Course curriculum

    1. Course Introduction

    2. Why We Are Here

    3. We're Going Deep

    4. About the Instructor

    5. What This Course Is

    6. What This Course is Not

    7. Core Concept Engineering and Architecture Are Very Different

    8. Prerequisites

    9. What is Generative AI

    10. Architecture for Systems Using Generative AI

    11. How IT Architecture and Generative AI are Related

    12. What's In It For You

    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. Section 1 Case Study

    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. Looking Ahead

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

    8. Diverse Impacts and Implications

    9. Responsible Deployment

    10. Positive Potential

    11. Innovative Advancements

    12. Interdisciplinary Collaboration

    13. Thoughtful Innovation

    14. Empowering Industries

    15. Conclusion

    16. Core Components of GEN AI Systems

    17. Data Input

    18. Neural Networks

    19. Encoder and Decoder

    20. Generative Adversarial Networks (GANs)

    21. Latent Space and Loss Function

    22. Optimizers and Transformers

    23. Training Algorithms and Regularization Techniques

    24. Evaluation Metrics

    25. Architecture Flexibility

    26. Considerations for Effective Models

    27. Emerging Advancements And Innovation

    28. Additional Reading

    1. Intro and 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. Understanding Data-Driven Architecture

    8. GEN AI and It's Signifcance

    9. Key Components of Data Driven Architecture in GEN AI

    10. Applications of Data-Driven Architecture in Generative AI

    11. Implications and Future Directions

    12. GEN AI Section 3 Case Study 1

    13. GEN AI Section 3 Case Study 2

    14. GEN AI Section 3 Case Study 3

    15. GEN AI Section 3 Case Study 4

    16. Additional Reading

    1. Intro and Objectives

    2. Assessment of Needs and Capabilities

    3. System Architectural Design

    4. Addressing Integration Challenges

    5. Development and Training of AI Models

    6. Testing and Evaluation Phase

    7. Preparation for Deployment

    8. Deploying the Generative AI System

    9. Monitoring and Ongoing Maintenance

    10. Post-Deployment Review

    11. Regulatory Compliance and Change Management

    12. GEN AI Section 4 Case Study 1

    13. GEN AI Section 4 Case Study 2

    14. Additional Reading

    1. Intro and Objectives

    2. Understanding GEN AI System Requirements

    3. Selecting Appropriate Hardware

    4. Infrastructure Scalability

    5. Software and Platform Optimization

    6. Technological Fusion

    7. Monitoring and Managing Infrastructure

    8. Ensuring Security and Compliance

    9. Testing and Validating

    10. GEN AI Section 5 Case Study

    11. Additional Reading

About this course

  • $2,749.00
  • 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