Course curriculum

    1. 1 Course Introduction

    2. 2 Why We Are Here

    3. 3 We-re Going Deep

    4. 4 About the Instructor

    5. 5 What This Course Is

    6. 6 What This Course is Not

    7. 7 Core Concept Engineering and Architecture Are Very Different

    8. 8 Prerequisites

    9. 9 What is Generative AI

    10. 10 Architecture for Systems Using Generative AI

    11. 11 How IT Architecture and Generative AI are Related

    12. 12 What-s In It For You

    13. 13 How The Course is Structured

    1. Objectives

    2. Keep Thes Things in Mind, GPU, CPU

    3. Storage Infrastructure

    4. Scalability and Distributed Coomputing; Quantum Computing

    5. Energy Effeciency and Sustainability

    6. Considering Cloud Computing: CPU Performance, Meet the Internet

    7. Factors Influencing Compute Platform Selection

    8. Impact of Network Performance on CPU

    9. Real World Cloud Computing Scenarios

    10. Optimizing Cloud Resources

    11. Designing for Performance

    12. Strategies for Cloud Deployment

    13. Case Study 1

    14. Additional Reading

    1. Objectives

    2. What is Generative AI

    3. Key Concepts and Techniques

    4. Applications of Generative AI

    5. Challenges and Ethical Considerations

    6. Understanding Large Language Models (LLM) and Generative AI

    7. Application in Various Domains

    8. Diverse Impacts and Implications

    9. Innovative Advanements

    10. Intro to Core Components

    11. Data Input

    12. Neural Networks

    13. Encoder and Decoder

    14. Generative Adversarial networks

    15. Latent Space and Loss Function

    16. Optimizers and Transformers

    17. Training Algorithms and Regularization

    18. Evaluation Metrics

    19. Architecture Flexibility

    20. Considerations for Effective Models

    21. Emerging Advancements & Innovations

    22. Additional Reading

    1. Objectives

    2. Data Brings Value

    3. Generative AI Relies on Data

    4. Data Design Basics

    5. Database Models

    6. Importance of Data Driven Architecture

    7. GEN AI and It's Signifcance

    8. Key Components of Data Driven Architecture in GEN AI

    9. Implications and Future Directions

    10. Case Study 1

    11. Case Study 2

    12. Case Study 3

    13. Additional Reading

    1. Objectives

    2. What is Generative AI

    3. Key Concepts and Techniques in Generative AI

    4. Applications of Generative AI

    5. Challenges and Ethical Considerations

    6. Looking Ahead with Generative AI

    7. The Power of Large Language Models and Generative AI

    8. Understanding Large Language Models and Generative AI

    9. Applications in Various Domains

    10. Diverse Impact and Implications

    11. Responsible Deployment

    12. Case Study 1

    13. Case Study 2

    14. Additional Reading

    1. Objectives

    2. Understanding Generative AI Systems Requirements

    3. Selecting Appropriate Hardware

    4. Infrastructure Capability

    5. Software and Platform Optimization

    6. Techological Fusion

    7. Monitoring and Managing Infrastructure

    8. Ensuring Security and Compliance

    9. Testing and Validation

    10. Case Study 1

    11. Additional Reading

About this course

  • $999.00
  • 900+ Lessons
  • 57.5 hours of on-demand content
  • 12 Months of Live Classes

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What is the Generative AI Architect Development Program

The Generative AI Architect Program Includes All of These Components