🚀 You Trained the Model. Now What?

You built the model.
Cross-validation looks great.
Notebook saved.
...Then what?

If no one ever showed you how to move from notebook to production, you're not alone.

😓 Why This Hurts

A model in a notebook doesn’t deliver value.
Until it’s in production, it’s just a demo.

And yet, most ML courses stop at training.

So let’s walk through the missing step: deployment.

✅ Micro-Guide: From Model to Production

Here are 3 simple options — from quick to production-ready.

1. Export + Load with joblib or pickle

Save your trained model:

import joblib
joblib.dump(model, 'model.pkl')

Load it later:

model = joblib.load('model.pkl')
prediction = model.predict(X_new)

🟢 Fast and simple
🔴 Not scalable or safe for untrusted environments

2. Wrap It in a Basic API with Flask

Turn your model into a lightweight web service.

from flask import Flask, request, jsonify
import joblib

model = joblib.load('model.pkl')
app = Flask(__name__)

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

Run it, send JSON, get predictions.

🟢 Great for demos, internal tools
🔴 Needs containerization for production

3. Use MLflow for Lifecycle Management

MLflow helps you track, package, and serve models.

mlflow models serve -m runs:/<run-id>/model -p 5000

✅ Version control
✅ Model registry
✅ REST API built-in

Perfect for teams or if you're working in MLOps stacks.

⚡ Bonus Tip: Start with One Use Case

Don’t aim for enterprise-grade deployment on day one.
Just pick one use case:

• Internal dashboard

• Batch scoring job

• API for another team

Deploy one model end-to-end.
Then improve from there.

📊 Poll

Have you deployed a model to production?
Click here to vote — curious to see where everyone’s at.