ML Deployment

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

Contents of this repo:

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

Name		Name	Last commit message	Last commit date
Latest commit History 70 Commits
.vscode		.vscode
Docker for Data Scientists		Docker for Data Scientists
Learn MLflow		Learn MLflow
Learn Neo4j/Learn Cypher		Learn Neo4j/Learn Cypher
Multiprocessing in Python		Multiprocessing in Python
OOP_projects		OOP_projects
Poetry - Getting Started		Poetry - Getting Started
Streamlit Apps for ML		Streamlit Apps for ML
Using a debugger		Using a debugger
Web_API_Flask		Web_API_Flask
_posts		_posts
.gitignore		.gitignore
404.html		404.html
About.md		About.md
Benchmarking_XGBoost_with_GPU_and_HummingBird.ipynb		Benchmarking_XGBoost_with_GPU_and_HummingBird.ipynb
Gemfile		Gemfile
Gemfile.lock		Gemfile.lock
ML_Debugging_is_Hard.ipynb		ML_Debugging_is_Hard.ipynb
README copy.md		README copy.md
README.md		README.md
Testing_and_debugging_in_Machine_Learning.ipynb		Testing_and_debugging_in_Machine_Learning.ipynb
_config.yml		_config.yml
about.markdown		about.markdown
index.markdown		index.markdown