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

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.

Duration: 4.0 days

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Objectives

  • 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

Content

Day-1

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

Day-2

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

Day-3

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

Day-4

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

Audience

N/A

Prerequisites

Anyone who has an understanding on ML models.

Certification

product-certification

Course Benefits

product-benefits
  • Career growth
  • Broad Career opportunities
  • Worldwide recognition from leaders
  • Up-to Date technical skills
  • Popular Certification Badges

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