Turn a modelling notebook into a reusable ML pipeline

Why use this

1. A data scientist may have an exploratory notebook that works in one session but depends on hidden cell order, local state or undocumented data retrieval. Turning it into a reusable pipeline can preserve those accidents unless the dependencies are made explicit.

2. This job could prepare a stage-by-stage migration plan with input, training and evaluation boundaries visible. The team can review what must remain equivalent before treating a cleaner structure as a reliable or production-ready modelling workflow.

Why an agent / simpler alternative

Useful judgment

Identify hidden dependencies, separate stages and preserve evaluation intent without silently changing modelling assumptions.

Simpler alternative

Keep a cleaned notebook when the analysis is infrequent and does not need shared orchestration.

Unsuitable when

The task would expose secrets, retrain on unauthorized data or deploy a model without appropriate validation.

Proposed inputs

1. Name

   Notebook manifest

   Owner

   Authorized data owner

   Minimum scope

   Relevant cells, dependencies and artifact descriptions

   Quality check

   Exclude credentials and identify hidden state

2. Name

   Pipeline requirements

   Owner

   ML owner

   Minimum scope

   Stage boundaries, data versions and evaluation criteria

   Quality check

   Separate reproducibility goals from production claims

Access and setup — proposed or unknown

Proposed minimum

1. Use fictional cell descriptions and a pipeline diagram; no notebook execution, data retrieval, training or deployment.

Compatibility unknowns

1. Current framework versions, data access, orchestration support and runtime compatibility are unknown.

External actions requiring approval

1. Repository changes, data access, compute spending, persistent integrations and model deployment requires authorization.

Expected deliverable and review checks

Proposed artifact

Pipeline-design candidate with stage contracts, dependencies, versioning and proposed equivalence checks.

Critical checks

1. Preserve data splits and preprocessing order; expose implicit state; distinguish training artifacts from evaluation evidence.

Missing output evidence

This overlay adds no new output inspection, execution or validation; consult the referenced canonical source outcome limits.

Failure signals and proposed responses

1. Signal

   A notebook cell relies on an earlier manual variable

   Why it matters

   The pipeline lacks a required input

   Proposed response

   Make the dependency explicit or request the intended value.

2. Signal

   Refactoring moves preprocessing across the data split

   Why it matters

   Evaluation meaning may change

   Proposed response

   Hold the change for ML review and preserve the original sequence.

Effort and cost — unknown

Setup work

1. Document notebook assumptions and stage contracts.

Ongoing review

1. ML reviewers inspect future authorized outputs and equivalence evidence.

Unknowns

1. Data reproducibility, model validity, compute requirements and migration costs are unknown.

First check — proposed, unexecuted

Status

proposed_unexecuted

Synthetic fixture

Invent a notebook outline with out-of-order state and ambiguous preprocessing placement; draft a stage map only.

Acceptance checks

1. Flag hidden dependencies; preserve evaluation boundaries; train nothing and claim no equivalence.

Stop conditions

1. Stop if data authority, stage semantics or evaluation requirements are unclear.

Limits

1. A pipeline design cannot establish reproducibility, predictive quality or production readiness of an ML model.