This article outlines the process of creating a Docker container for training image classification models and managing performance and deployment. While AI/ML engineers primarily focus on model training and data engineering, understanding the underlying infrastructure is essential for efficiency. The author describes a setup involving Kubernetes, where images are organized in a directory structure for easy modification. The key components discussed include building Docker containers, executing training runs, and preparing deployments, emphasizing the need for optimized performance through local storage as image libraries expand.
In this part of the series, I’ll outline how to create a Docker container for training image classification models efficiently using cloud resources like Kubernetes.
While focusing on model training, AI/ML engineers also need to grasp the underlying infrastructure to streamline the process and maintain cost efficiency.
Setting up a dedicated server may provide better performance for training, especially when dealing with large image libraries, compared to using cloud storage.
Understanding the process of building a Docker container, executing training runs, and deploying models is crucial for managing the AI/ML workflow effectively.
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