Epython Lab
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Welcome to Epython Lab, where you can get resources to learn, one-on-one trainings on machine learning, business analytics, and Python, and solutions for business problems.

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I can mention many reasons that FLASK is better than DJANGO but in some cases DJANGO is better than FLASK. I usually use FLASK for doing my tasks. What do you use?

#keynote #FLASK #DJANGO @epythonlab
Epython Lab
Which Framework is more likely used to implement machine learning model prediction task?
#Flask is a customizable Python framework that gives developers complete control over how users access data. Flask is a "micro-framework" based on Werkzeug's WSGI toolkit and Jinja 2's templating engine. It is designed as a web framework for RESTful API development.

#keynote #Flask #framework @epythonlab

Why Flask is Microframework?

Flask is a micro web framework written in Python. It is classified as a microframework because it does not require particular tools or libraries. It has no database abstraction layer, form validation, or any other components where pre-existing third-party libraries provide common functions.
A step by step tutorial how to build a github repo search tool using html, css, and flask.

This project will really help you to get insights on how to  build your own portfolio project using Python as a backend and html CSS as a front end and also integrating third party API to obtain the required data for your project.

Feel free to contribute to this project.

āœ”ļø The source code link is in the description box of this tutorial.
#python #epythonlab #webdevelopment #pythontutorial #flask

https://www.youtube.com/watch?v=iBMtv-MMqKg
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When I build an AI application, choosing the backend framework is an important decision.

There are several good options, but I usually look at š…ššš¬š­š€ššˆ, šƒš£ššš§š šØ, and š…š„ššš¬š¤ first.

The choice really depends on what I'm building.

āœ”ļø š…ššš¬š­š€ššˆ makes a lot of sense when the application is mainly an AI/API backend. Since most AI tools I use are already in Python, I can keep the whole stack in one ecosystem, from LLMs and embeddings to document processing, RAG, databases, and the API itself.

āœ”ļø šƒš£ššš§š šØ is a strong choice when the AI functionality is part of a larger web application. Its built-in ORM, authentication, admin panel, and other features can save a lot of development time.

āœ”ļø š…š„ššš¬š¤ is still a great option when I want something simple, lightweight, and flexible, especially for smaller services or prototypes.

For an AI application, I also need to think beyond the framework:

āœ”ļø Authentication

āœ”ļø Database and data persistence

āœ”ļø Document processing

āœ”ļø Embeddings and vector search

āœ”ļø RAG

āœ”ļø Background tasks

āœ”ļø Testing

āœ”ļø Docker

āœ”ļø Monitoring

āœ”ļø Deployment

There isn't one framework that is "best" for every AI application. For the type of production AI backends I'm building, š…ššš¬š­š€ššˆ is often a practical choice because it provides a clean API layer while keeping everything close to the Python AI ecosystem.

The framework is only one piece of the puzzle.

Good architecture matters more than the framework you choose.


What do you normally use for AI applications: š…ššš¬š­š€ššˆ, šƒš£ššš§š šØ, š…š„ššš¬š¤, or something else?

Here is the roadmap to build an AI application with FastAPI: https://www.youtube.com/watch?v=0SLLG2Z_Htw


#FastAPI #Python #AIEngineering #GenerativeAI #RAG #BackendDevelopment #MachineLearning #Django #Flask #SoftwareArchitecture