ML Deployment

This repo contains various resources related to bringing Machine Learning and Deep Learning projects into production.

Contents of this repo:

1. Deploy ML models using Flask: /Web_API_Flask
2. Using Streamlit to create data-driven web apps: /Streamlit Apps for ML
3. Multiprocessing in Python to improve performance of ML workflow: /Multiprocessing in Python
4. Testing and Debugging in ML: /Testing_and_debugging_in_Machine_Learning.ipynb
5. Benchmark tests: /Benchmarking_XGBoost_with_GPU_and_HummingBird.ipynb

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link to about section