A Modern MLOps Platform for Humanitarian Action
This interactive blueprint outlines a open-source MLOps platform designed to bring scalable machine learning to the humanitarian sector, enabling timely and data-driven insights for disaster response and development.
Transparency
Ensure every prediction is traceable to its source code, data, and model version for full auditability and accountability.
Reproducibility
Guarantee that any training run or experiment can be perfectly reproduced, fostering collaboration and scientific validation.
Accessibility
Empower non-technical experts to discover, run, and interpret model outputs through a user-friendly interface.
Cost-Effectiveness
Prioritize open-source tools and efficient cloud resource management(swap up and shut down pods) to minimize operational costs for budget-conscious organizations.
The Unified Architecture
The platform is an ecosystem of open-source components orchestrated by Kubernetes. Click on any component in the diagram to learn more about its role.
Select a Component
Click on a component in the diagram to see its detailed description, role, and the justification for its selection in this architecture.
Workflows in Action
Explore the three primary automated workflows that power the platform's lifecycle management for machine learning models.
The Open-Source Technology Stack
This platform is built on a "best-of-breed" stack of powerful, open-source tools. Each component was chosen for its specific strengths, creating a modular and flexible system.
STAC & MLM
The discoverability core, providing a standardized catalog for all models and data.
MLflow
The experimentation engine for tracking, packaging, and registering models.
Argo Workflows
The lightweight orchestrator for running container-based training pipelines.
Seldon Core
The advanced serving gateway for deploying complex inference graphs.
DVC
The version control system for large-scale data, ensuring reproducibility.
MinIO
The high-performance, S3-compatible storage for all artifacts and data.
Kubernetes
The foundational layer providing a scalable and resilient execution environment.
PostgreSQL
The robust database backend for both the STAC catalog and MLflow tracking.
Phased Implementation Roadmap
A tentative path for moving from local proof-of-concept to a fully operational, production-grade platform.
Phase 1: Local PoC
Deploy the core backend services (Postgres, MinIO, MLflow, STAC API) using Docker Compose to validate basic integration and workflows on a local machine.
Phase 2: Model Standardization
Package a reference model (e.g., Building Detector) with a standard MLproject, Dockerfile, and STAC MLM item. Set up Git and DVC repositories.
Phase 3: End-to-End on k3s
Deploy the full stack to a single-instance cloud server with k3s. Implement and test the complete, automated Argo training pipeline and Seldon inference service.
Phase 4: Production on EKS
Provision a production-grade AWS EKS cluster using Terraform. Deploy all application components via Helm charts. Implement observability and autoscaling.
Phase 5: Frontend & Adoption
Develop the user-facing web application for model discovery and interactive inference. Conduct user testing and iterate based on feedback from the humanitarian community.