Model Registry and Model Serving

Duration: 5 min

This module delves into the critical aspects of Model Registry and Model Serving in the MLOps pipeline. Understanding these components is essential for ensuring that machine learning models are efficiently managed, versioned, and deployed into production environments. We will explore how to register models, handle versioning, and serve models effectively using Python.

Model Registry

A Model Registry is a centralized repository for storing, versioning, and managing machine learning models. It allows data scientists and ML engineers to track model versions, compare performance metrics, and deploy models seamlessly. Using a Model Registry ensures reproducibility and simplifies the process of rolling back to previous model versions if needed.

import mlflow.sklearn

# Initialize MLflow
mlflow.set_tracking_uri("http://localhost:5000")

# Train a simple model
from sklearn.datasets import load_iris
from sklearn.ensemble import RandomForestClassifier

iris = load_iris()
model = RandomForestClassifier()
model.fit(iris.data, iris.target)

# Log the model to MLflow
with mlflow.start_run() as run:
    mlflow.sklearn.log_model(model, "model")
    run_id = run.info.run_uuid
    print(f'Model logged with run ID: {run_id}')

Try it in Google Colab:

Model logged with run ID: <run_id>

Model Serving

Model Serving refers to the process of deploying machine learning models into a production environment where they can receive input data, make predictions, and return results. Effective model serving ensures that models are scalable, reliable, and can handle real-time or batch inference requests. We will explore how to serve models using Flask, a popular web framework in Python.

from flask import Flask, request, jsonify
import joblib

app = Flask(__name__)

# Load the model
model = joblib.load('model.pkl')

@app.route('/predict', methods=['POST'])
def predict():
    data = request.json
    prediction = model.predict([data['features']])
    return jsonify({'prediction': prediction.tolist()})

if __name__ == '__main__':
    app.run(host='0.0.0.0', port=5000)

💡 Tip: Ensure that the model file ('model.pkl') is correctly saved and accessible by the Flask application to avoid runtime errors during prediction.

Key Concepts

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