ATC-GENAI - GenAI - Build Intelligent Applications Using AWS Services


Duration: 4.0 days

This course will help learners to get started with Amazon Web Services (AWS) Generative Artificial Intelligence (GenAI) services. Learners will learn how to leverage pre-built applications like computer vision, natural language processing, text-to-speech and more to build intelligent applications.


  • Understand the fundamentals of AWS services and cloud computing.
  • Explore the concepts of Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), and Generative AI.
  • Learn how to formulate Machine Learning problems.
  • Utilize data processing tools like Pandas, NumPy, and Matplotlib/Seaborn for data analysis and visualization
  • Explore SageMaker, AWS Bedrock
  • Understand the basics of Deep Learning and Artificial Neural Networks
  • Introduce Generative AI, Generative Adversarial Networks (GANs), and their applications
  • Discover Foundation Models (FMs) and their significance
  • Explore Amazon Lex for building conversational bots
  • Dive into Natural Language Processing (NLP) using AWS services like Comprehend, Lex, and Polly
  • Get hands-on experience with Amazon Bedrock and predefined FM models.
  • Understand how to scale and deploy production-ready GenAI applications on AWS
  • Explore AWS security services
  • Discuss the benefits and potential risks of using Generative AI



1. Introduction

  • Introduction to course/aws
  • Generative AI Fundamentals
  • Case Study: Generative AI
  • Understanding AI/ ML / DL / GenAI

2. Building Application with AI

  • Introduction to AI
  • Strong AI vs Weak AI
  • Benefits of using AI
  • Limitations & General Use cases
  • AWS Rekognition for Image recognition
  • Case Study

LAB- Using lambda to interact with Amazon Recognition/Textract service using boto3

3. Introduction to ML

  • Introduction to Machine Learning
  • ML in Action
  • What is model?
  • Categories of Machine Learning
    • Supervised Learning
    • Unsupervised Learning
    • Reinforcement Learning
  • ML Pipeline on AWS
  • AWS Machine Learning Stack
  • AW ML Use cases
  • Case Study

4. Amazon SageMaker

  • Introduction to Amazon SageMaker
  • What Amazon SageMaker can do?
  • Case Study
  • Amazon SageMaker GroundTruth
  • Amazon SageMaker Notebooks
  • SageMaker Algorithms

Demo: Labeling data using GroundTruth

Lab - Launching SageMaker Notebook instance


1. Problem Formulation

  • ML Problem Formulation
  • Understanding Data

2. Data Processing

  • Data Collection
  • Integrating Data
  • Using DataLake Architecture
  • Data Processing using Amazon EMR, Glue, DataBrew, Python libraries - Pandas, Numpy, MatplotLib/Seaborn etc
  • Cleaning Dirty Data
  • Understanding outliers
  • Amazon Macie

Lab - cleaning data using Sagemaker notebook with pandas and matplotlib


1. Model Training

  • Choosing right ML Model
  • Splitting data and cross-validation
  • Model Training
  • Model Evaluation
  • Feature Engineering
  • Model Deployment

Lab - using SageMaker to train Model & deploy

2. Introduction to Deep Learning

  • Introduction to Deep Learning
  • Using Artificial Neural Networks (ANN)
  • Deep Neural Networks (DNNs)
  • Deep Learning vs Machine Learning
  • Application
  • Deep Learning on AWS

3. Generative AI

  • Introduction to GenAI,
  • Generative AI vs Deep Learning
  • Types of Generative AI
  • Generative Adversarial Network (GAN)
  • Recurrent Neural Network (RNNs)
  • Long Short Term Memory (LSTM)
  • Transformer
  • Generative AI on AWS
  • Need of Generative AI

Demo/Lab - Creating bot with Amazon Lex


1. Amazon Sagemaker Jumpstart, Amazon Bedrock, Amazon Titan, AWS CodeWhisperer

  • Foundation Model
  • Build your own Foundation Model
  • Amazon Titan Foundation Model
  • Large Language Model (LLMs)
  • RAG/LangChain
  • Fine Tuning
  • Stable difusion
  • Amazon SageMaker Jumpstart
  • Amazon Bedrock
  • Amazon CodeWhisperer
  • Case Study

Demo/Lab - Using Amazon Bedrock

Demo: Fine tuning Bedrock Model

Lab - Building GenAI using predefined FM models

Lab- Using Bedrock agents

2. Scaling & deploying production scale GenAI on AWS

  • Scaling & deploying production scale GenAI on AWS

Demo : building app using SageMaker jumpstart fm models

3. Securing GenAI Applications

  • Threats to AI Applications
  • Single sign-on with SAML, OpenID Connect
  • Restricting Access to Sensitive Data
  • Monitoring with GuardDuty
  • Monitoring with Macie
  • Other Security Best Practices
  • Benefits, Risk of using GenAI




Anyone who has an understanding on ML models.



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