Deploying GenAI: Overcoming Challenges in Performance, Security, and Efficiency

Overview

Discover systematic approaches for deploying generative AI models on resource-constrained devices, balancing performance with hardware limitations through compression techniques and optimization strategies for real-world applications.

Syllabus

• Introduction to Generative AI Deployment
Overview of Generative AI Applications
Challenges in Deploying GenAI Models on Edge Devices
Course Goals and Learning Outcomes
• Fundamentals of Model Compression
Overview of Model Compression Techniques
Pruning and Quantization
Knowledge Distillation
Impact of Compression on Model Performance
• Optimization Strategies for GenAI
Hardware-aware Model Optimization
Efficient Model Architecture Design
Exploring Trade-offs Between Accuracy and Efficiency
• Deployment on Resource-Constrained Devices
Understanding Device Limitations
Techniques for Edge Deployment
Use Cases and Industry Applications
• Ensuring Performance in Real-world Scenarios
Monitoring and Measuring Model Performance
Tools and Techniques for Performance Tuning
Case Studies of Successful Deployments
• Addressing Security in GenAI Models
Common Security Risks in GenAI Applications
Strategies for Mitigating Security Threats
Ensuring Privacy and Data Protection
• Future Trends in GenAI Deployment
Emerging Technologies and Approaches
Anticipating and Adapting to Future Challenges
• Conclusion and Wrap-up
Recap of Key Concepts and Strategies
Resources for Continued Learning
Final Project and Evaluation
• Final Project
Design and Implement a Deployment Strategy for a Selected GenAI Model
Address Challenges of Performance, Security, and Efficiency
• Additional Resources
Recommended Readings and Tools
Online Communities and Forums for GenAI Practitioners

Subjects

Computer Science