Azure AI Foundations

1. Definition and scope of artificial intelligence
2. Categories of AI workloads
   1. Machine learning workloads
   2. Computer vision workloads
   3. Natural language processing workloads
   4. Conversational AI workloads
   5. Generative AI workloads
3. Real-world applications of AI across industries
4. Overview of Microsoft Azure as an AI platform
   1. Cloud computing fundamentals
   2. Azure global infrastructure and services
   3. AI service categories in Azure

1. Importance of ethical AI in production systems
2. Core principles of responsible AI
   1. Fairness and bias mitigation
   2. Reliability and safety
   3. Privacy and security
   4. Inclusiveness and accessibility
   5. Transparency and explainability
   6. Accountability and governance
3. Risk identification in AI solutions
4. Regulatory and compliance considerations

1. Definition and purpose of machine learning
2. Types of machine learning
   1. Supervised learning
      1. Regression
      2. Classification
   2. Unsupervised learning
      1. Clustering
   3. Deep learning fundamentals
3. Key machine learning concepts
   1. Features and labels
   2. Training vs validation datasets
   3. Overfitting and underfitting
   4. Model evaluation metrics
4. Machine learning lifecycle
   1. Data ingestion and preparation
   2. Model training
   3. Model evaluation
   4. Deployment and monitoring

1. Overview of Azure Machine Learning service
2. Automated Machine Learning (AutoML)
3. Data and compute resources in Azure
4. Model management and versioning
5. Model deployment options
   1. Real-time endpoints
   2. Batch processing
6. MLOps concepts in Azure

1. Core concepts of computer vision
2. Image analysis techniques
   1. Image classification
   2. Object detection
   3. Facial detection and analysis
3. Optical Character Recognition (OCR)
4. Azure AI Vision services
5. Multimodal models and visual AI
6. Image generation and video generation concepts

1. Fundamentals of NLP
2. Text processing techniques
   1. Tokenization
   2. Statistical analysis
   3. Semantic understanding
3. NLP use cases
   1. Sentiment analysis
   2. Entity recognition
   3. Language translation
4. Azure Language services
5. Conversational AI fundamentals
   1. Chatbots and virtual assistants
   2. Language understanding systems

1. Speech recognition concepts
2. Text-to-speech synthesis
3. Speech translation
4. Azure Speech services
5. Voice-enabled applications and agents

1. Overview of document processing
2. Structured vs unstructured data
3. OCR and form recognition
4. Field extraction and mapping
5. Azure document intelligence solutions

1. Introduction to generative AI
2. Large Language Models (LLMs)
3. Prompt engineering fundamentals
4. AI agents and agentic workflows
5. Azure OpenAI and Foundry capabilities
6. Use cases of generative AI in business
7. Risks and responsible use of generative AI

1. End-to-end AI solution architecture
2. Selecting appropriate Azure services for use cases
3. Integration with applications and APIs
4. Security and scalability considerations
5. Cost optimization strategies

1. Identifying problem types and AI approaches
2. Matching workloads to services
3. Evaluating trade-offs between solutions
4. Real-world solution design thinking

This one-day course is structured to mirror the full breadth of the Microsoft AI-901 curriculum while aligning it with the latest industry shifts—particularly the integration of generative AI and modern Azure AI services, ensuring participants leave with knowledge that is both certification-ready and industry-relevant.