jerife/MLOps-on-kubernetes

MLOps Implementing "Brain Computer Interface" on Kubernetes

31
/ 100
Emerging

This project helps MLOps engineers systematically build, operate, and monitor machine learning projects on Kubernetes. It takes raw brainwave data (EEG signals) and outputs a trained model that can classify motor imagery, specifically for Brain Computer Interface applications. The MLOps engineer manages data pipelines, model training, hyperparameter tuning, model versioning, and deployment using a robust and scalable infrastructure.

No commits in the last 6 months.

Use this if you are an MLOps engineer building a robust and scalable machine learning pipeline on Kubernetes for Brain Computer Interface (BCI) applications, needing to manage data, train models, track experiments, and deploy models efficiently.

Not ideal if you are an individual researcher or data scientist looking for a simple, local tool for quick model prototyping without needing enterprise-grade MLOps infrastructure.

Brain Computer Interface EEG signal processing MLOps infrastructure Machine Learning deployment Bioinformatics
Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 6 / 25
Maturity 16 / 25
Community 9 / 25

How are scores calculated?

Stars

16

Forks

2

Language

Python

License

Apache-2.0

Last pushed

Sep 30, 2022

Commits (30d)

0

Get this data via API

curl "https://pt-edge.onrender.com/api/v1/quality/mlops/jerife/MLOps-on-kubernetes"

Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.