Overview
Discover RamaLama, a tool that simplifies AI model deployment through containerization, offering privacy-focused, GPU-optimized workflows with support for multiple runtimes and seamless integration with Podman and Kubernetes.
Syllabus
-
- Introduction to RamaLama
-- Overview of AI model deployment challenges
-- Introduction to RamaLama and its purpose
- Containerization Basics
-- Understanding containers
-- Containerization vs virtualization
-- Introduction to Podman and Kubernetes
- Utilizing RamaLama for AI Deployment
-- Setting up RamaLama
-- Overview of RamaLama features
- Privacy-Focused Workflows
-- Handling sensitive data
-- Privacy measures in RamaLama
- GPU-Optimized Workflows
-- Importance of GPU optimization for AI models
-- Configuring RamaLama for GPU usage
- Support for Multiple Runtimes
-- Overview of runtime environments
-- Configuring different runtimes in RamaLama
- Integration with Podman and Kubernetes
-- Setting up Podman with RamaLama
-- Deploying AI models with Kubernetes and RamaLama
- Practical Demonstrations
-- Real-world deployment examples
-- Best practices in AI model deployment
- Troubleshooting and Optimization
-- Common deployment issues
-- Tips for optimizing performance
- Course Wrap-Up
-- Recap of key learnings
-- Additional resources and next steps
Taught by
Tags
On-Demand
