Deploying Machine Learning Models with Flask and AWS Lambda: A Complete Guide
In essence, this article is about:
1) Training a sample model and uploading it to an S3 bucket:
```python from sklearn.datasets import load_iris from sklearn.model_selection import train_test_split from sklearn.linear_model import LogisticRegression import joblib
Load the Iris dataset
iris = load_iris() X, y = iris.data, iris.target
Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
Train the logistic regression model
model = LogisticRegression(max_iter=200) model.fit(X_train, y_train)
Save the trained model to a file
joblib.dump(model, 'model.pkl') ```
- Creating a sample Zappa config, because AWS Lambda doesn’t natively support Flask, we need to use Zappa, a tool that helps deploy WSGI applications (like Flask) to AWS Lambda:
```json { "dev": { "app_function": "app.app", "exclude": [ "boto3", "dateutil", "botocore", "s3transfer", "concurrent" ], "profile_name": null, "project_name": "flask-test-app", "runtime": "python3.10", "s3_bucket": "zappa-31096o41b" },
"production": {
"app_function": "app.app",
"exclude": [
"boto3",
"dateutil",
"botocore",
"s3transfer",
"concurrent"
],
"profile_name": null,
"project_name": "flask-test-app",
"runtime": "python3.10",
"s3_bucket": "zappa-31096o41b"
}
} ```
- Writing a sample Flask app:
```python import boto3 import joblib import os
Initialize the Flask app
app = Flask(name)
S3 client to download the model
s3 = boto3.client('s3')
Download the model from S3 when the app starts
s3.download_file('your-s3-bucket-name', 'model.pkl', '/tmp/model.pkl') model = joblib.load('/tmp/model.pkl')
@app.route('/predict', methods=['POST']) def predict(): # Get the data from the POST request data = request.get_json(force=True)
# Convert the data into a numpy array
input_data = np.array(data['input']).reshape(1, -1)
# Make a prediction using the model
prediction = model.predict(input_data)
# Return the prediction as a JSON response
return jsonify({'prediction': int(prediction[0])})
if name == 'main': app.run(debug=True) ```
- Deploying this app to production (to AWS):
bash
zappa deploy production
and later eventually updating it:
bash
zappa update production
- We should get a URL like this:
https://xyz123.execute-api.us-east-1.amazonaws.com/production
which we can query:
curl -X POST -H "Content-Type: application/json" -d '{"input": [5.1, 3.5, 1.4, 0.2]}' https://xyz123.execute-api.us-east-1.amazonaws.com/production/predict
