MLOps Walkthrough with Jupyter Notebook Integration

Production-grade machine learning documentation pairs code, metrics, and narrative. This guide walks through a churn prediction notebook and highlights how the DataLog theme embeds notebooks with launch buttons for popular runtimes.

Published
April 4, 2024
Author
Reading time
2 mins read
Difficulty
Intermediate
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Online at https://diogoribeiro7.github.io/analytics-blog-jekyll/2024/04/04/machine-learning-notebook-integration/

Production-grade machine learning documentation pairs code, metrics, and narrative. This guide walks through a churn prediction notebook and highlights how the DataLog theme embeds notebooks with launch buttons for popular runtimes.

Notebook overview

The project notebook notebooks/churn-segmentation.ipynb contains:

  • Feature engineering with pandas and scikit-learn ColumnTransformer
  • Model training using xgboost.XGBClassifier
  • MLflow logging for parameters, metrics, and artifacts
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from xgboost import XGBClassifier
from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import OneHotEncoder, StandardScaler

numeric = ["monthly_charges", "tenure", "support_tickets"]
categorical = ["contract", "region"]

preprocess = ColumnTransformer(
    [
        ("num", StandardScaler(), numeric),
        ("cat", OneHotEncoder(handle_unknown="ignore"), categorical),
    ]
)

model = Pipeline(
    steps=[
        ("preprocess", preprocess),
        (
            "classifier",
            XGBClassifier(
                max_depth=4,
                n_estimators=200,
                subsample=0.8,
                colsample_bytree=0.9,
                eval_metric="auc",
            ),
        ),
    ]
)

Launch options

Run in Binder Open in Colab View on GitHub

Readers can open the notebook in the environment of their choice, while the theme preserves accessibility labels for screen readers.

Track experiments

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import mlflow

mlflow.set_experiment("churn-segmentation")
with mlflow.start_run(run_name="xgboost-baseline"):
    model.fit(train_features, train_labels)
    auc = model.score(test_features, test_labels)
    mlflow.log_metric("test_auc", auc)
    mlflow.xgboost.log_model(model.named_steps["classifier"], "model")

Embed evaluation tables and charts produced by MLflow in the post so stakeholders understand progress:

Metric Value
Validation AUC 0.864
Test AUC 0.851
Drift monitor Stable

Checklist before deployment

  • Notebook executed from top to bottom without errors
  • Model registered with reproducible environment metadata
  • Alert thresholds documented for precision/recall trade-offs

DataLog’s notebook integration keeps workflows transparent—link to runnable notebooks, surface experiment logs, and capture decisions alongside the code that produced them.

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    The code, data and environment behind this article.

    Source code
    github.com/DiogoRibeiro7/analytics-blog-jekyll at v0.8.0
    Data
    /analytics-blog-jekyll/datasets/sample-dataset/ version v1
    Environment
    requirements.txt

    Embed interactive plots, widgets, and demos using <figure>, <iframe>, or <div class="interactive-embed"> containers. Ensure each embed includes descriptive captions for accessibility.

    © 2024 Diogo Ribeiro. Text and figures under CC BY 4.0.

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    Use the quick export buttons to save citations for reference managers or copy the formatted text directly.

    Diogo Ribeiro (2024). MLOps Walkthrough with Jupyter Notebook Integration. DataLog | Data Science & Research Theme. https://diogoribeiro7.github.io/analytics-blog-jekyll/2024/04/04/machine-learning-notebook-integration/.

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    Open science & reproducibility badges

    These badges highlight the transparency practices applied to this work. Hover or focus on each badge to learn more about the criteria.

    • Open Data Dataset and code repository published with permissive license. Public repository, DOI issued, README with reproduction steps.
    • Reproducible Workflow Containerized environment and automated tests provided. Continuous integration pipeline with reproducibility checks.
    • Transparent Peer Review Peer review reports archived with DOI and linked to article. Open peer review statement and archived reports on Zenodo.

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