Building and Deploying a Machine Learning API using FastAPI and scikit-learn

⚡️FastAPI and scikit-learn are powerful tools for building and deploying machine learning APIs.

🔧Creating a machine learning API requires defining the API endpoint, handling requests, and making predictions.

📦Dependencies such as uvicorn, pandas, and pickled scikit-learn models are necessary for building the API.

🚀FastAPI's automatic data validation and API documentation make it efficient and user-friendly.

💡By following best practices and using the correct data types, we can create a well-structured and functional API.

What is FastAPI?

—FastAPI is a modern, fast (high-performance), web framework for building APIs with Python 3.7+ based on standard Python type hints.

What is scikit-learn?

—Scikit-learn is a free machine learning library for Python that provides simple and efficient tools for data mining and data analysis.

What is an API endpoint?

—An API endpoint is a specific URL or URI (Uniform Resource Identifier) that an API exposes to enable communication with other software.

How can I deploy a FastAPI app?

—You can deploy a FastAPI app to various platforms, including cloud services like Heroku or using containerization tools like Docker and Kubernetes.

Can I use models trained in other libraries with FastAPI?

—Yes, FastAPI can work with models trained in other libraries such as scikit-learn. You can load the pre-trained model and use it for making predictions in your API.

00:00In this episode, we introduce a new series called "Code That", where we code projects in a short timeframe.

01:20We start by creating the environment and installing necessary dependencies such as uvicorn, pandas, and scikit-learn.

03:09Using FastAPI, we define the API endpoint and handle incoming requests.

08:56We load a pre-trained machine learning model using pickle and the sklearn library.

10:45We make predictions using the model and return the results as a response from the API.