This repo contains various resources related to bringing Machine Learning and Deep Learning projects into production.
- Deploy ML models using Flask:
/Web_API_Flask
- Using Streamlit to create data-driven web apps:
/Streamlit Apps for ML
- Multiprocessing in Python to improve performance of ML workflow:
/Multiprocessing in Python
- Testing and Debugging in ML:
/Testing_and_debugging_in_Machine_Learning.ipynb
- Benchmark tests:
/Benchmarking_XGBoost_with_GPU_and_HummingBird.ipynb