Building a Scalable End-to-End Image Classification Pipeline on AWS

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One of the hardest parts of Machine Learning is not training the model — it’s building the pipeline around the model: data prep, training orchestration, deployment, and monitoring.

I recently worked on a project to design an end-to-end ML pipeline on AWS that’s:

• Automated (training kicks off as soon as data is ready)
• Scalable (can handle large datasets with custom containers)
• Flexible (training and deployment are decoupled)
• Multi-use-case friendly (adding a new model is just a one-line change)

I’ve open-sourced the code here 👉 GitHub: just4give/aws-ml-pipeline so others can build similar pipelines with ease.

Why This Pipeline?

Traditional ML demos often stop at “train in SageMaker, deploy endpoint”. That works for experiments but falls short in production:

• You don’t want to keep endpoints running (costly).
• You want to version models, roll back easily, and track experiments.
• You want to support multiple models/use-cases (doc classification, product categorization, etc.) without rewriting the pipeline.