AI Training by Industry Experts
Your Roadmap to Understanding Artificial Intelligence with Amazon Web Services
Artificial Intelligence is reshaping how we work, create, and solve problems, and AWS has built one of the most comprehensive ecosystems to make it accessible. Whether you're a complete beginner or a professional looking to upskill, this curriculum takes you from foundational AI concepts all the way through hands-on AWS AI services. Each topic builds on the last, giving you a clear, structured path forward.
No deep coding background required. Just curiosity and a willingness to learn.
What You'll Learn
1. Introduction to Artificial Intelligence & AWS AI Overview
⏱ Estimated Time: 1–2 hours
Get a bird's-eye view of what AI is, where it came from, and why it matters today. Explore AWS's AI ecosystem, from pre-built AI services to full ML platforms, and understand how the pieces fit together.
By the end of this module, you'll be able to:
Define artificial intelligence and explain its core concepts
Navigate the AWS AI/ML service landscape with confidence
Distinguish between AWS AI services (pre-built) vs. ML services (custom-built)
What You'll Learn
1. Introduction to Artificial Intelligence & AWS AI Overview
⏱ Estimated Time: 1–2 hours
Get a bird's-eye view of what AI is, where it came from, and why it matters today. Explore AWS's AI ecosystem, from pre-built AI services to full ML platforms, and understand how the pieces fit together.
By the end of this module, you'll be able to:
Define artificial intelligence and explain its core concepts
Navigate the AWS AI/ML service landscape with confidence
Distinguish between AWS AI services (pre-built) vs. ML services (custom-built)
What You'll Learn
1. Introduction to Artificial Intelligence & AWS AI Overview
⏱ Estimated Time: 1–2 hours
Get a bird's-eye view of what AI is, where it came from, and why it matters today. Explore AWS's AI ecosystem, from pre-built AI services to full ML platforms, and understand how the pieces fit together.
By the end of this module, you'll be able to:
Define artificial intelligence and explain its core concepts
Navigate the AWS AI/ML service landscape with confidence
Distinguish between AWS AI services (pre-built) vs. ML services (custom-built)
2. 🧠 How Machine Learning Works, with Amazon SageMaker AI
⏱ Estimated Time: 2–3 hours
Discover how computers learn from data without being explicitly programmed. Learn how Amazon SageMaker AI, AWS's flagship ML platform, makes it easy to build, train, and deploy ML models at scale.
By the end of this module, you'll be able to:
Explain the three main types of machine learning (supervised, unsupervised, reinforcement)
Describe the ML lifecycle from training to deployment
Identify key SageMaker features: Studio, Autopilot, and Ground Truth
03:18
3. 📊 Data: The Fuel Behind AI, with AWS Glue & Amazon S3
⏱ Estimated Time: 2–3 hours
Learn why data quality matters more than algorithm quality. Discover how AWS Glue handles data preparation and how Amazon S3 stores the massive datasets that power AI workloads.
By the end of this module, you'll be able to:
Explain why data quality directly impacts AI performance
Describe how AWS Glue automates ETL (Extract, Transform, Load) processes
Understand how S3 serves as the central data lake for AWS AI pipelines
03:18
4. 🔍 Neural Networks & Deep Learning, with AWS Deep Learning Tools
⏱ Estimated Time: 2–3 hours
Peek inside the "black box." Explore how neural networks work and how AWS supports deep learning through AWS Deep Learning AMIs, Deep Learning Containers, and SageMaker model tuning tools.
By the end of this module, you'll be able to:
Describe how a neural network is structured and how it learns
Explain the concept of layers, weights, and activation functions, in plain terms
Understand how AWS infrastructure accelerates deep learning training
03:18
5. 💬 Natural Language Processing, with Amazon Comprehend, Lex & Transcribe
⏱ Estimated Time: 2–3 hours
Explore how AI understands and generates human language using three powerful AWS services: Amazon Comprehend (text analysis), Amazon Lex (conversational chatbots), and Amazon Transcribe (speech-to-text).
