Skip to content


In LineaPy, an artifact refers to any intermediate result from the development process. Most often, an artifact manifests as a variable that stores data in a specific state (e.g., my_num = your_num + 10). In the data science workflow, an artifact can be a model, a chart, a statistic, or a dataframe, or a feature function.

What makes LineaPy special is that it treats an artifact as both code and value. That is, when storing an artifact, LineaPy not only records the state (i.e., value) of the variable but also traces and saves all relevant operations leading to this state — as code. Such a complete development history or lineage then allows LineaPy to fully reproduce the given artifact. Furthermore, it provides the ground to automate data engineering work to bring data science from development to production.

Was this helpful?

Help us improve docs with your feedback!