AWS AI Practitioner Certification Prep

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Overview

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Step into the world of Artificial Intelligence on AWS with a specialization designed to take you from cloud foundations to responsible, production-ready AI systems. The AWS AI Practitioner prep Specialization is ideal for students, IT professionals, cloud engineers, data analysts, and aspiring AI practitioners—no prior AI experiencerequired, just curiosity and ambition. You’ll begin with the basics of AWS, including EC2, networking, databases, andsecurity. Then check out the very basics of AI, Machine Learning, Feature Engineering, NLP, Deep Learning, and AWS Data Wrangler. The course goes deep into Generative AI, discussing models, prompt engineering, hyperparameters, Amazon Q, Amazon Bedrock, and model training and evaluation. What’s unique about this AWS Certified AI practitioner prep specialization is that it focuses on Responsible AI and educates around ethical principles, legal risks, MLOps, data encryption, IAM policies, and governance. With hands-on projects like developing secure chatbots and testing generative models, you’ll develop job-ready skills. By the end, you’ll be prepared for roles such as AI Practitioner, GenAI Engineer, or ML Support Engineer, with excellent job prospects and career growth in the AWS AI ecosystem. Disclaimer: AWS and Amazon Web Services are trademarks of Amazon.com, Inc. or its affiliates. This course is not affiliated with or endorsed by AWS.

Syllabus

Course 1: Introduction to AI and Machine Learning
Course 2: AWS Tools and Services for AI
Course 3: Responsible AI with AWS Security and Governance

Courses

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8 hours 50 minutes
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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
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8 hours 44 minutes
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Ready to witness the future of technology? AI and Machine learning are more than buzzwords—they drive industry innovation. This foundational course for the AWS Certified AI Practitioner Specialization uses hands-on practice and real-world applications to make learning more relevant. You'll learn about Supervised and Unsupervised learning, ML algorithms, Neural networks, Deep Learning, Natural Language Processing, computer vision, and important data approaches like Feature engineering and Data wrangling through engaging videos and guided demos. Unlike passive theory-based courses, this AWS AI Practitioner training course enables you to apply principles using Amazon SageMaker for training, deployment, and optimization, making your learning more practical and meaningful. This course is appropriate for both beginners and professionals who want to strengthen or refresh their knowledge of AI and machine learning. In the end, you'll be able to successfully use AWS, apply ML approaches, explore advanced topics, and acquire hands-on experience developing and deploying models with Amazon SageMaker.
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5 hours 57 minutes
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Master Responsible AI with AWS: A Security and Governance Learning Experience. In today's AI-driven world, creating intelligent systems is not enough; they must also be secure, ethical, and accountable. This course teaches you how to master responsible AI techniques with AWS technologies geared for governance, fairness, and compliance. Through real-life situations and hands-on activities, you'll learn about the fundamental concepts of responsible AI, the legal risks associated with generative AI, and how to apply model guardrails to ensure explainability and fairness. But this course doesn't just teach responsible AI in theory—it shows you how to secure it in practice. You'll work with key AWS services like Amazon Guardrails, Macie, and A2I, and implement IAM policies, data encryption, and compliance frameworks to protect your AI models at every stage. Designed for learners preparing for the AWS Certified AI Practitioner exam, this course is ideal for those with prior knowledge of AI/ML and AWS Cloud Fundamentals. By the end, you'll be equipped to design AI solutions that are not only smart but also safe, ethical, and trusted.