By the end of this module, you'll be able to:
Use Amazon Comprehend to extract sentiment, entities, and key phrases from text
Build a basic conversational chatbot interface using Amazon Lex
Convert audio and voice data into text using Amazon Transcribe
03:18
6. 👁️ Computer Vision, with Amazon Rekognition
⏱ Estimated Time: 2–3 hours
See how AI interprets images and video using Amazon Rekognition, one of AWS's most widely used AI services. From facial recognition to content moderation and object detection, computer vision is everywhere.
By the end of this module, you'll be able to:
Explain how Rekognition analyzes images and video in real time
Apply Rekognition to use cases like security monitoring, medical imaging, and retail
Understand the ethical considerations of using facial recognition technology
03:18
7. 🎨 Generative AI, with Amazon Bedrock & Amazon Q
⏱ Estimated Time: 2–4 hours
Dive into AWS's generative AI stack. Amazon Bedrock gives you access to powerful foundation models (FMs) for text, image, and code generation. Amazon Q is AWS's AI-powered assistant for business queries, summaries, and decision support.
By the end of this module, you'll be able to:
Explain how Amazon Bedrock provides access to foundation models without managing infrastructure
Use Amazon Q to generate summaries, reports, and insights
Apply responsible prompting practices and understand output limitations
8. ⚖️ AI Ethics & Responsible Use on AWS
⏱ Estimated Time: 2–3 hours
Understand the human side of AI, bias in algorithms, privacy concerns, and accountability. Learn how AWS addresses responsible AI through governance tools, model transparency features in SageMaker, and content moderation via Rekognition.
By the end of this module, you'll be able to:
Identify ethical risks in AI systems, including bias, privacy, and misinformation
Evaluate AWS AI tools through a responsible-use lens
Describe AWS's approach to model monitoring and explainability in SageMaker
9. 🏢 AI in Business & Industry, with Amazon Personalize, Forecast & Kendra
⏱ Estimated Time: 2–3 hours
See how AWS AI services are transforming industries. Amazon Personalize powers recommendation engines, Amazon Forecast predicts demand and sales trends, and Amazon Kendra delivers intelligent enterprise search.
By the end of this module, you'll be able to:
Identify how AWS AI services are applied across healthcare, retail, finance, and more
Describe how Amazon Personalize builds recommendation systems from user behavior data
Use Amazon Forecast to predict time-series data like inventory levels or customer demand
03:18
10. 🛠️ Practical AWS AI Tools, Hands-On with Polly, Textract & Lambda
⏱ Estimated Time: 3–4 hours
Get hands-on with the AWS tools professionals actually use. Amazon Polly converts text to lifelike speech, Amazon Textract extracts structured data from documents, and AWS Lambda lets you run AI models serverlessly, no dedicated servers needed.
By the end of this module, you'll be able to:
Use Amazon Polly to generate realistic voiceovers and audio outputs
Extract and structure data from scanned documents using Amazon Textract
Deploy a simple AI-powered function using AWS Lambda without managing servers
11. 🔮 The Future of AI, AWS Agentic AI & Amazon Nova
⏱ Estimated Time: 1–2 hours
Where is AI headed? Explore AWS's latest innovations including Amazon Bedrock AgentCore, Strands Agents for agentic AI workflows, and Amazon Nova, AWS's next-generation multimodal model family.
By the end of this module, you'll be able to:
Explain what agentic AI is and how AWS is building autonomous agent frameworks
Describe Amazon Nova's multimodal capabilities (text, image, video)
Think critically about AI's long-term impact on work, society, and cloud infrastructure
12. 🚀 Building Your AWS AI Strategy
⏱ Estimated Time: 2–3 hours
Put it all together. Learn how to evaluate AWS AI opportunities for your organization, map services to business problems, and build a personal or team roadmap for AWS AI adoption, including cert prep for the AWS Certified AI Practitioner (AIF-C01).
By the end of this module, you'll be able to:
Match the right AWS AI service to the right business problem
Build a practical AWS AI implementation or learning roadmap
Identify next steps for AWS certification and ongoing skill development