Kubernetes for AI Workloads provides a comprehensive exploration of deploying, managing, and scaling AI applications using Kubernetes. Participants will engage in hands-on projects that emphasize real-world applications, ensuring they gain practical experience in orchestrating containerized AI workloads. The course is structured to facilitate interactive learning, culminating in a final project that allows participants to showcase their skills and publish their findings in Cademix Magazine.
Throughout the program, learners will delve into the intricacies of Kubernetes architecture, focusing on how to optimize it for AI-driven tasks. Topics will cover everything from setting up Kubernetes clusters to advanced deployment strategies tailored for machine learning models. By the end of the course, participants will be equipped to handle the complexities of AI workloads in a Kubernetes environment, making them valuable assets in the ever-evolving tech landscape.
Introduction to Kubernetes and its role in AI
Setting up a Kubernetes cluster for AI applications
Containerizing AI models with Docker
Managing dependencies and configurations in Kubernetes
Scaling AI workloads using Kubernetes features
Implementing CI/CD pipelines for AI model deployment
Monitoring and logging for AI applications in Kubernetes
Best practices for resource management in Kubernetes
Security considerations for AI workloads in Kubernetes
Final project: Deploying an AI application on Kubernetes and publishing results