Cloud-Native AI: Wasm in Portable, Secure AI/ML Workloads

סקירה כללית

Discover how WebAssembly enables portable and secure AI/ML workloads in cloud environments, focusing on deploying models like Llama3 and Mixtral using Rust for optimal performance across platforms.

סילבוס

• Introduction to Cloud-Native AI
Overview of Cloud-Native Architecture
Benefits of Portability and Security in AI/ML Workloads
• Understanding WebAssembly (Wasm)
Introduction to WebAssembly and its Use Cases
How Wasm Enables Portable and Secure Workloads
• Fundamentals of AI/ML Workloads in the Cloud
Key Concepts in AI/ML
Cloud Infrastructure and Services for AI
• Introduction to Llama3 and Mixtral Models
Features and Use Cases of Llama3
Features and Use Cases of Mixtral
• Deploying AI/ML Models Using Wasm
Setting Up the Development Environment
Compiling AI/ML Models to Wasm
• Programming with Rust for AI/ML
Introduction to Rust Programming Language
Rust Ecosystem for AI/ML
Writing and Optimizing Rust Code for AI/ML Models
• Performance Optimization Across Platforms
Techniques for Optimizing Wasm Performance
Cross-Platform Performance Considerations
• Ensuring Security in Wasm-Based AI/ML Workloads
Security Best Practices in Wasm
Implementing Security Measures in AI Models
• Case Studies and Practical Deployments
Deploying Llama3 and Mixtral in Cloud Environments
Real-World Case Studies of Wasm in AI/ML Workloads
• Future Trends in Cloud-Native AI/ML and Wasm
Emerging Technologies and Innovations
Predictions for the Future of AI/ML in the Cloud
• Project and Assessment
Hands-on Project: Develop and Deploy a Wasm-based AI/ML Model
Assessment and Feedback on Project
• Course Review and Wrap-Up
Review of Key Concepts
Q&A and Final Thoughts

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נושאים

Programming