End-to-End Data Pipelines for Machine Learning on the Cloud
I have built ML pipelines that ran on a single VM, pipelines that ran on a Kubernetes cluster I babysat personally, and pipelines that ran fully managed on cloud platforms. Honestly, the cloud-managed ones have aged the best. Not because they are flawless, but because the failure modes are predictable and the operational burden actually scales with the team, not the team plus three on-call engineers. This is my take on what an end-to-end pipeline should look like in 2026. I am keeping it opinionated on purpose. Pipelines that try to please everyone end up pleasing no one.
The Five Stages I Always Build
Every production ML pipeline I have shipped collapses into five stages.
The names change.
The shape does not.
- Ingestion
- Transformation and validation
- Feature engineering and labeling
- Training and evaluation
- Serving and monitoring
The mistake I see most often is treating these as separate projects.
They are...
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