English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 43 lectures (2h 40m) | 2.51 GB
Build scalable, secure, and efficient GenAI applications with AWS, MLOps, monitoring, and cloud-native architecture
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.
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.
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.
- 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
Table of Contents
Introduction
1 Introduction & Course Prerequisites
2 IMPORTANT note and Course Structure
GenAI (Generative AI) Deep Dive
4 GenAI Deep Dive – What is It and Example of GenAI – Real-life Applications
5 The Evolution of AI Architecture – Traditional to Generative AI – Overview
6 GenAI Key Concepts Variational Autoencoders and Generative Adversarial Networks
7 Variational Autoenconders (VAEs) and Generative Adversarial Networks (LLM)
8 Benefits and Challenges of Building GenAI Applications
The LGPL Architecture – Deep Dive
9 Check in
10 The LGPL Architecture Deep Dive – Why GenAI Application Architecture is a Must
11 The LGPL Architecture and Layers – Overview
12 Gates
13 Pipes
14 Loops and Bringing it All Together – The Whole LPGL Architecture Overview
15 Hands-on – Simple Gates Simulation – Python console Program
16 Hands-on – Simulate Feedback Loop
17 Hands-on – Full Simulation Including all Architecture Layers
Building Scalable GenAI Applications
18 Building Scalable GenAI Applications – Introduction & Infrastructure Selection
19 Containerization & Docker Crash Course
20 Microservices vs Monolith Architecture
21 Load Balancing and Fault Tolerance working Together – Full overview
22 Load Balancing and Fault Tolerance – Full Overview
Building for Cloud-Native Deployments
23 Leveraging the Cloud for Scalable GenAI Applictions – Introduction
24 The Cloud Advantage for GenAI Applications
Building Resilient GenAI Applications
25 Building Resilient GenAI Applications – Error Handling & Exception Management
26 Error Logging – Cascading Failure Prevention & Retry Logic for Corrective Action
27 Monitoring and Logging and Alerting
28 Diagrams for Monitoring – Log Processing and Alert Systems
Disaster Recovery and High Availability Strategies
29 Disaster Recovery and High Availability Strategies – Introduction to DR
30 High Availability and Disaster Recovery in Action – AWS Support for DR and HA
31 Case Study 1 – Realtime Trading System – Full GenAI Application Architecture
32 Case Study 2 – Your Turn – GenAI System for Diagnosis Recommendation System
Security Threats in GenAI Applications
33 Security Threats in GenAI Applications – Model Hijacking and Privacy Leakage
34 Deepfakes – Evasion Attacks – Insider Threats – Adversarial Attack
35 Adversarial Attacks Solution – XAI (Explainable AI)
Cost Optimization Strategies for GenAI Infrastructure
36 GenAI Application Cost Optimization – Right-sizing & Spot Instances & Containers
37 Some Techniques to Reduce Cost
38 Choosing the Right Cloud Platform and Pricing Optimization Summary
Advanced Topics in GenAI Application Architecture
39 Advanced Topics in GenAI App Architecture – XAI Full Overview
40 MLOps and CICD Full Overview and Importance
41 Responsible AI and Ethical Considerations for GenAI Applications
42 Emerging Trends – Full Overview
Next Steps
43 Next Steps
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