ThinamXx/MLOps
The repository contains a list of projects which I will work on while learning and implementing MLOps.
This project helps machine learning engineers manage the entire lifecycle of their machine learning models. It takes raw data, model code, and hyperparameters as input, allowing you to track experiments, manage model versions, deploy models as web services, and monitor their performance. The output is a robust, versioned, and monitored machine learning system ready for production. This is for machine learning engineers, data scientists, and MLOps practitioners.
No commits in the last 6 months.
Use this if you need to standardize and streamline the process of moving machine learning models from development to production, including tracking, deployment, and monitoring.
Not ideal if you are looking for a simple, one-off script to train a model without needing to manage its lifecycle or deploy it systematically.
Stars
80
Forks
29
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Jan 22, 2023
Commits (30d)
0
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