Please checkout:
In detail, in this project I have explored Neural-Style-Transfer. In particular, I have built a free Neural Style Transfer web-application that runs entirely on Google Cloud. The idea is to use predominately Cloud services and Open-Source technologies to make the app tangible and accessible for everyone. In detail, to build and run the web-application, I used:
- Google App Engine
- Google Cloud Storage
- TensorFlow Hub
- Flask
Tl;dr: Machine Learning does not have to be a black-box. Applications that use ML generally consist of different building blocks. My motivation to build the application and write the blog post mentioned above is entirely focused on educational purposes. Further, I want to introduce a simple guide on how to deploy ML applications in the Cloud and make them accessible to the wider public. In this blog post, I am only going to outline the steps on how to deploy the machine learning model and web application. I uploaded the entire code in this github repo.
the repo does hold the following:
- app.yaml (to specify the python runtime)
- cron.yaml (to avoid shutting down of the Google app engine)
- main.py (the python application with the ml model)
- requiremenrs.txt (to specify the dependencies)
- A static folder (including the web interface and the images / favicons used
Example pic:
