jomariya23156/full-stack-on-prem-cv-mlops

"1 config, 1 command from Jupyter Notebook to serve Millions of users", Full-stack On-Premises MLOps system for Computer Vision from Data versioning to Model monitoring and drift detection.

36
/ 100
Emerging

This system provides a complete environment for developing and deploying computer vision models, specifically for image classification. It takes your image datasets and model configurations to produce a production-ready model served to millions of users. It is designed for machine learning engineers and data scientists who manage the full lifecycle of computer vision projects.

No commits in the last 6 months.

Use this if you need an all-in-one, on-premises solution to manage, train, and deploy computer vision models at scale, from initial data versioning to continuous monitoring.

Not ideal if you primarily work with cloud-based MLOps platforms or are not comfortable managing your own infrastructure via Docker.

image-classification model-deployment mlops-infrastructure data-versioning model-monitoring
Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 8 / 25
Maturity 16 / 25
Community 12 / 25

How are scores calculated?

Stars

54

Forks

7

Language

Jupyter Notebook

License

MIT

Last pushed

Aug 12, 2024

Commits (30d)

0

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