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Build and deploy reproducible production ML pipelines for research
Passed 14 of 22 test cases with the skill, and 9 of 22 without it.
and 3 more in the full report
6 fixed8 passed either way7 still failing1 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 6 test cases the skill fixed. Same model, same prompt, with and without the skill.
In our dvc.yaml pipeline file under the train stage, we want to track validation metrics and training loss curves without caching metrics files in DVC history. How should the metrics and plots fields be configured for metrics.json and plots/training_curve.csv?
| Checked | Without skill | With skill |
|---|---|---|
| Configures metrics.json with cache set to false. | Passed | Passed |
| Configures plots/training_curve.csv mapping x to epoch and y to loss. | Missed | Passed |
The instructions your model reads when the skill loads.
Machine learning research increasingly demands reproducible, end-to-end pipelines that go beyond a single training script. A research ML pipeline encompasses data ingestion, feature engineering, model training, evaluation, experiment tracking, and artifact management. Without a structured pipeline, research results become difficult to reproduce, ablation studies become error-prone, and collaborators cannot build on prior work.
This guide covers the practical tools and patterns for building ML pipelines in an academic research context. The focus is on reproducibility, experiment tracking, and the transition from notebook prototyping to structured experiments. The patterns use MLflow, DVC, and standard Python tooling -- chosen because they are open source, widely adopted in published research, and require minimal infrastructure.
Unlike industry MLOps guides that emphasize deployment at scale, this guide prioritizes the research workflow: running many experiments, tracking what changed between runs, and producing results that reviewers can verify.
A research ML pipeline typically has five stages:
Data Ingestion → Feature Engineering → Training → Evaluation → Artifact Storage
│ │ │ │ │
├── raw data ├── transforms ├── model ├── metrics ├── models
├── splits ├── features ├── logs ├── plots ├── configs
└── metadata └── cache └── ckpts └── tables └── reports
project/
├── configs/
│ ├── base.yaml # Default hyperparameters
│ ├── experiment_001.yaml # Experiment-specific overrides
│ └── sweep.yaml # Hyperparameter search space
├── data/
│ ├── raw/ # Immutable original data
│ ├── processed/ # Cleaned and transformed
│ └── splits/ # Train/val/test splits (versioned)
├── src/
│ ├── data/ # Data loading and preprocessing
│ ├── features/ # Feature engineering
│ ├── models/ # Model definitions
│ ├── training/ # Training loops
│ └── evaluation/ # Metrics and visualization
test cases passed, vs 9 / 22 without
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