FastAPI ML Model Deployment: Your First API!
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🚀 Deploy your machine learning models with FastAPI! This video is the first in a series on MLOps, covering FastAPI, Streamlit, MLflow, Gradio, Hugging Face, Flask, and Airflow.
In this tutorial, you'll learn how to:
Set up a basic FastAPI application.
Load your trained ML model (e.g., from scikit-learn or TensorFlow).
Create an API endpoint to receive input data.
Process the data through your model.
Return predictions in a JSON format.
Test your API with a simple request.
We'll walk you through the entire process step-by-step, making it easy to deploy your models and integrate them into real-world applications.
This is perfect for data scientists, machine learning engineers, and anyone interested in learning about MLOps. Stay tuned for future videos on monitoring model performance with MLflow, retraining models with Airflow, and exploring other deployment options like Gradio, Hugging Face Spaces, and Flask.
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