Drag-and-drop MLOps automation to train, deploy, and manage ML models in the cloud.
CloudFlowML is an open source, low-code / no-code platform that helps you build end-to-end machine learning workflows visually.
Run your pipelines in any cloud or on your own infrastructure — and keep models updated automatically.
- Drag-and-drop pipeline builder (data → training → deployment → monitoring)
- Connect to AWS, GCP, Azure, or Kubernetes
- Auto-deployment as REST APIs or batch jobs
- Monitor metrics & detect drift
- Automatic retraining on new data or performance drop
- Model and dataset versioning
- Hybrid mode: combine no-code blocks with custom Python code
- Design your ML pipeline visually.
- Connect to your cloud account (BYOC).
- Run training & deployment in the cloud — pay only for your cloud resources.
- Monitor models with built-in dashboards.
You keep control: your data and compute stay in your cloud.
- Hosted CloudFlowML SaaS for teams
- Cost dashboards & usage insights
- Enterprise features (RBAC, SSO)
- Plugin marketplace for custom pipeline components
git clone https://github.com/yourusername/cloudflowml.git
cd cloudflowml
npm install # frontend
pip install -r requirements.txt # backend
npm start & python app.py