Focus on this objective today: Apply language model text analysis: Configure detection of sentiment, tone, safety issues, and sensitive content. Explain it in your own words, apply it to one realistic scenario, and verify the details against the official source. Source context: Read in English Scale responsible AI governance with Azure AI Content Safety and Microsoft Foundry Module 7 Units Feedback Advanced AI Engineer Solution Architect Azure Microsoft Foundry Scale responsible AI governance to enterprise multi-agent systems using Azure AI Content Safety and Microsoft Foundry. Design systematic fairness and bias monitoring pipelines for agent decision chains, implement transparency and explainability mechanisms for complex multi-agent outputs, configure privacy protection for multi-agent data pipelines, and establish audit trail frameworks for enterprise accountability. Learning objectives By the end of this module, you'll be able to: Design systematic fairness and bias monitoring pipelines for multi-agent decision chains Implement transparency and explainability mechanisms that trace complex agent outputs to their sources Configure privacy protection and data minimization for multi-agent data handling pipelines Establish accountability frameworks with complete audit trails for enterprise agent deployments Add Prerequisites Before starting this module, you should have: Understanding of responsible AI principles and content filtering in Microsoft Foundry Experience implementing content safety controls with Azure AI Content Safety Familiarity with the Azure AI Evaluation SDK Experience deploying agents to Microsoft Foundry Proficiency in Python Get started with Azure Choose the Azure account that's right for you.
Objective
Apply language model text analysis: Configure detection of sentiment, tone, safety issues, and sensitive content