Move fast from data science prototype to pipeline
Stop dealing with messy notebooks, broken data pipelines, irreproducible artifacts, and unknown dependencies.
Create Data Pipelines Fast
Translate messy notebook code into data pipelines. With LineaPy, automatically clean up and refactor data science code so it can be run in an orchestration system. Eliminate bugs or irrelevant code, and accelerate time to value. Execute locally on your server or deploy to a shared compute environment.
Create Reusable Components
Create vetted, reusable components that other data practitioners can discover and incorporate into their workflows like building blocks. Start serving the team instead of acting as ad hoc support.
Trace back the code that generates your results. Prioritize high demand pipelines, see dependencies, and deprecate unused pipelines while alerting downstream consumers. Get to the root of missing values, odd numbers, or unintelligible variable names. Use automatically captured lineage for more robust data engineering support.
See it in Action
Explore a Tutorial
Whether you’re a data engineer, data scientist, or ML engineer, get started right away with LineaPy.
Reproduce and Share Data Science Artifacts
Revisit previous data science work and save data science artifacts into reproducible LineaPy artifacts.