Model Versioning in Machine Learning

Introduction

Model versioning is the process of tracking and managing different versions of machine learning models over time. It helps data scientists and engineers keep control of changes, reproduce results, and deploy the right model confidently.

بالعربية المغربية (الدارجة): تتبع نسخ النماذج (Model Versioning) هو الطريقة اللي كتنظم بيها الإصدارات المختلفة ديال النموذج. بهاد الطريقة نقدر نعرف شنو تبدّل، ونعيد التجارب، ونخدم بالإصدار الصحيح بسهولة.

Why Model Versioning Matters

• Ensures reproducibility of experiments.
• Keeps a record of model improvements.
• Prevents confusion during deployment.
• Allows rollback to a previous stable version.
• Facilitates collaboration between data teams.

بالعربية المغربية: التتبع ديال النسخ كيعطينا إمكانية نرجعو لأي نسخة قديمة، نعرفو شنو تحسّن، ونخدمو كفريق بلا مشاكل.

Core Concepts Explained

• Model Metadata: Information about the model (parameters, dataset version, metrics).
• Version Identifier: A unique tag or hash assigned to each model version.
• Artifact Storage: A repository where model files are saved (like S3, DVC remote, MLflow server).
• Version Control Integration: Tools like Git or DVC that track experiments, data, and models.

Example: Using DVC for Model Versioning

DVC (Data Version Control) is a tool that helps track datasets and models like Git tracks code.

# Initialize DVC in your project
dvc init

# Add a trained model file to version control
dvc add model.pkl

# Save the change in Git
git add model.pkl.dvc .gitignore
git commit -m "Add model version 1.0"

# Push model to remote storage
dvc remote add -d myremote s3://my-bucket/models
dvc push

This process saves your model with its metadata and links it to the specific experiment that produced it.

بالعربية المغربية: بـ DVC نقدر نسجّل النموذج ديالنا بحال الكود. كنضيفو، كنحطّو فالـGit، ومن بعد كنرفعو للسيرفر. كل نسخة كتكون مربوطة بتجربة محددة.

Python Example: Tracking Model Versions with MLflow

MLflow is another tool used to manage model versions, experiments, and deployments.

import mlflow
import mlflow.sklearn
from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split

# Load data
X, y = load_iris(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

# Train model
model = RandomForestClassifier(n_estimators=50, random_state=42)
model.fit(X_train, y_train)
accuracy = model.score(X_test, y_test)

# Track model with MLflow
mlflow.set_experiment("iris-classification")
with mlflow.start_run():
    mlflow.log_param("n_estimators", 50)
    mlflow.log_metric("accuracy", accuracy)
    mlflow.sklearn.log_model(model, "model")

print("Model version logged successfully.")

Explanation of the Example

• The model and parameters are saved automatically by MLflow.
• Each experiment run creates a unique version ID.
• All metrics and models can be viewed in the MLflow UI.

بالعربية المغربية: MLflow كيسجّل النموذج، المعاملات، والنتائج فكل تجربة. كل مرة كتدير تجربة جديدة كتولد نسخة جديدة بآيدي خاص بيها.

Best Practices for Model Versioning

• Tag models with clear version numbers (v1.0, v1.1).
• Always log dataset version and preprocessing steps.
• Store evaluation metrics with each model.
• Use automation to register models after training.
• Link each model to its source code commit.

Common Tools for Model Versioning

Tool	Main Purpose
Git	Code versioning
DVC	Data and model tracking
MLflow	Experiment and model version management
Weights & Biases (W&B)	Visualization and experiment tracking
TensorBoard	Model metrics visualization

بالعربية المغربية: كاينين بزاف ديال الأدوات اللي كتعاون فالتتبع: Git للكود، DVC للبيانات والنماذج، وMLflow للتجارب. كل وحدة كتخدم جزء مهم فالسيرورة ديال التعلم الآلي.

10 Exercises for Practice

1. Define what model versioning means in machine learning.
2. List three benefits of using model versioning.
3. Explain the difference between Git and DVC.
4. Set up DVC in a local project and add a model file.
5. Use MLflow to log model metrics and parameters.
6. Create a versioning system using model version tags (v1, v2).
7. Track both data and model versions for one experiment.
8. Connect DVC or MLflow with remote storage (like S3 or Google Drive).
9. Build a Python script to automatically register new model versions.
10. Discuss how versioning improves collaboration in AI projects.