Most ML tooling assumes your data can live in someone else’s cloud, or that your team wants to assemble a stack of separate tools (orchestrator + tracking + deployment + UI) and spend weeks wiring everything together.
Who Skyulf is for
Skyulf is built for:
- Teams working with sensitive/regulated data
- People who want a local-first workflow (laptop → server → on-prem)
- ML engineers and data scientists who prefer one integrated workflow over a pile of disconnected components
- Anyone iterating quickly on models and wanting workflows that stay visible, repeatable, and easy to review.
What you can do with Skyulf
Skyulf focuses on the end-to-end loop:
- Ingest + explore data
- Feature engineering (visually, as a pipeline)
- Training (including background jobs)
- Deployment (self-hosted inference service)
- Verification with an API testing panel (send JSON, view response/latency)
pipeline → run → deploy → test API
Why “visual pipelines” matter (beyond aesthetics)
A visual pipeline canvas isn’t just a pretty UI; it’s a way to make ML workflows:
- explainable (anyone can see what happens between raw data and model)
- repeatable (less tribal knowledge, fewer hidden scripts)
- reviewable (pipelines become artifacts you can share and iterate on)
What’s next
Skyulf is open source and evolving. Near-term focus areas:
- more example pipelines (tabular, time-series, text/embeddings)
- more models
- better packaging for “one command” self-hosting
- integrations/export paths for teams already using other tools.
If you want to try it, start here:
GitHub repo: https://github.com/flyingriverhorse/Skyulf
Website/docs: https://www.skyulf.com/
If you only want the Python engine (no UI), for example, to integrate Skyulf into your own application or scripts, you can install skyulf-core directly via pip:
pip install skyulf-core
If you run it and have feedback, open an issue, especially around onboarding and docs clarity.
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