The Explorer's Guide to Cloud Native GenAI Platform Engineering

Overview

Discover a practical roadmap for building generative AI infrastructure on Kubernetes, from minimal viable setups to advanced features like LLM gateways, vector databases, load balancing, and performance optimization techniques.

Syllabus

• Introduction to Cloud Native GenAI
Overview of Cloud Native and Generative AI
The role of Kubernetes in AI Infrastructure
• Setting Up a Minimal Viable GenAI Platform
Installing and Configuring Kubernetes
Deployment of Basic AI Services
• Exploring Kubernetes for GenAI
Kubernetes Concepts: Pods, Nodes, and Services
Orchestration of AI Workloads
• Building and Integrating LLM Gateways
Understanding LLM (Large Language Models) Architecture
Deploying and Configuring LLM Gateways on Kubernetes
• Implementing Vector Databases
Introduction to Vector Databases
Setting Up and Using Vector Store for AI Applications
• Load Balancing in GenAI Platforms
Load Balancing Techniques and Tools
Best Practices for Traffic Management in AI Services
• Performance Optimization Techniques
Monitoring and Profiling AI Workloads
Techniques for Improving AI Application Performance
• Advanced Features and Enhancements
Utilizing Auto-scaling and Self-healing Capabilities
Security Best Practices for GenAI Platforms
• Case Studies and Real-World Applications
Examples of Successful Cloud Native GenAI Implementations
Lessons Learned and Best Practices
• Future Trends in Cloud Native GenAI
Emerging Technologies and Innovations
Preparing for the Future of AI Infrastructure
• Conclusion and Roadmap Beyond the Course
Recap of Key Learnings
Resources for Further Study and Community Involvement

Subjects

Computer Science