PyTorch makes it easier to experiment with neural networks, computer vision, natural language processing, and deep learning.

But building a model is only one part of a machine learning project.

When it's time to move from experimentation to production, you need reliable compute, data storage, repeatable training, model artifacts, deployment, monitoring, and cost controls.

This is where AWS can help.

By combining PyTorch, Amazon SageMaker AI, Amazon S3, and Amazon CloudWatch , developers can build machine learning workflows that move from local experiments toward scalable cloud deployments.

In this article, we'll build a simple image classifier and explore how to extend the project into an AWS-based machine learning pipeline.

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