Topics
Skill: Describe Artificial Intelligence Workloads and Considerations
Part 1: Identify Features of Common AI Workloads
Session 1: Content Moderation and Personalization Workloads
- Characteristics of content moderation solutions
- Examples of personalization workloads in AI applications
Session 2: Computer Vision Workloads
- Features of image classification and object detection
- Applications of optical character recognition (OCR)
- Understanding facial detection and analysis workloads
Session 3: Natural Language Processing (NLP) Workloads
- Overview of NLP tasks, including sentiment analysis and key phrase extraction
- Features of entity recognition and language modeling
- Applications of speech recognition, synthesis, and translation
Session 4: Knowledge Mining Workloads
- Leveraging AI for extracting insights from unstructured data
- Features of document intelligence and indexing solutions
Session 5: Generative AI Workloads
- Characteristics of generative AI models
- Common scenarios, such as text, image, and code generation
- Responsible AI considerations for generative AI
Part 2: Identify Guiding Principles for Responsible AI
Session 6: Fairness in AI Solutions
- Identifying biases and ensuring equitable AI outcomes
- Techniques for testing and mitigating bias in AI systems
Session 7: Reliability and Safety in AI Solutions
- Ensuring robust and reliable AI performance
- Safety measures for deploying AI in critical environments
Session 8: Privacy and Security in AI Solutions
- Securing AI data and models against vulnerabilities
- Adhering to privacy regulations in AI systems
Session 9: Inclusiveness in AI Solutions
- Designing AI systems to be accessible and inclusive
- Considering diverse user needs and perspectives
Session 10: Transparency and Accountability in AI Solutions
- Documenting decision-making processes in AI
- Establishing accountability for AI system outcomes
Skill: Describe Fundamental Principles of Machine Learning on Azure
Part 1: Identify Common Machine Learning Techniques
Session 11: Regression, Classification, and Clustering Scenarios
- Identifying scenarios for regression, classification, and clustering
- Examples of applications for each machine learning technique
Session 12: Features of Deep Learning Techniques
- Characteristics of deep learning models and architectures
- Common use cases for neural networks
Part 2: Describe Core Machine Learning Concepts
Session 13: Understanding Datasets in Machine Learning
- Identifying features and labels in datasets
- Differences between training and validation datasets
Session 14: Azure Machine Learning Capabilities
- Overview of automated machine learning
- Data and compute services available in Azure Machine Learning
- Model management and deployment capabilities
Skill: Describe Features of Computer Vision Workloads on Azure
Part 1: Identify Common Computer Vision Solutions
Session 15: Image Classification and Object Detection
- Features and examples of image classification solutions
- Applications of object detection in various industries
Session 16: Optical Character Recognition (OCR) and Facial Analysis
- Features of OCR for digitizing text
- Capabilities of facial detection and analysis solutions
Part 2: Azure Tools for Computer Vision
Session 17: Azure AI Vision Service
- Capabilities of the Azure AI Vision service
- Configuring and deploying vision solutions in Azure
Session 18: Azure AI Face Detection Service
- Features of the Azure AI Face detection service
- Use cases for facial recognition and analysis
Skill: Describe Features of Natural Language Processing (NLP) Workloads on Azure
Part 1: Identify Common NLP Workload Scenarios
Session 19: Key Phrase Extraction and Entity Recognition
- Features and use cases for key phrase extraction
- Applications of entity recognition in real-world scenarios
Session 20: Sentiment Analysis and Language Modeling
- Identifying use cases for sentiment analysis
- Applications of language modeling in NLP
Session 21: Speech Recognition, Synthesis, and Translation
- Features and applications of speech-to-text and text-to-speech
- Scenarios for real-time translation solutions
Part 2: Azure Tools for NLP
Session 22: Azure AI Language Service
- Capabilities of the Azure AI Language service
- Examples of implementing NLP tasks using Azure
Session 23: Azure AI Speech Service
- Features of the Azure AI Speech service
- Configuring speech recognition and synthesis tasks
Skill: Describe Features of Generative AI Workloads on Azure
Part 1: Identify Features of Generative AI Solutions
Session 24: Common Features and Scenarios
- Overview of generative AI models and their applications
- Scenarios for text, image, and code generation
Session 25: Responsible AI for Generative AI
- Ensuring fairness, privacy, and security in generative AI
- Addressing challenges in transparency and accountability
Part 2: Azure OpenAI Service
Session 26: Capabilities of Azure OpenAI Service
- Natural language generation capabilities of Azure OpenAI Service
- Features for code and image generation using Azure OpenAI Service
