Curriculum Overview: Developing GenAI Applications on AWS

Welcome to the curriculum overview for Developing Generative AI Applications on AWS. This learning path is designed for prospective AI Practitioners looking to understand and select the right AWS infrastructure and services—such as Amazon SageMaker JumpStart, Amazon Bedrock, PartyRock, and Amazon Q—to build scalable, cost-effective, and secure generative AI solutions.

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Prerequisites

Before diving into this curriculum, learners should ensure they have a baseline understanding of the following concepts:

• Cloud Computing Fundamentals: Familiarity with basic AWS infrastructure (e.g., IAM roles, Amazon S3, Amazon EC2) and the AWS Shared Responsibility Model.
• Machine Learning Basics: Understanding the difference between supervised learning, unsupervised learning, and reinforcement learning.
• Foundational GenAI Concepts: A high-level grasp of large language models (LLMs), foundation models (FMs), tokens, embeddings, and prompt engineering.
• Basic Python/API Knowledge: While services like PartyRock are no-code, interacting with Amazon Bedrock and SageMaker JumpStart requires basic familiarity with REST APIs and scripting.

[!IMPORTANT] If you are entirely new to AI, we recommend completing a primer on Artificial Intelligence (AI) versus Machine Learning (ML) versus Deep Learning before starting this module.

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Module Breakdown

The curriculum is structured progressively, moving from high-level, no-code AI assistants to granular, highly customizable machine learning environments.

AWS AI Services Abstraction Stack

To understand the module progression, review the AWS AI stack abstraction below. We move from the top tier (ready-to-use) down to the middle tier (managed ML).

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Learning Objectives per Module

Module 1: No-Code Experimentation

• Build interactive generative AI applications without writing any code.
• Experiment with various prompt engineering techniques using a visual web interface.
• Share custom applications (e.g., chatbots, story generators) securely with internal teams.

Module 2: AI Assistants & Productivity

• Deploy Amazon Q Developer to assist with code generation, debugging, and AWS infrastructure queries.
• Integrate Amazon Q Business with enterprise data repositories (e.g., Amazon S3, internal wikis) to securely answer employee questions.
• Evaluate the ROI of using AI assistants (e.g., measuring developer hours saved during workload transformations).

Module 3: Managed GenAI via APIs

• Compare multiple foundation models (Anthropic Claude, Amazon Titan, Meta Llama) accessible via a single API in Amazon Bedrock.
• Implement Retrieval-Augmented Generation (RAG) using Bedrock Knowledge Bases to reduce hallucinations.
• Identify advantages of fully managed GenAI APIs, such as an artificially lowered barrier to entry, compliance guarantees, and speed to market.

Module 4: Customizing Foundation Models

• Deploy pre-trained, open-source computer vision and NLP models securely using Amazon SageMaker JumpStart.
• Fine-Tune base models utilizing enterprise datasets while maintaining data privacy.
• Monitor deployed models for data drift, bias, and performance using SageMaker Model Monitor and SageMaker Clarify.

Module 5: Economics & Cost Tradeoffs

• Calculate Token-based pricing mechanisms ($C_{total} = Tokens_{in} \times Rate_{in} + Tokens_{out} \times Rate_{out}$).
• Assess the trade-offs between utilizing Provisioned Throughput versus On-Demand pricing based on availability and responsiveness requirements.

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Success Metrics

How will you know you have mastered the curriculum? We measure success through three core evaluation pillars:

1. Architectural Decision Making: You can correctly identify the optimal AWS service for a given business scenario.
2. Cost Optimization: You can architect a solution that meets latency requirements without over-provisioning.
3. Application Deployment: You have successfully built and queried an LLM-backed application.

Service Selection Flowchart

Mastering the logic in this flowchart is a primary success metric.

[!TIP] Exam Preparation: If you are studying for the AWS Certified AI Practitioner (AIF-C01) exam, aim for a >80% score on practice scenarios requiring you to distinguish between Bedrock and SageMaker JumpStart.

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Real-World Application

Generative AI is rapidly moving from proof-of-concept to production. Understanding these AWS services ensures you can translate technical capabilities into tangible business value.

Industry Use Cases

• Software Development: Utilizing Amazon Q Developer to upgrade legacy systems. For example, AWS used Q to migrate tens of thousands of applications from Java 8 to Java 17, a task that would traditionally take 4,500 developer years, saving an estimated $260 million annually.
• Rapid Prototyping: A marketing team uses Amazon PartyRock to rapidly build a "campaign slogan generator" to brainstorm ideas without waiting on the IT department to provision servers.
• Secure Customer Service Bots: A healthcare provider uses Amazon Bedrock alongside an enterprise Vector Database (like Amazon OpenSearch) to build a compliant, RAG-powered patient assistant that answers questions based strictly on internal medical guidelines, mitigating the risk of "hallucinations" and ensuring HIPAA compliance.

Business Metrics Driven by GenAI

When applying these skills in the real world, you will learn to map technical implementations to core business metrics:

• Average Revenue Per User (ARPU): Increased through hyper-personalized GenAI recommendations.
• Conversion Rate: Boosted via intelligent, conversational search tools.
• Operational Efficiency: Measured in human hours saved from automation (e.g., Bedrock Data Automation summarizing complex PDFs).