ATC-GENAILLM - Building & Deploying Generative AI Models


Duration: 3.0 days

If you're a newcomer to this technology, this course is designed to provide you with a profound understanding of building and deploying machine learning models, along with scaling up generative AI models. The curriculum encompasses a diverse range of models and illustrates how to seamlessly integrate them into real-world applications.


  • Foster a comprehensive understanding of machine learning model development and deployment, catering specifically to individuals new to the technology.
  • Equip participants with the skills to proficiently build machine learning models, ensuring a solid foundation in model creation.
  • Explore strategies for scaling up generative AI models, enabling participants to handle larger and more complex applications.
  • Introduce participants to a diverse range of machine learning and generative AI models, providing exposure to various methodologies and approaches.
  • Demonstrate the seamless integration of machine learning and generative AI models into real-world applications, emphasizing practical implementation.
  • Enable participants to apply their acquired knowledge by guiding them through practical exercises that involve building, deploying, and scaling models.
  • Facilitate hands-on experiences that allow participants to gain practical skills and confidence in working with machine learning and generative AI technologies.
  • Develop problem-solving aptitude by presenting real-world challenges and demonstrating how machine learning and generative AI models can be effective solutions.
  • Prepare participants for real-world applications in their professional journeys by providing insights and skills relevant to the current landscape of machine learning and generative AI.
  • Upon completion of this course, participants will be equipped with the knowledge and practical skills needed to navigate the intricacies of building, deploying, and scaling machine learning and generative AI models in real-world scenarios.


Module 1. Welcome & Course Introduction

  • Welcome
  • Overview: AI/ ML/ DL/ GenerativeAI
  • Overview: cloud - AWS/GCP/AZURE
  • Benefits of building GenerativeAI Models on Cloud

Module 2. Machine Learning

  • Machine Learning
  • Types of Machine Learning:
    • Supervised
    • Unsupervised
    • Reinforcement Training
  • Building Machine Learning Pipelines
  • AWS Machine Learning Stack
  • Amazon SageMaker

Lab: Labelling data using GroundTruth     

Lab - Launching SageMaker Notebook instance

Module 3. Machine Learning Pipeline in action - I

  • Understanding Business Problem
  • Data Collection & Preprocessing
  • Data cleansing using python libraries & AWS managed services
  • Data Exploration

Lab: using Jupyter Notebook for data cleaning & exploration using python libraries

Module 4: Machine Learning Pipeline in action - II

  • Model Training & Tuning with SageMaker
  • Feature Engineering
  • Model Evaluation
  • Model Deployment on Cloud : AWS/GCP/AZURE

Lab: Model Training & Deployment with Amazon SageMaker

Module 5: Engaging with Generative AI

  • What is GenerativeAI
  • GenAI vs ML vs DL
  • Types of Generative AI
  • Generative Adversarial Network (GAN)
  • Recurrent Neural Network (RNNs)
  • Long short-term Memory
  • Transformer
  • Natural Language Processing
  • Variational Encoder (VAE)
  • Amazon Bedrock Architecture
  • Amazon Bedrock Inference Parameter
  • Amazon Bedrock Pricing
  • Reinforcement Learning with Amazon Bedrock Agent
  • Amazon Bedrock : Case Study
  • Retrieval Augmented Generator

Lab - using Bedrock Agent

Module 6: GenAI Models

  • Understanding Foundation Model
  • Understanding Large Language Models
  • Google BERT in action
  • Building application with ChatGPT integration

Lab - Integrating ChatGPT & building intelligent Application

Module 7: Developing with GenAI Models

  • Developing application with Amazon Bedrock
  • Using AWS Titan Foundation Model
  • AWS Supported Foundation Models
  • SageMaker Jumpstart

Lab - Deploying foundational model using SageMaker Jumpstart

Module 8: Securing GenAI Models

  • 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




Introduction to Generative AI course



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