artificial intelligence and explores the Microsoft Azure services used to create intelligent applications and AI agents.

Participants explore core AI workloads including Generative AI, Natural Language Processing, Speech, and Computer Vision. The programme also introduces Azure AI services and development tools, giving learners a practical starting point for creating simple AI-powered solutions.

The course also covers Responsible AI, ethical considerations, service selection, deployment, monitoring, and the integration of AI capabilities into existing workflows.

Aligned with Microsoft's updated AI curriculum, this programme provides a clear entry point for learners beginning their journey into AI development.

There are no formal prerequisites for attending this course.

However, participants will benefit from having:

• A basic understanding of programming concepts, ideally with Python,
• General familiarity with Cloud Computing principles,
• Awareness of basic cloud concepts such as Compute, Storage, and Authentication,
• An interest in AI technologies and their real-world applications.

This course is particularly suitable for:

• Aspiring AI solution developers,
• Junior technologists exploring AI capabilities in Azure,
• Students and career switchers moving into AI or cloud-related roles,
• Professionals who want to understand how AI can be applied in Azure environments,
• Technical learners beginning to work with AI-powered applications and agents.

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

• Describe fundamental Artificial Intelligence and Machine Learning concepts,
• Identify common AI workloads including Generative AI, NLP, Speech, and Computer Vision,
• Explain the core principles of Responsible AI and ethical AI development,
• Recognise the capabilities of Azure AI services,
• Build and deploy simple AI-powered applications,
• Understand how basic AI agents can be created and integrated into workflows,
• Continue developing AI skills through Microsoft Learn and Foundry modules.

1. Introduction to AI Fundamentals

• What is Artificial Intelligence?
• What is Machine Learning?
• The relationship between AI and ML
• How AI is used across industries
• Traditional AI and modern AI approaches
• Real-world AI use cases
• How AI-powered applications support business processes

2. Understanding AI Workloads

• Common AI workload categories
• Introduction to Generative AI
• Business impact of Generative AI
• Natural Language Processing
• Text Analysis scenarios
• Speech Recognition
• Speech Synthesis
• Computer Vision
• Image and Video Analysis
• Selecting suitable approaches for different AI workloads

3. Responsible AI and Ethics

• Core Responsible AI principles
• Fairness
• Transparency
• Accountability
• Understanding Bias
• Reducing bias in AI systems
• Supporting ethical AI outcomes
• Governance and Compliance considerations
• Building trust and responsibility into AI solutions

4. Introduction to Azure AI Services

• Overview of the Azure AI portfolio
• How Azure supports the AI development lifecycle
• Language Services
• Speech Services
• Vision Services
• Decision-making capabilities
• Selecting the right Azure service for different AI workloads
• Combining Azure AI services

5. Building AI Applications and Agents

• Introduction to Microsoft Foundry
• AI development tools
• Creating simple AI-powered applications
• Connecting Azure AI services to applications
• Introduction to AI agents
• Basic AI agent design
• Agent deployment
• Integrating AI agents into existing workflows

6. Working with Azure AI Services

• Using Azure AI Language
• Text Analysis and NLP scenarios
• Building voice-enabled applications with Speech Services
• Speech Recognition and Speech Synthesis
• Using Vision Services for Image Analysis
• Video Analysis scenarios
• Exploring Document Intelligence
• Extracting information from documents
• Applying different AI services to practical scenarios

7. Deploying and Managing AI Solutions

• Deploying lightweight AI applications in Azure
• Monitoring AI services
• Managing AI resources
• Scalability considerations
• Performance considerations
• Managing AI solutions in production
• Best practices for maintenance and operations

8. Putting It All Together

• End-to-end AI-powered application walkthrough
• Combining multiple Azure AI services into one solution
• Real-world AI use case demonstration
• Understanding the flow from application design to deployment
• Recommended next learning steps
• Microsoft Learn resources
• Foundry learning modules
• Overview of further training and certification pathways