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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Build and deploy a machine learning app from scratch 🚀
| Date | Stars |
|---|---|
| 2026-07-24 | 392 |
| 2026-07-25 | 392 |
| 2026-07-28 | 392 |
| 2026-07-30 | 392 |
| 2026-07-31 | 392 |
| 2026-08-06 | 392 |
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# End 2 End Machine Learning : From Data Collection to Deployment 🚀
In this job, I collaborated with <a href="https://github.com/ahmedbesbes">Ahmed BESBES</a>
Medium post <a href="https://medium.com/@ahmedbesbes/end-to-end-machine-learning-from-data-collection-to-deployment-ce74f51ca203">here</a>.
You may also read about it <a href="https://ahmedbesbes.com/end-to-end-ml.html">here</a> and <a href="https://marwandebbiche.com/posts/e2e-ml/">here</a>.
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](https://www.reviews.ai2prod.com/) we'll build:
<p align="center">
<img src="./blog-post/assets/app.gif" style="margin:15px">
</p>
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 `Selenium` and `Scrapy`
- Training a deep learning sentiment classifier on this data using `PyTorch`
- Building an interactive web app using `Dash`
- Setting a `REST API` and a `Postgres` database
- Dockerizing the app using `Docker Compose`
- Deploying to `AWS`
<hr>
## Project architecture
### Run the app locally
To run this project locally using `Docker Compose` `run`:
```
docker-compose build
docker-compose up
```
You can then access the dash app at [http://localhost:8050](http://localhost:8050)
### Development
If you want to contribute to this project and run each service independently:
#### Launch API
In order to launch the API, you will first need to run a local `postgres` db using `Docker`:
```
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:
```shell
cd src/api/
python app.py
```
#### Launch Dash app
In order to run the `dash` server to visualize the output:
```shell
cd src/dash/
python app.py
```
### How to contribute 😁
Feel free to contribute! Report any bugs in the [issue section](https://github.com/MarwanDebbiche/post-tuto-deployment/issues).
Here are the few things we noticed, and wanted to add.
- [ ] Add server-side pagination for Admin Page and `GET /api/reviews` route.
- [ ] Protect admin page with authentication.
- [ ] Either use [Kubernetes](https://kubernetes.io) or [Amazon ECS](https://aws.amazon.com/ecs) to deploy the app on a cluster of containers, instead of on one single EC2 instance.
- [ ] Use continuous deployment with [Travis CI](https://travis-ci.org)
- [ ] Use a managed service such as [RDD](https://aws.amazon.com/rds/) for the database
### Licence
MIT
Excerpt of 3,207 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:2859cbb3aa8d8141, topic:pytorch
matched fp:2859cbb3aa8d8141, topic:selenium