Genai Application Architecture: Scalable & Secure Ai Design

Build scalable, secure, and efficient GenAI applications with AWS, MLOps, monitoring, and cloud-native architecture

What you'll learn

Design Scalable GenAI Applications: Learn to architect and build scalable GenAI applications using the LGPL architecture, focusing on Layer, Gate, Pipes

Implement Resiliency and Error Handling: Understand how to incorporate error handling, monitoring, logging, and disaster recovery to create resilient GenAI Apps

Ensure Security and Cost Efficiency: Develop secure and cost-effective GenAI solutions by leveraging AWS security services, containerization

Automate and Optimize with MLOps & CI/CD: Learn to implement MLOps, CI/CD, and Explainable AI (XAI) for streamlined deployment and future-proofing GenAI apps

Requirements

Basic Knowledge of AI and Machine Learning: Understanding of fundamental AI and machine learning concepts.

Familiarity with AWS: Experience with AWS services such as Lambda, S3, and DynamoDB is recommended.

Programming Skills: Intermediate-level knowledge of Python is essential.

Basic Understanding of Software Architecture: Familiarity with software architecture principles such as scalability, load balancing, and error handling.

Description

Master the essential techniques and best practices for designing and architecting scalable, secure, and cost-effective Generative AI (GenAI) applications. In this course, you’ll explore the principles of the LGPL architecture (Layers, Gates, Pipes, and Loops) and how they apply to building GenAI systems using modern cloud services like AWS. We’ll cover critical topics such as load balancing, containerization, error handling, monitoring, logging, and disaster recovery. This course is ideal for those looking to understand GenAI architecture, ensuring applications are resilient, secure, and efficient.What You'll Learn:Architect scalable and secure GenAI applications using the LGPL model.Understand core concepts such as containerization, load balancing, and disaster recovery.Learn best practices for monitoring, logging, and error handling in GenAI systems.Explore MLOps, CI/CD, and security strategies for future-proofing AI applications.This course focuses on the architecture and principles behind building robust GenAI systems, providing the knowledge needed to design effective AI solutions.Enroll now to transform your GenAI Application Architecture skills to the next level.  Master GenAI Application Architecture - the core best practices and techniques for building secure, efficient, scalable GenAI Applications.Ready to take your skills to the next level?  Join me, and let's get started.  See you inside the course!

Overview

Section 1: Introduction

Lecture 1 Introduction & Course Prerequisites

Lecture 2 IMPORTANT note and Course Structure

Section 2: Course Code and Resources

Lecture 3 Get Course Code

Section 3: GenAI (Generative AI) Deep Dive

Lecture 4 GenAI Deep Dive - What is It and Example of GenAI - Real-life Applications

Lecture 5 The Evolution of AI Architecture - Traditional to Generative AI - Overview

Lecture 6 GenAI Key Concepts: Variational Autoencoders and Generative Adversarial Networks

Lecture 7 Variational Autoenconders (VAEs) and Generative Adversarial Networks (LLM)

Lecture 8 Benefits and Challenges of Building GenAI Applications

Section 4: The LGPL Architecture - Deep Dive

Lecture 9 Check in

Lecture 10 The LGPL Architecture Deep Dive - Why GenAI Application Architecture is a Must

Lecture 11 The LGPL Architecture and Layers - Overview

Lecture 12 Gates

Lecture 13 Pipes

Lecture 14 Loops and Bringing it All Together - The Whole LPGL Architecture Overview

Lecture 15 Hands-on - Simple Gates Simulation - Python console Program

Lecture 16 Hands-on - Simulate Feedback Loop

Lecture 17 Hands-on - Full Simulation Including all Architecture Layers

Section 5: Building Scalable GenAI Applications

Lecture 18 Building Scalable GenAI Applications - Introduction & Infrastructure Selection

Lecture 19 Containerization & Docker Crash Course

Lecture 20 Microservices vs Monolith Architecture

Lecture 21 Load Balancing and Fault Tolerance working Together - Full overview

Lecture 22 Load Balancing and Fault Tolerance - Full Overview

Section 6: Building for Cloud-Native Deployments

Lecture 23 Leveraging the Cloud for Scalable GenAI Applictions - Introduction

Lecture 24 The Cloud Advantage for GenAI Applications

Section 7: Building Resilient GenAI Applications

Lecture 25 Building Resilient GenAI Applications - Error Handling & Exception Management

Lecture 26 Error Logging - Cascading Failure Prevention & Retry Logic for Corrective Action

Lecture 27 Monitoring and Logging and Alerting

Lecture 28 Diagrams for Monitoring - Log Processing and Alert Systems

Section 8: Disaster Recovery and High Availability Strategies

Lecture 29 Disaster Recovery and High Availability Strategies - Introduction to DR

Lecture 30 High Availability and Disaster Recovery in Action - AWS Support for DR and HA

Lecture 31 Case Study 1 - Realtime Trading System - Full GenAI Application Architecture

Lecture 32 Case Study 2 - Your Turn - GenAI System for Diagnosis Recommendation System

Section 9: Security Threats in GenAI Applications

Lecture 33 Security Threats in GenAI Applications - Model Hijacking and Privacy Leakage

Lecture 34 Deepfakes - Evasion Attacks - Insider Threats - Adversarial Attack

Lecture 35 Adversarial Attacks Solution - XAI (Explainable AI)

Section 10: Cost Optimization Strategies for GenAI Infrastructure

Lecture 36 GenAI Application Cost Optimization - Right-sizing & Spot Instances & Containers

Lecture 37 Some Techniques to Reduce Cost

Lecture 38 Choosing the Right Cloud Platform and Pricing Optimization Summary

Section 11: Advanced Topics in GenAI Application Architecture

Lecture 39 Advanced Topics in GenAI App Architecture - XAI Full Overview

Lecture 40 MLOps and CICD Full Overview and Importance

Lecture 41 Responsible AI and Ethical Considerations for GenAI Applications

Lecture 42 Emerging Trends - Full Overview

Section 12: Next Steps

Lecture 43 Next Steps

AI Developers and Engineers: Those looking to build scalable, secure, and cost-effective GenAI applications.,Cloud Architects: Professionals working with AWS who want to implement GenAI architectures using best practices.,Machine Learning Enthusiasts: Individuals with a foundational understanding of machine learning and programming who want to expand into GenAI development.,Software Engineers: Engineers seeking to integrate AI into cloud-native applications and implement MLOps pipelines