So here comes the most awaited part of the most talked about yet the most undisclosed knowledge in the field of ML or Data Science.
Well that is the deployment of the applications as web app and other integrated forms. Now Python being the programming tool for both the communities has a lot to play here. As we mentioned that Tableau and Excel are some platforms to perform data science, they holdback in terms of deployment as an application or something that can hold its own as a standalone web app.
Streamlit is the best and the most easy to implement framework for web deployment for data scientists with a little kink for coding. You can customize the UI but its still limited and you need to force in some hybrid frontend tech stack to do so.
Flask on the other hand can help you get the best out of the UI part. Both the libraries are easy to be implemented and a lot fun to be used.
Here, I will link some projects of the same genre that I had posted.
Movie Recommendation System - Flask
Well all the things that you need to add and maintain are there, mentioned in the repositories.
Here is a link to one blog that can help you further.
https://towardsdatascience.com/building-a-machine-learning-web-application-using-flask-29fa9ea11dac
Now, I have also created a PWA that has all the major ML apps available for viewing the code and the deployed versions.
The above app also showcases the wonders that Streamlit library can do. However, that topic requires a dedicated blog that is soon to be released.
Recently there has been another method added to the arsenal.
Read about this thing called : Mercury
Read about it here :
https://towardsdatascience.com/create-a-web-app-from-your-jupyter-notebook-with-mercury-21239b7abb37
Those who are willing to learn ML and Data Science in depth, I have some good news to share, my book on the same topic is releasing in two months
Stay-Tuned
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