Azure Services Fundamentals | firstconsulting-1

• Describe Artificial Intelligence workloads and considerations
• Identify features of common AI workloads
• identify prediction/forecasting workloads
• identify features of anomaly detection workloads
• identify computer vision workloads
• identify natural language processing or knowledge mining workloads
• identify conversational AI workloads
• Identify guiding principles for responsible AI
• describe considerations for fairness in an AI solution
• describe considerations for reliability and safety in an AI solution
• describe considerations for privacy and security in an AI solution
• describe considerations for inclusiveness in an AI solution describe considerations for transparency in an AI solution
• describe considerations for accountability in an AI solution
• Describe fundamental principles of machine learning on Azure
• Identify common machine learning types
• identify regression machine learning scenarios
• identify classification machine learning scenarios
• identify clustering machine learning scenarios
• Describe core machine learning concepts
• identify features and labels in a dataset for machine learning
• describe how training and validation datasets are used in machine learning
• describe how machine learning algorithms are used for model training
• select and interpret model evaluation metrics for classification and regression
• Identify core tasks in creating a machine learning solution
• describe common features of data ingestion and preparation
• describe feature engineering and selection
• describe common features of model training and evaluation
• describe common features of model deployment and management
• Describe capabilities of no-code machine learning with Azure Machine Learning studio
• automated ML UI
• azure Machine Learning designer

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