Vinay Komaravolu

Our team will be using Git for the version control and will be stored on Github. All changes will be done through pull requests to ensure the master repository is deployable at all times and error free.

We will be using Postman to test out our API endpoints. It is also possible to create/automate tests and makes it easier to debug projects.

As for our editor, we have decided to use Visual Studio Code because of its many extensions, simplicity, and familiarity. There are a lot of core extensions that can be used for web development as well as version control integration.

Heroku offers fast deployment which is very useful for our fast paced startup environment. Deploying is also very simple and doesn't require much setup compared to other hosting services making it a good choice for our MVP. It’s also free.

We decided to use scikit-learn as our machine-learning library as provides a large set of ML algorihms that are easy to use. scikit-learn is also scalable which makes it great when shifting from using test data to handling real-world data. scikit-learn also works very well with Flask. Numpy and Pandas are used with scikit-learn for data processing and manipulation.

Backend:

Python is a great industry standard language that can easily handle both machine learning and web development tasks. Our dev team is very familiar with the language and has used it in various web and Machine learning projects. Python has many versatile ML specific libraries that include TensorFlow, Pytorch, Pycaret, and Keras. It also has packages for data manipulations and visualization like Numpy, Pandas, and Matplotlib. Since our software requires machine learning algorithms, big data processing and a backend server, Python seemed like the way to go.

Our team decided to go distributed databases (NoSQL) over a relational database (SQL) because of the NoSQL dynamic schemas for unstructured data. We are using MongoDB as our NoSQL database due to its simplicity, schema less documentation, deep/fast querying ability, user data management, big data, JSON style documents, and great scaling out. We also chose MongoDB due to its horizontal scaling as a NoSQL database.

Since we are using python as our backend programming language, we decided to use Flask as our web framework. Flask is a micro and lightweight web framework that provides the required functionality to efficiently develop our web server. Flask has a great community with many online resources and provides more flexibility in terms of customization when compared to other frameworks like Django. While Django is great for large scale applications, it does not work well with NoSQL databases.

Frontend:

For our front end framework, we decided to go with React due to its component based structures, flexibility, scalability, and high performance. React has a strong community and is trusted by top companies such as Facebook, Netflix, and Paypal. We can also easily transition our react app to a react native or electron app. We will also be using material-ui framework alongside react for that crisp google material design!

Node.js will be used for development purposes for the front end only. Once we deploy for production, the react frontend will be served from the flask web server and will not require Node.js. This separates the frontend and backend during development, making it easier to work with.

Javascript is one of the most widely used languages for front end development and we will be using it alongside React to develop our user interface. Our dev team is familiar with it through previous web projects. Given how popular it is, its community is very active for any problems that come up and is easy to hire for in the future.