End 2 End Machine Learning : From Data Collection to Deployment
In this job, I collaborated with Ahmed BESBES
In this post, we'll go through the necessary steps to build and deploy a machine learning application. This starts from data collection to deployment; and the journey, you'll see, is exciting and fun.
Before we begin, let's have a look at the app we'll build:
As you see, this web app allows a user to evaluate random brands by writing reviews. While writing, the user will see the sentiment score of his input updating in real-time, alongside a proposed 1 to 5 rating.
The user can then change the rating in case the suggested one does not reflect his views, and submit.
You can think of this as a crowd sourcing app of brand reviews, with a sentiment analysis model that suggests ratings that the user can tweak and adapt afterwards.
To build this application, we'll follow these steps:
- Collecting and scraping customer reviews data using
- Training a deep learning sentiment classifier on this data using
- Building an interactive web app using
- Setting a
REST APIand a
- Dockerizing the app using
- Deploying to
Run the app locally
To run this project locally using
docker-compose build docker-compose up
You can then access the dash app at http://localhost:8050
If you want to contribute to this project and run each service independently:
In order to launch the API, you will first need to run a local
postgres db using
docker run --name postgres -e POSTGRES_USER=postgres -e POSTGRES_PASSWORD=password -e POSTGRES_DB=postgres -p 5432:5432 -d postgres
Then you'll have to type the following commands:
cd src/api/ python app.py
Launch Dash app
In order to run the
dash server to visualize the output:
cd src/dash/ python app.py
How to contribute
Feel free to contribute! Report any bugs in the issue section.
Here are the few things we noticed, and wanted to add.
- Add server-side pagination for Admin Page and
- Protect admin page with authentication.
- Either use Kubernetes or Amazon ECS to deploy the app on a cluster of containers, instead of on one single EC2 instance.
- Use continuous deployment with Travis CI
- Use a managed service such as RDD for the database