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…
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? – If you need help with your projects, we can accompany you in the end-to-end process and take it to the next level, send us a message. You can find more notes from the world of technology and data on our blog.
