End-to-end workflow (high level)

  1. Problem definition — business metric, label definition.
  2. Data collection & ingestion — raw logs, APIs, data lakes.
  3. Exploratory Data Analysis (EDA) — understand distributions, missingness, correlations.
  4. Preprocessing & feature engineering — cleaning, encoding, scaling.
  5. Modeling — choose algorithms, cross-validate, tune hyperparameters.
  6. Evaluation — holdout sets, metrics, calibration, fairness checks.
  7. Deployment — package model, serve via REST/gRPC/edge.
  8. Monitoring & retraining — data drift, model degradation.

Dataset lifecycle

  • Raw data (immutable snapshot) → store as source of truth.
  • Cleaned / transformed (feature store or artifact) → reproducible, versioned.
  • Split into training/validation/test; keep test unseen until final evaluation.
  • Model artifacts (pickled model, scaler, feature list) with metadata (training data version, hyperparams).

Reproducibility essentials

  • Fix random seeds (numpy, scikit-learn, torch).
  • Record package versions (requirements.txt or pip freeze).
  • Store dataset snapshots or use Data Version Control (DVC).
  • Use containers (Docker) and CI pipelines.
  • Keep an experiment tracker (MLflow, Weights & Biases).

Example: simple sklearn pipeline + persist artifacts

from sklearn.pipeline import Pipeline
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import StandardScaler
from sklearn.ensemble import RandomForestRegressor
import joblib

pipeline = Pipeline([
    ("imputer", SimpleImputer(strategy="median")),
    ("scaler", StandardScaler()),
    ("model", RandomForestRegressor(n_estimators=100, random_state=42))
])

pipeline.fit(X_train, y_train)
joblib.dump(pipeline, "model-v1.joblib")