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Responsible AI

via Udacity Nanodegree

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Details

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Provider

Udacity Nanodegree
Pricing

Paid Course
Languages

English
Certificate

Certificate Available
Effort

45 hours
Sessions

Self-Paced
Level

Intermediate

Found in

Overview

This program teaches you how to turn AI governance principles into technical implementation. You'll learn Governance-as-Code by translating regulations like the EU AI Act and GDPR into automated engineering workflows. Build skills in managing, versioning, and securing structured, unstructured, and multimodal data for generative AI and RAG systems. Conduct ethical audits, implement Human-in-the-Loop safeguards, protect enterprise IP, and support Sovereign AI strategies. By the end, you'll be able to design scalable governance frameworks that improve compliance, security, transparency, and responsible AI deployment.

Syllabus

Generative AI Data Ethics

This course provides a comprehensive exploration of ethical considerations in AI development and deployment. Beginning with an introduction to responsible AI frameworks, you will grasp the significance of addressing bias in generative models and effective mitigation strategies. Key lessons cover the explainability of models, data licensing, and the auditing of data provenance. The course further delves into societal impacts, environmental sustainability, and security vulnerabilities related to data leaks. You will design ethical risk mitigation plans and explore human-in-the-loop systems. Ultimately, you will conduct a technical ethical audit of a generative AI tool, equipping them with practical skills to navigate complex ethical landscapes in AI.

Data Governance for Generative AI

This course offers a comprehensive exploration of data governance fundamentals critical for managing AI systems. You will learn about establishing AI governance foundations, regulatory compliance, and risk assessment frameworks aimed at ensuring responsible AI usage. Key lessons include implementing ISO/IEC 42001 controls, developing robust model governance, and formulating data retention policies. The course emphasizes the importance of third-party vendor governance and stakeholder engagement. Through a hands-on project, you will create a comprehensive AI governance framework, equipping you with the strategies necessary for effective oversight and management in the generative AI landscape.

Generative AI Data Management

This course provides a comprehensive exploration of the entire data lifecycle in generative AI systems. You will learn to plan and build effective data pipelines, manage multimodal data processing, and implement data versioning and lineage. The course covers essential concepts in access control, data cataloging, and synthetic data creation and evaluation. A key project involves developing an Ethical Multi-Agent Data Orchestrator. By integrating theory with practical applications, this course prepares you to proficiently manage data in AI contexts, ensuring ethical and effective use of generative technologies.