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Meet LineaPy

Supercharge your data science workflow with LineaPy.

Just two lines of code captures, analyzes, and transforms messy data science development code to extract production data pipelines in minutes. No refactoring or new tools needed.

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Automatically capture data science development.

By simply importing LineaPy into your Python development environment, you can automatically capture the entire development history without manually adding logging statements.

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Easily transform development code to production.

LineaPy captures development lineage and code refactoring, which is automatically transformed to production code, freeing data scientists from tedious labor to focus on developing more experiments, analysis, and models.

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Move fast from prototype to pipeline.

Rapidly create analytics pipelines with a simple API – no refactoring or new tools needed. Go from a Juypter notebook to an Airflow pipeline in minutes.

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Modern Data Scientists Love LineaPy

LineaPy helps me extract data pipelines from messy notebooks. I have built a “home-grown” solution for this, but LineaPy makes it 100 times better.

Mike Arov | ML Lead, Postclick

LineaPy is the shortest path from prototype to production for data science. It cleans up my code and instantly turns it into something I can hand off to a collaborator, or schedule with my favorite orchestration tool.

Sean Taylor | Data Scientist, Formerly Lyft, Meta

A tool like LineaPy can free Data Engineers like me from answering ad hoc requests from Data Scientists. So I can better focus on things that are truly engineering. With a tool like LineaPy, my Data Scientist colleagues can help themselves deploy data pipelines. It also gives me a central point of management and visibility.

Bing Wu | Data Engineer

LineaPy makes it easier to go from test coding to running scripted experiments. It's super useful as a tool for double checking functions and saving workflow to revisit later. It’s great for users who tend to be messy and focused on experimentation with functions and algorithms rather than keeping track of code.

Eun Seo Jo | Research Engineer, Hugging Face

How to explore and process raw data to save an artifact:
Broke: long and messy .ipynb file that does it all.
Woke: refactor to drop unused code, version data and artifact, schedule a workflow.
Bespoke: have LineaPy do it for you.

Sergey Karayev | Co-founder, Volition & Co-creator, Full Stack Deep Learning

Get Started In Minutes

Take your data science artifact from development to production without friction.

Quickstart

import lineapy

lineapy.save(your_artifact)