Stop Using Notebooks: How to Deploy ML Models with Docker

In this video, I walk through an End-to-End Machine Learning project where I take a model from a Jupyter Notebook and deploy it as a production-ready containerized API. 🚀 Project Source Code: https://github.com/aaronwillyOG/trend_dete... 🔗 Connect on LinkedIn: https://www.linkedin.com/in/aaron-willy/ What I Built: A real-time Bitcoin trend detection system using: Data: Live financial data via yfinance Model: XGBoost Classifier (Trained on technical indicators) Backend: FastAPI (High-performance Async API) Deployment: Docker (Containerized for reproducibility) Frontend: Streamlit (Interactive Dashboard) Tech Stack: Python 3.10, Pandas, Scikit-Learn, XGBoost, Docker, FastAPI, Uvicorn. #MachineLearning #MLOps #Docker #Python #Portfolio