I have been working on a few ML projects recently and I thought of deploying the ML models into a web-based UI rather than the Cloud-based UIs.

For those of you thinking — What's the difference? For cloud-based UIs you need to create an account, then may be as well pay money for extra features like — more storage / RAM / CPUs / GPUs. Few examples for cloud-based UIs (rather boxes) are: digitalocean.com, linode.com, vultr.com, Google colab etc. Whereas for UI based — it could be as simple as your local machine.

While watching a few videos based on the same on youtube.com — one thing that strike me — was the use of Streamlit (streamlit.io). Luckily I could also watch a video based on Django — which also did a comparison between the two...

I decided to go through the same process of comparison and then thought of documenting the same. Hence, here is this article.

1. Framework Complexity:

- Django: It's a high-level, full-stack web framework known for its "batteries-included" philosophy, meaning it provides a wide array of built-in features which can be beneficial for complex or larger projects.

- Streamlit: On the other hand, is more streamlined and is touted as a toolbox for creating interactive and performant user interfaces. It's often recommended for quick prototyping or smaller projects.

2. Learning Curve:

- If your focus is more on the ML part and you prefer a quicker setup for your web app, Streamlit might be a better choice. It's considered to have a lower learning curve compared to Django.

3. Customization:

- Django: Offers a higher degree of customization due to its comprehensive framework nature. It allows for more control over HTML and CSS, and the use of third-party tools like Bootstrap and React, which can be beneficial if you have specific UI/UX requirements.

- Streamlit: While it's more straightforward, it may not offer the same level of customization as Django.

4. Integration:

- Django and other traditional frameworks like Flask allow you to integrate Python functionality and "offline apps" with HTML logic to build web apps. However, they may not have specific tools for building all the required ML functionalities, and might require additional setup.

5. Deployment:

- Streamlit is often praised for its ease of deployment for ML models, allowing developers to quickly turn data scripts into shareable web apps.

6. Community and Support:

- Both Django and Streamlit have active communities, but Django being older, has a larger community and more extensive documentation.

Hope you could compare the differences and take a decision of the right tool for your usage and requirements. Till my next article, adios.. Happy learning…