Orchestrate Your Data Science Project with Prefect 2.0

Make Your Data Science Pipeline Resilient Against Failures

Motivation

There are a lot of components of a typical data science pipeline such as loading data, processing data, training a model, and making predictions. As a project grows, the number of components, as well as the dependencies between them, proliferate.

If each component has an independent chance of failing, it increases the likelihood that the entire pipeline fails with each run…