Workflow · workflow
End-to-end ML pipelines with Kubeflow, Docker, uv, and Vertex AI for local and cloud LLM experimentation
Data scientists face the challenge of bridging local development with scalable cloud deployment — the 'works on my laptop' problem — while LLMs add costs and development time if pipelines are not managed correctly, and slow remote feedback loops make iteration painful.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Pipeline triggered
The pipeline is triggered with a single command.
Tools used
Kubeflow PipelinesDocker DesktopuvVertex AIBuildKittypermypyGitHub CI/CD
Outcome
The pipeline setup enables local execution with feedback within seconds, remote builds completing in under one minute due to caching, and experiment tracking to compare LLM runs across parameters.
Results
Time savedunder one minute
Grounding & classification
Source type: technical build writeup
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