AI Ml Pipeline

Contract

• Input: dataset description, problem type, performance target.
• Output: pipeline architecture + training report + deployment checklist.
• Side effects: may create artifacts (models, logs) when executed.
• Dependencies: external ML framework (scikit-learn, PyTorch, TensorFlow, XGBoost) and data source.
• Stop condition: pipeline documented; model validated; checklist filled.
• Risk: medium — model decisions affect users; requires validation.
• Boundary: designs pipeline; does not train production models unless explicitly executed.

ML Pipeline Design

Build a machine-learning pipeline from data to deployed model with reproducibility and fairness checks.

Process

1. Frame the problem

State: supervised / unsupervised / reinforcement; classification / regression / clustering; time-series / tabular / image / text / tabular-time-series.

Completion criterion: problem type named; target metric defined.

2. Data design

• Source (database, API, file store, synthetic).
• Schema (features, target, weights, time stamps).
• Preprocessing (imputation, encoding, scaling, feature engineering, augmentation).
• Train / validation / test split; stratification if needed.

Completion criterion: data schema and split strategy documented.

3. Model selection

• Baseline (simple: logistic regression, linear, random forest, XGBoost).
• Advanced (deep learning, transformers, ensembles).
• Selection criteria: interpretability vs performance vs resource cost.

Completion criterion: model family selected with justification.

4. Training design

• Loss function; optimiser; learning rate schedule.
• Cross-validation strategy (k-fold, time-series split, stratified).
• Regularisation (L2, dropout, early stopping, data augmentation).
• Reproducibility: seed, versioned library, container.

Completion criterion: training plan documented with seeds and versions.

5. Evaluation

• Metrics aligned with business goal (accuracy / F1 / ROC-AUC / MAE / RMSE / log-loss / lift / ranking metrics).
• Validation vs test distinction; no lookahead.
• Error analysis (confusion matrix, worst-case examples, subgroup differences / fairness audit).

Completion criterion: evaluation report with subgroup fairness check.

6. Deployment and monitoring

• Model registry (MLflow, DVC, Weights & Biases).
• Container (Docker / Kubernetes) with pinned versions.
• Monitoring: drift (data / concept / performance), latency, error rate.
• Rollback plan.

Completion criterion: deployment checklist complete; monitoring rules defined.