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

Overview

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

Syllabus

• Introduction to Cloud-Native AI
Overview of Cloud-Native Concepts
Importance of Portability and Security in AI/ML Workloads
• Introduction to WebAssembly (Wasm)
What is WebAssembly?
Wasm in Cloud Computing: Benefits and Use Cases
• AI/ML Workloads in Cloud Environments
Characteristics of AI/ML Workloads
Challenges of Cloud Deployment
• Wasm for AI/ML Workloads
How WebAssembly Supports AI/ML Models
Portable and Secure Deployment with Wasm
• Deployment of AI Models Using Wasm
Introduction to Llama3 and Mixtral Models
Steps to Deploy Llama3 on Wasm
Steps to Deploy Mixtral on Wasm
• Performance Optimization
Measuring AI/ML Performance in Cloud-Native Environments
Techniques for Optimizing Wasm-Based AI/ML Models
Performance Tuning for Llama3 and Mixtral
• Ensuring Security and Compliance
Wasm Security Features
Implementing Security Best Practices in Wasm Deployments
• Hands-On Lab
Setting Up a Cloud Environment for Wasm AI/ML
Deploying a Sample Model Using Wasm
Performance and Security Testing
• Case Studies and Industry Applications
Real-World Examples of Wasm in AI/ML Deployments
• Summary and Future Directions
Recap of Key Concepts
Emerging Trends in Cloud-Native AI and WebAssembly
• Final Project
Develop and Deploy a Wasm-Based AI/ML Model in a Cloud Environment

Subjects

Programming