prabhuomkar/bitbeast
Experiments with Model Training, Deployment & Monitoring
If you're an ML engineer or data scientist looking to streamline how machine learning models are built, deployed, and monitored, this collection provides practical code examples and tools. It helps you manage model artifacts, serve models efficiently for predictions, and evaluate their performance. You'd use this to improve your model's journey from development to production.
No commits in the last 6 months.
Use this if you need to experiment with different ways to serve your machine learning models or want to set up robust model deployment and monitoring pipelines.
Not ideal if you're a business user looking for a no-code solution to apply existing ML models without any technical setup.
Stars
40
Forks
4
Language
Python
License
—
Category
Last pushed
Aug 10, 2025
Commits (30d)
0
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