Use Cases
Relational Deep Learning
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Explore real-world workflows you can ship today—powered by xorq’s flexible, engine-agnostic design.
30+ ML integrations
"The Xorq framework greatly simplified our ML pipelines, improving performance 10x and reducing the compute and storage resources required to run them."
Daniel Ashy
Chief Technology Officer, Yendo
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Install Xorq
Spin up your first Xorq engine in minutes—locally or in the cloud.
Install with pip
pip install xorq
Or use nix instead
nix run github:xorq-labs/xorq
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Not sure where to start? We’ll build your first xorq pipeline—free. Tailored just to your stack, your use case, and your goals.
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Start with a pre-built pipeline tailored to real-world ML tasks—modify it, run it, and make it yours in minutes.
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30+ ML integrations
More ways to use xorq
Users often build custom machinery for their common ML inference tasks e.g. online inference and batch scoring. To address the batch scoring use-case, we are previewing a new built-in UDF for XGBoost models in that can be used to score data row-wise via a DataFrame API. Moreoever, with ’s multi-engine support, data practitioners do not have to leave their DataFrame API to connect to SQL sources from a familiar high-level, pandas-like, DSL.
As Python packaging tools evolve, uv has emerged as a promising alternative to Poetry, offering faster dependency resolution and simplified environment management. This guide walks through migrating a Python project from Poetry to uv, including handling maturin build backend, pre-commit hooks, and CI/CD configuration.
From dev to prod,
without any rewrites.
Better ML pipelines.
Launched anywhere.
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