Mastering AWS Managed AI Services & Responsible AI

О курсе

This course is designed to provide a comprehensive understanding of Responsible AI principles and AWS Managed AI Services, enabling learners to build intelligent, trustworthy, and business-focused AI solutions on AWS. As organizations increasingly adopt artificial intelligence and generative AI technologies, it is essential to understand not only how AI services work but also how to implement them responsibly, securely, and ethically. The course begins by exploring the foundations of Responsible AI, including fairness, transparency, explainability, governance, data privacy, consent management, and human oversight. Learners will examine the risks and challenges associated with AI systems and discover AWS tools that help organizations implement responsible AI practices. The course then progresses to AWS Managed AI Services, covering solutions for natural language processing, translation, speech recognition, text-to-speech conversion, computer vision, conversational AI, intelligent search, personalization, and document intelligence. This course is structured into multiple modules, each featuring lessons and video lectures that provide both conceptual understanding and practical demonstrations. Participants will engage with approximately 2–3 hours of instructional content, ensuring both theoretical knowledge and real-world application. To reinforce learning, graded and ungraded assignments are included within each module to help learners apply Responsible AI concepts and AWS AI services in practical business scenarios. Module 1: Responsible AI on AWS Module 2: AWS Managed AI Services At the end of the course, learners will learn * Understand the principles of Responsible AI and apply best practices for developing trustworthy AI solutions. * Identify and implement AWS Managed AI Services for language processing, speech AI, computer vision, intelligent search, personalization, and document intelligence use cases. * Evaluate AI solutions from both technical and governance perspectives, incorporating explainability, human oversight, privacy, and responsible AI considerations.

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