Stop Using Notebooks: How to Deploy ML Models with Docker

Description

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_detection_ml 🔗 *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