GaiaFlow MLOps Project Template
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Welcome to GaiaFlow — an MLOps project template designed to streamline Earth Observation machine learning projects with best practices and automation.
What is GaiaFlow?
The name GaiaFlow combines Gaia (the Greek goddess of Earth, symbolizing our planet) with Flow (representing seamless workflows in MLOps). This template provides a comprehensive framework for building, testing, and deploying ML workflows tailored for remote sensing projects.
It integrates essential tools for modern MLOps pipelines:
- Apache Airflow for orchestrating workflows and scheduling ML pipelines.
- MLflow for tracking experiments, managing model versions, and model registry.
- JupyterLab for interactive data exploration and development.
- MinIO for local S3-compatible storage of datasets and artifacts.
- Minikube for local lightweight Kubernetes.
Why use GaiaFlow?
- Standardizes ML project structure and workflows.
- Provides an easy-to-use local MLOps environment.
- Ensures reproducibility and seamless CI/CD pipeline integration.
- Supports scalable, production-ready deployments.
- Built with open-source tools widely used in industry.
Who is this for?
- Data scientists working on Earth Observation projects.
- Teams wanting to implement standardized and repeatable ML workflows.
Quick Links
- Getting Started — Installation and initial setup
- Architecture — System design and local MLOps components
- ML Pipeline — Overview of pipeline tools and usage
- Development Guide — How to develop your ML project within GaiaFlow
- Production Guide — Steps to deploy workflows in production
- Testing Workflows — Validating DAGs from local dev to production
- Project Structure — Explanation of the repository layout
- Troubleshooting — Common issues and solutions
License & Acknowledgments
This template is open source and community-driven.
Acknowledgments:
Created and maintained by BC Dev Team