Machine learning deployment
ML in Production - Deployment Series
A multi-part blog series on deploying machine learning models in an automated, reproducible, and auditable manner.
Full Stack Deep Learning
Full Stack Deep Learning helps you bridge the gap from training machine learning models to deploying AI systems in the real world.
Machine Learning: Tests and Production
Best practices for testing ML-based systems.
Getting Machine Learning to Production
Machine learning is hard and there are a lot, a lot of moving pieces.
How to Deploy your ML models as Telegram Bots
In this project, I trained a Model to detect mask on people's face and made it available on both Android and IOS through a Telegram Bot. It's deployed on ...
Python Template for All Projects
A template that gives the batteries required to package code, CI checks, auto build and deploy docs, easy PyPi publishing support and docker files.
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How to add Projects to your Collection
Identify the project you want to add to this collection. You can discover projects on the
page or search for them using the search bar at the top left of any page.
Once you've identified the project, click on the green bookmark symbol to its right.
means that you've already added it to some Collections and
means that you have yet to add it to any Collections.
Select the Collections you want to add the project to and unselect the Collections you want to remove the project from (if the project already existed in one of your collections).
and continue to add more projects and create more Collections.
Don't forget to tag
in your comment, otherwise they may not be notified.
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ML in Production
Share what you've made with ML.