AI-900 - Azure AI Fundamentals: Artificial Intelligence Principles





Artificial intelligence (AI) and machine learning (ML) are solving a significant number of business and social problems and giving computers a new way to handle and process vast amounts of data. In this course, you'll learn about AI principles, beginning with fairness, reliability, and safety in AI algorithms, privacy and security for responsible AI, and inclusiveness, transparency, and accountability in AI algorithms. Then you’ll dig into Azure AI’s capabilities, Machine Language Operations (MLOps), Azure AI model management, and Azure AI model training. Finally, you’ll explore key concepts surrounding Azure AI content safety and Azure reinforcement learning. This course is one of a collection that prepares learners for the Microsoft Azure AI Fundamentals (AI-900) exam.




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AI-900 - Azure AI Fundamentals: Artificial Intelligence Principles

  • provide an overview of how the principle of fairness in artificial intelligence (AI) algorithms results in responsible AI
  • outline how to ensure responsible AI algorithms through reliability and safety
  • outline how privacy and security must be considered when responsibly creating and using AI solutions
  • provide an overview of how inclusiveness in AI algorithms can benefit everyone
  • outline how transparency should be used with AI algorithms in a responsible manner
  • identify how governance and organizational policies provide accountability for AI responsibility
  • provide an overview of Azure and AI service capabilities
  • outline the purpose, features, and characteristics of MLOps
  • provide an overview of Azure AI model management capabilities, including pipelines, working with assets, how to manage models, and Git integration
  • provide an overview of the purpose of training AI models
  • provide an overview of Azure AI Content Safety and Content Moderator and how they aid in the dealing with harmful content in applications and services
  • review the features and benefits of Azure ML reinforcement learning and how it delivers personalized and relevant experiences for users


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