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Best practices for notebooks and model management with Python/Pyspark

In the data science ecosystem, 🗂️ organization, ⚙️ modularity, and 🔗 traceability are critical to the success of machine learning projects.

This detailed guide will explore 📋 best practices for working with 📓 notebooks in python/pyspark and managing model lifecycle using 🧪 MLflow. Let’s take a look:

 

1️⃣ Strategic Organization of Notebooks: The first thing you have to do, as always, is get organized. Governance!

a) Modular Structuring

The key to an efficient data project is to divide the workflow into specialized notebooks:

  • Ingestion: Capturing data from original sources.
  • Transformation: Data cleaning and preparation.
  • Feature Engineering: Generating features for models.
  • Training: Building and evaluating models.
  • Prediction: Generating inferences.
b) Naming Convention

Use clear and sequential nomenclature:

01_data_ingestion.ipynb
02_data_processing.ipynb
03_feature_engineering.ipynb
04_model_training.ipynb
05_model_inference.ipynb
c) Smart Modularization

c.1) Reusable Functions: Create utility scripts to share code between notebooks:

# utils.py
def clean_data(df):
return df.dropna()def feature_scaling(df):
return (df – df.mean()) / df.std()

c.2) Dynamic Widgets: Parameterize your notebooks for greater flexibility:

.widgets.text(“input_path”, “/mnt/raw_data/”)
input_path = dbutils.widgets.get(“input_path”)

 

2️⃣ ML model lifecycle management: and here we are going to have to do a treatment by experiments!

a) Structured Experimentation

Define experiments

mlflow.set_experiment(“/Users/your_user/proyect_performance”)with mlflow.start_run() as run:
mlflow.set_tag(“model”, “RandomForest”)
mlflow.set_tag(“client”, “pilot_1”)
b) Advanced Versioning

Register models

# Register model after training
model_uri = f”runs:/{run.info.run_id}/model”
mlflow.register_model(model_uri, “PerformanceModel”) # Manage model states
client = MlflowClient()
client.transition_model_version_stage(
name=”PerformanceModel”,
version=1,
stage=”Production”
)
c) Complete Traceability

Make a detailed record

with mlflow.start_run():
mlflow.log_params({
“depth”: 10,
“n_estimators”: 100
})
mlflow.log_metric(“accuracy”, 0.95)
mlflow.sklearn.log_model(model, “model”)

 

3️⃣ Deployment Best Practices: finally, it’s time to go live!

a) Prior Validation
  • Test models in the staging stage before production
  • Use recent data for validation
b) Serve as Models

Let’s get ready to expose via APIs…

from mlflow.pyfunc import load_modelmodel = load_model(“models:/PerformanceModel/Production”)
predictions = model.predict(new_data)

Conclusions

If you implement these practices you have a high chance of improving:

📜 Reproducibility of experiments

🔗 Traceability of models

📈 Scalability of machine learning projects

The combination of 📓 modular notebooks and 🧪 good management of the ML model cycle transforms data projects into systematic and controlled processes.

 

Are you now ready to better manage ML scripts in your organization?

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