Deploying ML Models as API using FastAPI

Machine Learning models have gained immense popularity in recent years due to their ability to analyze data and make predictions. One of the key steps in the machine learning process is deploying the models so that others can use them. FastAPI is a Python web framework that makes it easy to deploy machine learning models as APIs.

What is FastAPI?

FastAPI is a modern, fast (high-performance), web framework for building APIs with Python 3.6+ based on standard Python type hints. It is easy to use, flexible, and efficient, making it an ideal choice for deploying machine learning models as APIs.

How to Deploy ML Models as API using FastAPI

Here is a simple step-by-step guide to deploying machine learning models as APIs using FastAPI:

1. Install FastAPI and Uvicorn:

    
    pip install fastapi
    pip install uvicorn
    
  

2. Create a Python file for your API:

    
    from fastapi import FastAPI
    app = FastAPI()
    
    @app.get("/")
    def read_root():
        return {"Hello": "World"}
    
  

3. Run your API using Uvicorn:

    
    uvicorn your_api_file_name:app --reload
    
  

4. Test your API by visiting http://localhost:8000 in your browser.

Conclusion

FastAPI is a powerful tool for deploying machine learning models as APIs. By following the simple steps outlined above, you can quickly create and deploy your own machine learning API. This makes it easy for others to access your models and use them in their own applications.