Content-Based Filtering
AI Infrastructure
A lightweight form of virtualization where applications and their dependencies are packaged together in isolated environments.
Detailed Explanation
Containerization is a method of packaging applications with all their necessary dependencies into isolated, lightweight containers. This ensures consistent performance across different computing environments, simplifies deployment, and enhances scalability. Containers utilize operating system features like kernel sharing to maintain minimal overhead, enabling efficient resource use and rapid startup times, making them essential for modern AI infrastructure and development workflows.
Use Cases
- •Use case: Deploy AI models across multiple cloud platforms efficiently, ensuring consistent environments and rapid scaling with containerized applications.
Related Terms
Other terms in the AI Infrastructure category