MLOps workflow: from input to evidence
Machine learning experiments become far easier to evaluate when their inputs, environment, execution steps and outputs are traceable.
The platform is structured around an end-to-end sequence:
- Version the input. DVC associates datasets and experiment inputs with a known version.
- Describe the run. Store the model identifier, code revision, parameters and environment details.
- Execute the work. Python and notebook code run locally on the GPU; Prefect can coordinate multi-step tasks.
- Collect outputs. Store model files, figures and generated artifacts in MinIO, with consistent naming.
- Observe behavior. Use Prometheus/Grafana for system resource trends and Loki for logs.
- Repeat and compare. Change one part of the workflow and document what actually improved.
The technology choices
| Tool | Reason for using it |
|---|---|
| DVC | It connects large-data versions to reproducible code history. |
| Prefect | It makes workflow dependencies, task results and failures easier to follow. |
| MinIO | It offers S3-compatible storage under my own control. |
| Python SDK | It standardizes how an experiment records context, measurements and outputs. |
| Grafana + Prometheus + Loki | They make resource load and failures observable during a run. |
A meaningful benchmark
A useful public result must be repeatable and specific: hardware, model, version, input shape, precision, number of warm-up runs, sample size, timing method and outcome. A one-off screenshot is not enough to explain why a number should be trusted.
My RTX demo guide describes the interactive interface; the research platform case study explains the broader environment supporting it.