Skyulf
Local-first MLOps for privacy-preserving teams
What is Skyulf? Complete Overview
Skyulf is a self-hosted MLOps platform designed for teams that require complete data sovereignty. It provides a FastAPI workspace to build, train, and monitor machine learning pipelines entirely on your own infrastructure. Unlike cloud-based solutions, Skyulf ensures your data never leaves your servers, making it ideal for privacy-sensitive or regulated environments. The platform is currently in early alpha (v0.0.1) and is a passion project, evolving with community contributions. Target users include healthcare & research teams, government & public sector institutions, and SMEs & startups who need ML tooling without cloud dependencies or expensive SaaS subscriptions.
Skyulf Interface & Screenshots
Skyulf Official screenshot of the tool interface
What Can Skyulf Do? Key Features
Data Ingestion
Load CSV, Excel, Parquet, or SQL data with automatic schema detection. Results are cached for reproducible experiments. REST loaders are planned for future releases.
Feature Canvas
Drag and drop to wire up feature pipelines or write Python recipes directly. Includes transforms for scaling, binning, feature selection, and planned geospatial operations.
Async Training
Kick off grid searches, random sweeps, or halving trials in the background with Celery workers. Supports integration with Optuna for advanced hyperparameter tuning.
LLM Helpers (Planned)
Future feature to connect OpenAI, Anthropic, DeepSeek, or run Ollama locally with built-in guardrails to keep LLM outputs tied to your actual data.
Run Monitoring (Planned)
Upcoming feature for live logs and run tracking with MLflow-compatible exports to integrate with other observability tools.
Self-hosted DevEx
Configure once via `config.py`, run on SQLite by default, and scale to PostgreSQL or Docker as needed. Includes local user management and admin panel.
Best Skyulf Use Cases & Applications
Healthcare & Research
Train models on patient data or research datasets that can't leave your infrastructure due to GDPR or institutional policies. Skyulf ensures compliance by keeping all data processing local.
Government & Public Sector
Municipalities and public institutions can process citizen data on-premises for privacy and compliance. Skyulf provides full audit trails and avoids cloud vendor lock-in.
SMEs & Startups
Small teams can avoid expensive SaaS subscriptions and keep data under their control. Skyulf allows starting with SQLite and scaling to Postgres or Docker as needed.
How to Use Skyulf: Step-by-Step Guide
Load your data by pointing to CSV, Parquet, or SQL sources. The schema is detected automatically, and results are cached for repeat runs.
Build your pipeline using the drag-and-drop Feature Canvas to wire up transforms. Preview stats at each step before saving to the feature store.
Train and monitor your models by launching training runs in the background. Track metrics as they come in, with planned MLflow-compatible exports for deployment.
Skyulf Pros and Cons: Honest Review
Pros
Considerations
Is Skyulf Worth It? FAQ & Reviews
Skyulf is a self-hosted MLOps platform that lets you build, train, and monitor machine learning pipelines entirely on your infrastructure, ensuring data never leaves your servers.
Local-first means complete data sovereignty—no vendor lock-in, no surprise cloud bills, and no data privacy concerns. You control where your models train and where your data lives.
Cloud platforms require uploading data to third-party servers and charge based on usage. Skyulf runs 100% on-premise or in your private cloud with no data leaving your network.
Skyulf is in early alpha (v0.0.1). Core features are functional but actively evolving. Check the roadmap for upcoming features and improvements.
Not yet. Skyulf is in early alpha and not production-ready. Use it for experimentation and learning until stable releases are announced.