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⇱ AWS Tools and Services for AI | Coursera

AWS Tools and Services for AI

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AWS Tools and Services for AI

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

Recommended experience

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

Recommended experience

9 hours to complete
Flexible schedule
Learn at your own pace

What you'll learn

  • Understand Generative AI foundations by exploring diffusion models, LLMs, and multi-modal systems that power modern intelligent cloud applications

  • Build enterprise AI solutions using Amazon Bedrock, apply structured prompt engineering, and boost productivity with AI assistance from Amazon Q

  • Implement Retrieval-Augmented Generation (RAG), connect real-time data to models, and understand its practical strengths, limits, and use cases

  • Deploy scalable AI using AWS managed services, choose the right databases for embeddings, and apply model evaluation after production rollout

Details to know

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Assessments

12 assignments

Taught in English

Build your subject-matter expertise

This course is part of the AWS AI Practitioner Certification Prep Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate

There are 2 modules in this course

The next generation of cloud applications is powered by intelligent systems—and AWS Tools and Services for AI is your gateway into building them. This course takes you from the core foundations of Generative AI into the technologies that drive modern innovation.

You’ll explore diffusion models for content generation, transformer-based Large Language Models (LLMs) behind conversational AI, and multi-modal models that unify text, images, and data into a single intelligent pipeline. You’ll then step into enterprise-grade AI development using foundation models with Amazon Bedrock, master high-impact prompt creation through structured prompt engineering, and accelerate productivity with AI-powered assistance from Amazon Q. The journey advances into Retrieval-Augmented Generation (RAG), where models learn from real-time data—along with its strengths and limitations. You’ll work with AWS AI managed services, select the right AWS databases for embedding storage, apply model evaluation techniques, and learn how to continuously evaluate and refine models after deployment. By the end, you won’t just learn AI—you’ll be ready to deploy it with confidence at scale

This module introduces the core principles of Generative AI, covering foundational concepts, language models, and prompt engineering. Learners also explore practical applications using Amazon Bedrock and Amazon Q.

What's included

32 videos5 readings7 assignments

32 videos•Total 110 minutes
  • Course Introduction•4 minutes
  • Learning Objectives•1 minute
  • Fundamentals of Generative AI •5 minutes
  • Demo : Getting Familiar with AWS AI Services•6 minutes
  • Diffusion Models•3 minutes
  • Transformer-Based Large Language Models (LLMs)•3 minutes
  • Multi-modal Models•2 minutes
  • Key Features of Amazon Bedrock•5 minutes
  • Amazon Bedrock – Use Cases•1 minute
  • Demo- Bedrock overview•5 minutes
  • Understanding GPT•2 minutes
  • Implementing Generative AI with Amazon Bedrock•4 minutes
  • Summary•2 minutes
  • Learning Objectives•1 minute
  • Key Concepts in Language Models: Part-1•5 minutes
  • Key Concepts in Language Models: Part-2•5 minutes
  • Prompts and Prompt Engineering•2 minutes
  • Designing a Prompt•5 minutes
  • Hyperparameter•3 minutes
  • Choosing Top-p, Top-k and Temperature•7 minutes
  • Prompt Engineering Techniques•4 minutes
  • Demo- Prompt Engineering Techniques•5 minutes
  • Prompt Engineering Risks•3 minutes
  • Prompt Templates•1 minute
  • Demo-Prompt Optimization Parameters•7 minutes
  • Generative AI Foundation Models•3 minutes
  • Practical Applications of Generative AI•3 minutes
  • Amazon Q•1 minute
  • Key Components of Amazon Q•2 minutes
  • Developer Fundamentals on Amazon Q•3 minutes
  • Demo- Amazon Q Walkthorugh•8 minutes
  • Summary•2 minutes
5 readings•Total 50 minutes
  • Bedrock Cost Considerations•10 minutes
  • Amazon Bedrock Agents•10 minutes
  • Gen AI: Measuring Business Efficiency and Value•10 minutes
  • Optimizing Prompts•10 minutes
  • Party Rock - Playground for Gen AI apps•10 minutes
7 assignments•Total 120 minutes
  • Core Concepts of Generative AI and Amazon Bedrock•15 minutes
  • Language Models, Prompt Engineering, and Amazon Q•30 minutes
  • Core Concepts of Generative AI•15 minutes
  • Amazon Bedrock•15 minutes
  • Language Models and Prompt Engineering•15 minutes
  • Prompt Engineering Techniques•15 minutes
  • Generative AI Foundation Models and Amazon Q•15 minutes

This module explores how Retrieval-Augmented Generation (RAG) enhances AI responses with relevant data and how to optimize AI models for performance and efficiency using AWS AI services.

What's included

23 videos4 readings5 assignments

23 videos•Total 105 minutes
  • Learning Objectives•1 minute
  • RAG (Retrieval-Augmented Generation)•5 minutes
  • Applications of RAG•2 minutes
  • RAG – Advantages and Disadvantages•5 minutes
  • AWS AI Managed Services Part-1•6 minutes
  • Demo- Amazon Transcribe in Action•4 minutes
  • Demo- Amazon Translate Walkthrough•8 minutes
  • Demo-Simplifying Text Analysis with Comprehend•7 minutes
  • Demo- Exploring Amazon Polly•6 minutes
  • AWS AI Managed Services Part-2•5 minutes
  • Demo- Understanding Amazon Lex•10 minutes
  • Demo-Amazon Rekognition Walkthrough•7 minutes
  • Demo- Amazon Textract Overview•5 minutes
  • AWS Database Options for Storing Embeddings•5 minutes
  • Summary•2 minutes
  • Learning Objectives•0 minutes
  • Model Pre-Training and Fine-Tuning with AWS •5 minutes
  • Fine-Tuning for Conversational AI•2 minutes
  • Demo- Fine-Tuning Bedrock Model•8 minutes
  • Model Evaluation Techniques•5 minutes
  • Evaluate and Refine Models Post-deployment•5 minutes
  • Summary•1 minute
  • Course Completion•2 minutes
4 readings•Total 40 minutes
  • Amazon Mechanical Turk•10 minutes
  • Amazon Personalize•10 minutes
  • Industry-Specific Use Cases for Model Evaluation•10 minutes
  • Advanced Guidelines for Continuous Model Optimization•10 minutes
5 assignments•Total 105 minutes
  • Retrieval-Augmented Generation (RAG) and AWS AI Services•30 minutes
  • Model Optimization•30 minutes
  • Retrieval-Augmented Generation (RAG)•15 minutes
  • AWS AI Managed Services•15 minutes
  • Model Optimization•15 minutes

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Frequently asked questions

The AI Practitioner course covers Amazon Bedrock for creating generative AI apps, Amazon Q , and AWS AI Managed Services - Transcribe, Translate, Comprehend, Polly, Lex, Rekognition and Textract.

Yes, a course completion certificate is provided upon completing all graded assignments and quizzes present in the AWS AI Practitioner course.

Absolutely. AI-focused positions are in great demand, and this course will provide you with the practical skills you need to stand out as an AI analyst, AI product manager, or Cloud practitioner. 

Yes, once enrolled, you will have continuing access to all materials, demos, and forum conversations to help you review and improve your AI expertise at any time.

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Financial aid available,