1. Plan and manage an Azure AI solution

• Understanding Azure AI Capabilities: Introduction to Azure AI services, including their capabilities and use cases.
• Azure AI Services Resources: Creating and configuring Azure resources for AI solutions, including security, monitoring, and cost management.
• Responsible AI: Understanding and applying Microsoft's responsible AI principles, which include fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability.
• Deploying AI Services in Containers: Packaging and deploying Azure AI services in containers for portability and reusability. 

2. Implement computer vision solutions

• Image Analysis and Object Detection: Utilizing Azure AI Vision services to analyze images, detect objects, and extract information.
• Optical Character Recognition (OCR): Implementing OCR to extract text from images and documents.
• Face Detection, Analysis, and Recognition: Working with face detection, analysis, and recognition capabilities using Azure AI Vision.
• Custom Vision Models: Training and deploying custom image classification models using Azure AI Custom Vision.
• Video Analysis: Describing and leveraging Azure Video Indexer to analyze video content. 

3. Implement natural language processing solutions

• Text Analysis and Translation: Analyzing text for sentiment, key phrase extraction, named entity recognition, and language detection using Azure AI Language.
• Question Answering: Building and integrating knowledge bases for question-and-answer solutions using Azure AI Language.
• Speech Recognition and Synthesis: Implementing speech-to-text and text-to-speech capabilities using Azure AI Speech.
• Speech Translation: Translating spoken language in real-time using Azure AI Speech.
• Conversational Language Understanding (CLU): Building custom conversational models using Azure AI Language. 

4. Implement knowledge mining solutions

• Azure AI Search: Creating and managing an Azure AI Search solution, including custom skills, enrichment pipelines, and knowledge stores.
• Azure AI Document Intelligence: Utilizing Azure AI Document Intelligence for extracting information from documents and forms.
• Custom Skill Implementation: Developing custom skills to extend the capabilities of Azure AI Search. 

5. Implement generative AI solutions

• Azure OpenAI Service: Getting started with Azure OpenAI Service, including deploying base models, generating completions, and managing model parameters.
• Building Natural Language Solutions: Integrating Azure OpenAI into applications for natural language understanding and generation.
• Prompt Engineering: Understanding and applying prompt engineering techniques to optimize the use of large language models.
• Code and Image Generation: Using Azure OpenAI to generate code, build unit tests, understand complex code, and create images with DALL-E models.
• Retrieval Augmented Generation (RAG): Implementing RAG with Azure OpenAI Service to leverage custom data.
• Responsible Generative AI: Addressing ethical considerations in generative AI development, including fairness, bias detection, and mitigating harms. 

6. Implement agentic solutions

• Building Conversational AI Solutions: Implementing intelligent chatbots using Azure Bot Service and integrating with Cognitive Services.
• Integrating Cognitive Services with Bots: Enhancing bot functionality with natural language understanding, speech recognition, and other AI capabilities.