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Mastering AWS Managed AI Services & Responsible AI

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Whizlabs

Mastering AWS Managed AI Services & Responsible AI

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Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

5 hours to complete
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

5 hours to complete
Flexible schedule
Learn at your own pace

What you'll learn

  • Explain Responsible AI principles and best practices.

  • Use AWS AI services for common business use cases.

  • Select the right AWS AI service for specific requirements.

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Recently updated!

June 2026

Assessments

5 assignments

Taught in English

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There are 2 modules in this course

In this module, you'll build a strong foundation in Responsible AI concepts and learn how to develop, deploy, and manage AI systems in a safe, ethical, and trustworthy manner on AWS. You'll begin by exploring the key principles of Responsible AI, including fairness, transparency, accountability, governance, and human oversight, and understand why these principles are essential for modern AI applications. As you progress, you'll examine responsible model selection practices, legal and regulatory considerations surrounding generative AI, and best practices for collecting and managing data. You'll also learn the importance of data privacy, consent management, and protecting sensitive information throughout the AI lifecycle. The section further introduces AWS services and tools that support Responsible AI initiatives, including Guardrails, Explainable AI capabilities, Amazon Mechanical Turk, and Amazon Augmented AI (A2I). You'll discover how these services help organizations improve model transparency, incorporate human review processes, and maintain governance and compliance standards while deploying AI solutions at scale. By the end of this section, you'll have a solid understanding of Responsible AI principles, governance frameworks, data privacy considerations, and the AWS tools available to build trustworthy, transparent, and human-centered AI systems.

What's included

9 videos2 readings2 assignments1 discussion prompt

In this module, you'll explore AWS Managed AI Services and learn how to add powerful artificial intelligence capabilities to applications without building machine learning models from scratch. You'll begin with AWS services for natural language processing and speech AI, including Amazon Comprehend, Amazon Translate, Amazon Transcribe, and Amazon Polly. Through guided demonstrations, you'll discover how these services can analyze text, translate languages, convert speech to text, and generate natural-sounding speech for a wide range of business applications.You'll then expand your knowledge into computer vision, conversational AI, intelligent search, personalization, and document intelligence. Using services such as Amazon Rekognition, Amazon Lex, Amazon Kendra, Amazon Personalize, and Amazon Textract, you'll learn how organizations can extract insights from images and documents, build conversational chatbots, create intelligent search experiences, and deliver personalized recommendations to users.Through practical examples and demonstrations, you'll see how AWS Managed AI Services can be integrated into real-world solutions to improve customer experiences, automate business processes, and accelerate AI adoption while reducing the complexity of traditional machine learning development.By the end of this section, you'll understand the capabilities, use cases, and implementation considerations of AWS Managed AI Services and how they can be leveraged to build intelligent, scalable, and business-focused AI applications

What's included

15 videos3 readings3 assignments

Instructor

Whizlabs Instructor
Whizlabs
169 Courses126,586 learners

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