https://github.com/curiousily/deploy-keras-deep-learning-model-with-flask

Build Deep Neural Network model in Keras and deploy a REST API to production with Flask on Google App Engine
https://github.com/curiousily/deploy-keras-deep-learning-model-with-flask

airbnb deep-learning google-appengine jupyter-notebook keras machine-learning neural-network price-prediction python regression rest-api scikit-learn tensorflow

Last synced: about 1 month ago
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Build Deep Neural Network model in Keras and deploy a REST API to production with Flask on Google App Engine

• Host: GitHub
• URL: https://github.com/curiousily/deploy-keras-deep-learning-model-with-flask
• Owner: curiousily
• License: mit
• Created: 2019-10-21T15:18:24.000Z (about 5 years ago)
• Default Branch: master
• Last Pushed: 2023-05-01T21:15:57.000Z (over 1 year ago)
• Last Synced: 2024-05-01T18:48:42.339Z (8 months ago)
• Topics: airbnb, deep-learning, google-appengine, jupyter-notebook, keras, machine-learning, neural-network, price-prediction, python, regression, rest-api, scikit-learn, tensorflow
• Language: Jupyter Notebook
• Homepage: https://www.curiousily.com/posts/deploy-keras-deep-learning-project-to-production-with-flask/
• Size: 699 KB
• Stars: 34
• Watchers: 2
• Forks: 18
• Open Issues: 3
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# 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.

[Read the complete guide](https://www.curiousily.com/posts/deploy-keras-deep-learning-project-to-production-with-flask/)

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](https://cloud.google.com/sdk/install)

Clone this repository:

```bash

git clone [email protected]:curiousily/End-to-End-Machine-Learning-with-Keras.git

cd End-to-End-Machine-Learning-with-Keras

```

Install libraries:

```bash

pip install -r requirements.txt

```

## Start local server

```bash

flask run

```

## Make predictions

```bash

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

```bash

gcloud app deploy

```

[Read the complete guide](https://www.curiousily.com/posts/deploy-keras-deep-learning-project-to-production-with-flask/)