# Build Ml Pipeline

> Declare the pipeline from data source to predictor as a skrub DataOps graph (not as a bare sklearn.Pipeline). Every step is either a pure-Python function (stateless) attached via .skb.applyfunc, or a sklearn-compatible estimator (stateful) attached via .skb.apply. Stops at the declared object — no fit, split, tuning, persistence, or evaluation. TRIGGER — any of: - Writing or editing code that declares any link in the chain data source → predictor: loaders, preprocessing, encoders / imputers / scalers, feature steps, composition objects (Pipeline, ColumnTransformer, skrub tabularpipeline, nn.Module), or the final estimator. - A pure-Python data-processing function destined for the pipeline path (cleans / derives / reshapes) — whether wrapped via FunctionTransformer, skrub.@deferred / skrub.var, a custom BaseEstimator subclass, or just called in the training path before the estimator. - A step is added, removed, swapped, or reordered inside an existing pipeline declaration. - A bare sklearn.Pipeline / makepipeline is being used as the top-level — fire to redirect into a skrub DataOps graph. - The user asks to build / declare / set up a pipeline / classifier / regressor for X. SKIP when: .fit(...) calls / training loops / Trainer.fit / epoch loops; train/test split or cross-validation splitting; hyperparameter search; persistence (joblib.dump, checkpointing); evaluation / metrics / scoring; inference over a pre-trained model; pure EDA; library-choice questions with no concrete declaration in play. HOW TO USE: consult before the first declarative line and on every structural edit (added/swapped step, changed input columns, changed estimator family). Don't re-consult for cosmetic edits. First, read the Stop conditions and emit the Pre-flight checklist as visible text before any code. Always invoke python-api to confirm skrub / sklearn symbol names and signatures before typing — don't guess from memory.

## Facts
- Page: https://tashan.sh/capability/skill-probabl-ai-build-ml-pipeline
- tashan id: skill:probabl-ai/build-ml-pipeline
- Source: https://github.com/probabl-ai/skills
- Type: skill
- Category: ai
- tashan score: not scored (catalogued only — too little public evidence)
- Adoption: 9.0
- Upkeep: 97.0
- Freshness: 94.0
- Evidence coverage: 84% of the inputs this score can use
- Health: active
- Instruction depth: not yet graded
- License: BSD-3-Clause
- Official: no

## Install

```sh
cp -r build-ml-pipeline ~/.claude/skills/
```

## Security audit
Not scanned. We audit npm-published capabilities; this one has no npm package we can resolve, or has not reached the queue. This is not a clean bill of health.

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Measured 2026-08-14 by tashan (https://tashan.sh) from public evidence. Scorer s5.