MLOps Prototyping

Goal

To create standardized, reproducible, and production-ready prototypes in Jupyter notebooks. This skill enforces a structured layout (Imports -> Configs -> Load -> EDA -> Modeling -> Eval) and robust engineering practices (Pipelines, Split-Verification) to prevent technical debt and data leakage.

Prerequisites

• Language: Python 3.14
• Environment: uv managed project (.venv), with ipykernel in a notebook dependency group
• Context: Executed within a .ipynb file or converting to one.

Instructions

1. Notebook Structure

Enforce the following linear sections in every notebook to ensure readability and maintainability.

1. Title & Purpose: H1 Title and a brief description of the experiment goals.
2. Imports: Group standard libraries, third-party, and usage-specific imports.
3. Configs: Define Global Constants (paths, random seeds, hyperparameters) here. No magic numbers deeper in the code.
4. Datasets: Load, validate, and split data.
5. Analysis (EDA): Inspect target distributions and correlations.
6. Modeling: Define and train sklearn.pipeline.Pipeline objects.
7. Evaluations: Compute metrics and visualize performance on held-out data.

2. Configuration Standards

Expose all "knobs" at the top of the notebook for easy experimentation.

• Randomness: Define RANDOM_STATE = 42 and use it in splits and model initialization.

• Paths: Use pathlib for robust path handling.

  from pathlib import Path
  
  ROOT = Path("..")
  DATA_PATH = ROOT / "data" / "input.parquet"
  

• Hyperparameters: Group model params (e.g., N_ESTIMATORS, MAX_DEPTH).

• Toggles: Use booleans for expensive operations (e.g., USE_GPU = True, RUN_GRID_SEARCH = False).

3. Data Management

Ensure data integrity and prevent leakage.

• Loading: Prefer pd.read_parquet for speed/types, or pd.read_csv.
• Splitting:
  • Always split into X_train, X_test, y_train, y_test before any data-dependent transformations (imputation, scaling).
  • Random Split: Use sklearn.model_selection.train_test_split with stratify for balanced classification.
  • Time Series: Use sklearn.model_selection.TimeSeriesSplit if data has a temporal dimension (do NOT shuffle).
  • Use random_state=RANDOM_STATE.

4. Pipeline Construction

Prohibit raw data transformations on the full dataset.

• Mandate: Use sklearn.pipeline.Pipeline or ColumnTransformer.

• Why: Automation of fit on train and transform on test prevents data leakage.

• Example:

  from sklearn.compose import ColumnTransformer
  from sklearn.impute import SimpleImputer
  from sklearn.pipeline import Pipeline
  from sklearn.preprocessing import StandardScaler
  
  CACHE = "./.cache"  # Define a cache directory
  
  numeric_transformer = Pipeline(
      steps=[
          ("imputer", SimpleImputer(strategy="median")),
          ("scaler", StandardScaler()),
      ]
  )
  
  preprocessor = ColumnTransformer(
      transformers=[("num", numeric_transformer, numeric_features)]
  )
  
  # Use 'memory' to cache transformer outputs, speeding up GridSearch
  model = Pipeline(
      steps=[
          ("preprocessor", preprocessor),
          ("classifier", RandomForestClassifier()),
      ],
      memory=CACHE,
  )
  

5. Experiment Tracking from the Notebook

Prototypes are experiments; record them from the first run rather than retrofitting tracking later.

1. Backend: Point MLflow at a SQL store, not the deprecated file store: mlflow.set_tracking_uri("sqlite:///mlflow.db"). SQLite is a real SQLAlchemy backend, it supports the model registry, and it is the same store shape as a production Postgres — so moving up later is a URI change, not a rewrite.
2. Autologging: mlflow.autolog() before fit captures parameters, metrics, and the model for scikit-learn without extra code.
3. Scope: Keep one MLflow experiment per notebook question, and name runs after the hypothesis being tested.

6. Evaluation & Visualization

Go beyond accuracy/MSE.

• Metrics: Use sklearn.metrics appropriate for the task (F1, ROC-AUC, RMSE, MAE).
• Baselines: Compare against a "Dummy" model (mean/mode) to verify learning.
• Visualization:
  • Regression: Residual plots, Actual vs Predicted.
  • Classification: Confusion Matrix, ROC Curve, Precision-Recall.
  • Feature Importance: Visualize feature_importances_ or SHAP values.

7. Transition to Production

Facilitate the move from notebook to python package (src/).

• Function Refactoring: Once a block of code is stable (e.g., a complex data cleaning step), refactor it into a function within the notebook. This makes moving it to a .py file trivial later.
• Cell Tagging: Use tags like parameters (for Papermill) or export to mark cells that should be part of the final documentation or automated pipeline.
• Clean State: Ensure the notebook runs top-to-bottom (Restart Kernel and Run All) without errors before committing.
• Formatting: Ruff 0.16 formats Python inside Markdown too, so mise run format normalizes the snippets you paste into notes and docs. Run mise run all before committing a notebook alongside package code.
• Next step: mlops-industrialization covers the package layout the refactored functions move into.

Self-Correction Checklist

• No Magic Numbers: Are all parameters in the Configs section?
• No Data Leakage: Is fit called ONLY on X_train?
• Reproducibility: Is random_state set for all stochastic operations?
• Resilience: Are paths defined relative to the project root?
• Tracking: Do runs land in a SQL-backed MLflow store rather than the deprecated file store?
• Clarity: Does the notebook read like a report (Markdown cells explaining the Why)?