AWS Generative AI for Developers
Field | Description / Template |
|---|---|
Purpose | This course provides a comprehensive pathway from understanding generative AI fundamentals to building and deploying production-grade AI applications on AWS. It covers business strategy, prompt engineering, foundation models, multimodal AI, agent-based systems, and full-stack GenAI deployment using Bedrock, SageMaker, and Amazon Q. |
Audience | Developers, AI/ML engineers, cloud engineers, product managers, startup founders, and professionals exploring generative AI adoption. |
Role | AI Engineer, Machine Learning Engineer, GenAI Developer, Solutions Architect, Cloud Engineer, Technical Product Manager. |
Domain | Generative AI, Machine Learning, Cloud Computing, Applied AI Systems |
Skill Level | Intermediate |
Style | Hands-on, project-based learning with strategic frameworks, real-world labs, and a capstone project simulating enterprise-grade AI systems. |
Duration | 3 Day |
Related Technologies | Amazon Bedrock, Amazon Nova Models, Amazon SageMaker, Amazon Q Developer, AWS Lambda, Amazon S3, LangChain, Streamlit, Foundation Models (Llama, Mistral), Boto3 |
Course Description
This course bridges the gap between generative AI strategy and real-world implementation on AWS. It begins with foundational concepts, helping learners understand the shift from traditional machine learning to generative AI, along with identifying high-impact business use cases.
Learners will explore Amazon Bedrock for accessing and orchestrating foundation models, followed by deep dives into prompt engineering techniques such as zero-shot, few-shot, and chain-of-thought prompting. The course introduces different models available in bedrock(from OpenSource and Proprietary models), enabling multimodal reasoning across text, images, video, and audio.
As the course progresses, learners will build AI-powered applications using LangChain and Amazon Bedrock Agents, incorporating memory and state management. They will also leverage Amazon Q Developer to accelerate coding, debugging, and infrastructure troubleshooting.
The course expands into Amazon SageMaker for advanced use cases such as fine-tuning and deploying open-source models like Llama and Mistral. Through hands-on labs, learners will build real-world systems including AI agents, multimodal analysis tools, and developer productivity workflows.
Who is this course for
Developers building AI-powered applications
AI/ML engineers exploring generative AI on AWS
Cloud engineers and architects designing AI systems
Product managers and founders evaluating AI strategy
Anyone looking to build end-to-end GenAI applications
Course Objectives
By the end of this course, learners will be able to:
Understand Generative AI concepts, business applications, and foundation model selection based on cost and performance
Design effective prompts using advanced prompt engineering techniques
Work with multimodal AI models across text, image, video, and audio use cases
Build and orchestrate AI agents and workflows using Amazon Bedrock, external APIs, and LangChain
Deploy, manage, and scale production-ready GenAI applications using Amazon SageMaker and enterprise-grade AI assistant architectures
Prerequisites
Basic understanding of cloud computing and AWS services
Familiarity with Python programming
Basic understanding of APIs and JSON
No prior experience with generative AI required (beginner-friendly start)
Course outline
Section 1: Introduction to Generative AI
Shift from Predictive ML to Generative AI
Practical Business AI Strategy: Identifying "Low-Hanging Fruit" in Business.
Ethical Guardrails: Bias, Hallucinations, and Intellectual Property.
Lab 1: Generative AI Strategy Workshop. A non-technical lab where students use a "Value-Complexity" matrix to rank AI use cases for a sample business scenario.
Section 2: Amazon Bedrock Getting Started
Model Selection: Performance vs. Cost (The 3-Layer Stack).
Requesting Model Access and Setting Up IAM Permissions.
Lab 2: The Console Walkthrough: Setting up your first Bedrock environment, enabling model access, and using the text/image playgrounds.
Section 3: Foundations of Prompt Engineering
Prompt Anatomy: Instructions, Context, and Input Data.
Techniques: Zero-shot, Few-shot, and Chain-of-Thought (CoT).
Negative Prompting and Output Structuring.
Lab 3: Prompt Engineering Lab. Given a "broken" prompt, students must iterate on it using CoT and few-shot examples to achieve a 100% accurate structured JSON response.
Section 4: Exploring Amazon Bedrock Models
Comparing Claude 4.6 Opus (agentic coding) vs. OpenAI GPT-5.5 Pro (persistence reasoning).
The Open-Source Titan: Intro to Llama 4 Scout 3.5M-token context window.
Creative Asset Pipeline: Utilizing Amazon Nova 2 Reel for enterprise video generation
Multimodal Embeddings: Using Amazon Titan Multimodal Embeddings G2 Searching across Text, Video, and Audio.
Lab 4: Multimodal Analysis. Using Claude Opus 4.7 model to analyze a product video and a CSV price list simultaneously to generate a marketing summary.
Section 5: Building Generative AI Applications
Orchestration with LangChain vs. Bedrock Agents.
Memory and State Management in AI Apps.
Lab 5: Building an AI Agent. Create an Amazon Bedrock Agent that can query an external API to fetch real-time customer data and summarize it for a support ticket.
Section 6: Introduction to Amazon SageMaker
When to move from Bedrock to SageMaker (Fine-tuning vs. Training).
SageMaker JumpStart: Deploying Open-Source models (Llama 3.1, Mistral).
Compute Scaling: Managing Spot Instances and Notebook Life Cycles.
Lab 6: End-to-End SageMaker Pipeline. Deploying a Llama model via SageMaker JumpStart, creating an endpoint, and testing it via a Jupyter Notebook.
Section 9: Capstone - The Enterprise Assistant
A project that connects an Amazon Nova model to a Knowledge Base (via Bedrock), secured by Guardrails, and deployed with a simple Streamlit UI.