https://github.com/tuancamtbtx/mlops-architecture

How to build a complete MLOPS system : Development, Training, Serving, Model Registry
https://github.com/tuancamtbtx/mlops-architecture

airflow argocd kubeflow mlflow mlops python

Last synced: 8 months ago
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How to build a complete MLOPS system : Development, Training, Serving, Model Registry

• Host: GitHub
• URL: https://github.com/tuancamtbtx/mlops-architecture
• Owner: tuancamtbtx
• License: mit
• Created: 2024-07-25T08:27:59.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2024-07-31T07:47:28.000Z (about 1 year ago)
• Last Synced: 2025-01-02T20:19:18.505Z (9 months ago)
• Topics: airflow, argocd, kubeflow, mlflow, mlops, python
• Homepage:
• Size: 8.03 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • Contributing: CONTRIBUTING.md
  • License: LICENSE

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README

          
# MLOPS

## What is MLOPS?

Machine learning operations (MLOps) are a set of practices that automate and simplify machine learning (ML) workflows and deployments. Machine learning and artificial intelligence (AI) are core capabilities that you can implement to solve complex real-world problems and deliver value to your customers.

## Build a Full MLOps Solution For Machine Learning System

**MLops System**



    



**MLops Flow**



    



**MLops Principles**



    



## Technologies

- Cloud: AWS, GCP, Azure, ...

- Experiment tracking tools: MLFlow, Weights & Biases, ...

- Workflow orchestration: Prefect, Airflow, Flyte, Kubeflow, Argo, ...

- Monitoring: Evidently, WhyLabs/whylogs, ...

- CI/CD: Github actions, Gitlab CI/CD, ...

- Infrastructure as code (IaC): Terraform, Pulumi, Cloud Formation, ...

## Contributing

The project has a separate contribution file. Please adhere to the steps listed in the separate contributions [file](./CONTRIBUTING.md)

## License

[![Licence](https://img.shields.io/github/license/Ileriayo/markdown-badges?style=for-the-badge)](./LICENSE)

# DEMO WOKRING DEBUG TRACKING