Use Docker to Deploy Computer Vision Models

Deploying deep learning models can be complex due to various dependencies and hardware requirements, but Docker offers a streamlined solution by using containers to package and deploy applications consistently across different environments. Docker addresses compatibility and reproducibility challenges, allowing models to run in isolated environments, making it a preferred option for deploying models on platforms like AWS, Azure, and Google Cloud. Although Docker's containerization around CUDA has limitations, it simplifies the deployment process by leveraging existing base images and facilitates continuous integration/continuous deployment (CI/CD) workflows by allowing for consistent testing across different hardware configurations. Utilizing Docker in deep learning projects can reduce the complexity of setting up environments, enable GPU-accelerated container configurations, and focus development efforts on the application itself rather than on configuration details.