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Showing posts with the label integration

Python Code Testing

 Let's look at how to test our Python code and follow the code coverage as much as possible. How to follow the MVC pattern in FastAPI How to write Pythonic code Types of testing with pytest Usage of patching , monkeypatching , fixture , and mocking 🚀 How to Follow the MVC Pattern in FastAPI FastAPI doesn’t enforce a strict MVC structure, but you can follow an organized MVC-like structure: 🔹 MVC Directory Structure Example app/ │ ├── models/ # ORM models (e.g., SQLAlchemy) │ └── user.py │ ├── schemas/ # Pydantic schemas (DTOs) │ └── user.py │ ├── controllers/ # Business logic (aka services) │ └── user_controller.py │ ├── routes/ # Route definitions │ └── user_routes.py │ ├── main.py # Entry point └── database.py # DB engine/session 🔹 MVC Mapping Model → app/models/ View → app/routes/ (FastAPI endpoints) Controller → app/controllers/ (business logic) 🐍 How to Write Pythonic Code Follo...

AI Integration

Following are some questions regarding Python and AI integration.  1. What is AI integration in the context of cloud computing? Answer: AI integration in cloud computing refers to the seamless incorporation of Artificial Intelligence services, frameworks, or models into cloud platforms. It allows users to leverage AI capabilities without managing the underlying infrastructure. 2. How can Python be used for AI integration in the cloud? Answer: Python is widely used for AI integration in the cloud due to its extensive libraries and frameworks. Tools like TensorFlow, PyTorch, and scikit-learn are compatible with cloud platforms, enabling developers to deploy and scale AI models efficiently. Also, it can use different MVC frameworks eg. FastAPI, Flask or serverless functions eg. Lmabda or Azure function 3. What are the benefits of integrating AI with cloud services? Answer: Integrating AI with cloud services offers scalability, cost-effectiveness, and accessibility. It allows businesse...