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Developing Generative AI Applications on AWS

Last Updated: 08-03-2025

The Developing Generative AI Applications on AWS course is designed for AI and machine learning practitioners who want to learn how to build, deploy, and scale generative AI applications using AWS’s cutting-edge tools and services. In this hands-on course, you'll explore how to leverage Amazon SageMaker, AWS Lambda, and AWS Deep Learning AMIs to develop models for content generation, natural language processing, image synthesis, and more. You’ll gain practical experience with fine-tuning pre-trained models, integrating APIs, and optimizing your generative AI applications for real-world use cases like chatbots, art generation, and automated content creation.

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Schedule Learners Course Fee (Incl. of all Taxes) Register Your Interest
December 20th - 21st
09:00 AM - 05:00 PM (CST)
Live Virtual Classroom (Duration : 16 Hours)
10% Off
$640
$576
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December 22nd - 23rd
09:00 AM - 05:00 PM (CST)
Live Virtual Classroom (Duration : 16 Hours)
Guaranteed-to-Run
10% Off
$640
$576
December 27th - 28th
09:00 AM - 05:00 PM (CST)
Live Virtual Classroom (Duration : 16 Hours)
10% Off
$640
$576
January 03rd - 04th
09:00 AM - 05:00 PM (CST)
Live Virtual Classroom (Duration : 16 Hours)
20% Off
$640
$512
January 05th - 06th
09:00 AM - 05:00 PM (CST)
Live Virtual Classroom (Duration : 16 Hours)
20% Off
$640
$512
January 10th - 11th
09:00 AM - 05:00 PM (CST)
Live Virtual Classroom (Duration : 16 Hours)
20% Off
$640
$512
January 12th - 13th
09:00 AM - 05:00 PM (CST)
Live Virtual Classroom (Duration : 16 Hours)
20% Off
$640
$512
January 17th - 18th
09:00 AM - 05:00 PM (CST)
Live Virtual Classroom (Duration : 16 Hours)
20% Off
$640
$512
January 19th - 22nd
06:00 AM - 10:00 PM (CST)
Live Virtual Classroom (Duration : 16 Hours)
20% Off
$640
$512
January 26th - 27th
09:00 AM - 05:00 PM (CST)
Live Virtual Classroom (Duration : 16 Hours)
Guaranteed-to-Run
20% Off
$640
$512

Course Prerequisites

  • Familiarity with machine learning concepts and frameworks (e.g., TensorFlow, PyTorch).
  • Experience with Python and data science libraries such as NumPy, Pandas, and scikit-learn.
  • Basic understanding of AWS services like EC2, S3, IAM, and SageMaker.
  • Recommended: Knowledge of deep learning architectures, such as GANs (Generative Adversarial Networks) or transformers (e.g., GPT, BERT).

Learning Objectives

By the end of this course, you will be able to:

  1. Understand the fundamentals of generative AI and its real-world applications across various domains like NLP, computer vision, and content generation.
  2. Leverage Amazon SageMaker to train, fine-tune, and deploy generative AI models for text, image, and audio generation.
  3. Use pre-trained models and APIs (e.g., GPT-3, DALL·E) to accelerate the development of generative AI applications.
  4. Implement and scale deep learning architectures like GANs and transformers for custom generative models.
  5. Build and integrate generative AI applications into your workflows with AWS Lambda, Amazon API Gateway, and AWS Step Functions.
  6. Optimize the performance and cost of generative AI applications using AWS tools like Amazon Elastic Inference, SageMaker Model Monitor, and AutoML.
  7. Understand best practices for managing generative AI models in production, including versioning, monitoring, and scaling.
  8. Gain hands-on experience with real-world generative AI use cases, such as conversational AI, content creation, and data augmentation.

Target Audience

This course is ideal for:

  • Data scientists, AI engineers, and machine learning practitioners eager to explore generative AI techniques and applications.
  • Developers looking to build intelligent generative AI systems using AWS cloud technologies.
  • Professionals working on AI-driven projects, such as chatbots, text generation, and image creation, who want to scale their applications using AWS.
  • Individuals interested in exploring the latest advancements in AI and leveraging AWS for real-time deployment and optimization.

Course Modules

Day 1:

  1. Module 1: Introduction to Generative AI – Art of the Possible

    • Overview of Machine Learning (ML)
    • Basics of Generative AI
    • Generative AI Use Cases
    • Practical Applications of Generative AI
    • Understanding Risks and Benefits

  2. Module 2: Planning a Generative AI Project

    • Fundamentals of Generative AI
    • Contextualizing Generative AI
    • Steps in Planning a Generative AI Project
    • Identifying Risks and Mitigation Strategies

  3. Module 3: Getting Started with Amazon Bedrock

    • Introduction to Amazon Bedrock
    • Architecture and Use Cases
    • Using Amazon Bedrock
    • Demonstration: Setting Up Bedrock Access and Utilizing Playgrounds

  4. Module 4: Foundations of Prompt Engineering

    • Basics of Foundation Models
    • Fundamentals of Prompt Engineering
    • Basic and Advanced Prompt Techniques
    • Model-Specific Prompt Techniques
    • Demonstration: Fine-Tuning Basic Text Prompts
    • Addressing Prompt Misuses
    • Mitigating Bias in Prompts
    • Demonstration: Image Bias Mitigation

Day 2:

  1. Module 5: Amazon Bedrock Application Components

    • Overview of Generative AI Application Components
    • Foundation Models and the FM Interface
    • Working with Datasets and Embeddings
    • Demonstration: Word Embeddings
    • Additional Application Components
    • Retrieval Augmented Generation (RAG)
    • Model Fine-Tuning
    • Securing Generative AI Applications
    • Generative AI Application Architecture

  2. Module 6: Amazon Bedrock Foundation Models

    • Introduction to Amazon Bedrock Foundation Models
    • Using Amazon Bedrock FMs for Inference
    • Amazon Bedrock Methods
    • Data Protection and Auditability
    • Demonstration: Invoking Bedrock Models for Text Generation with Zero-Shot Prompts

  3. Module 7: LangChain

    • Introduction to Architecture Patterns
    • Text Summarization Techniques
    • Demonstration: Text Summarization with Anthropic Claude
    • Abstractive Text Summarization with Amazon Titan using LangChain
    • Question Answering Systems
    • Demonstration: Using Amazon Bedrock for Question Answering
    • Chatbot Development
    • Demonstration: Conversational Interface with AI21 LLM
    • Code Generation Techniques
    • Demonstration: Using Amazon Bedrock Models for Code Generation

  4. Module 8: Architecture Patterns

    • Designing Architecture Patterns for Generative AI Applications
    • Implementing Various Generative AI Use Cases

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What Our Learners Are Saying

The training, courseware, and lab experience were insightful and valuable. Keep up the great work and learning experience!

Nitish A. Anand – Accenture

Course: SC-200: Microsoft Security Operations Analyst
Date: 15th Jan 2025

The instructor was professional and very content.

Justine Daudi Mlimbilah – Bank of Africa, Tanzania

Course: MD-102: Microsoft 365 Endpoint Administrator
Date: 20th Dec 2024

The instructor was so knowledgeable & humble. Rare to find someone so confident but so down to earth these days. So appreciative to him.”

Mohd. Hassan – Ministry of Finance, UAE

Course: AZ-700: Designing and Implementing Microsoft Azure Networking Solutions
Date: 31st July 2024

Instructor is experienced and knowledgeable in guiding.

Dharshini Mahalaxmi – Dr. MGR Education and Research Institute, Chennai, India

Course: SC-300: Microsoft Identity and Access Administrator
Date: 4th May 2024