Deploy-Keras-Deep-Learning-Model-with-Flask
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Build Deep Neural Network model in Keras and deploy a REST API to production with Flask on Google App Engine
Zero to Production
It is not recommended to deploy your production models as shown here. This is just an end-to-end example to get started quickly.
This guide shows you how to:
- build a Deep Neural Network that predicts Airbnb prices in NYC (using scikit-learn and Keras)
- build a REST API that predicts prices based on the model (using Flask and gunicorn)
- deploy the model to production on Google App Engine
Quick start
Requirements:
- Python 3.7
- Google Cloud Engine account
- Google Cloud SDK
Clone this repository:
git clone [email protected]:curiousily/End-to-End-Machine-Learning-with-Keras.git
cd End-to-End-Machine-Learning-with-Keras
Install libraries:
pip install -r requirements.txt
Start local server
flask run
Make predictions
curl -d '{"neighbourhood_group": "Brooklyn", "latitude": 40.64749, "longitude": -73.97237, "room_type": "Private room", "minimum_nights": 1, "number_of_reviews": 9, "calculated_host_listings_count": 6, "availability_365": 365}' -H "Content-Type: application/json" -X POST http://localhost:5000
Deploy to Google App Engine
gcloud app deploy