Learnings from a Machine Learning Engineer — Part 5: The Training

Practical insights for model training in a Docker container

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In this fifth part of my series, I will outline the steps for creating a Docker container for training your image classification model, evaluating performance, and preparing for deployment.

AI/ML engineers would prefer to focus on model training and data engineering, but the reality is that we also need to understand the infrastructure and mechanics behind the scenes.

I hope to share some tips, not only to get your training run running, but how to streamline the process in a cost efficient manner on cloud resources such as Kubernetes.

I will reference elements from my previous articles for getting the best model performance, so be sure to check out Part 1 and Part 2 on the data sets, as well as Part 3 and Part 4 on model evaluation.

Here are the learnings that I will share with you:

• Infrastructure overview
• Building your Docker container
• Executing your training run
• Deploying your model

Infrastructure overview