Description
Containerization has become the de facto standard for deploying applications in modern enterprises, and AI models are no exception. Docker containers provide a consistent, reproducible environment for AI model deployment, solving many of the challenges associated with managing complex dependencies and ensuring consistent behavior across different environments.
The process of containerizing AI models presents unique challenges, from managing GPU access and optimizing image sizes to ensuring efficient resource utilization. Here is an approach to containerizing AI models using Docker, addressing both fundamental concepts and advanced optimization techniques necessary for production deployments.
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