ZenML

A dive into Python type hinting, how implementing them makes your codebase more robust, and some suggestions on how you might approach adding them into a large legacy codebase.

ZenML recently added an integration with Evidently, an open-source tool that allows you to monitor your data for drift (among other things). This post showcases the integration alongside some of the other parts of Evidently that we like.

All the advantages that ZenML will bring you if you choose to use it to productionize your model development workflows.

Release notes for the new version of ZenML.

We launched a podcast to have conversations with people working to productionize their machine learning models and to learn from their experience.

Why data scientists need to own their ML workflows in production.

An overview of some of the capabilities that ZenML will unlock for our users.

A set of guiding principles to help you better productionize your machine learning models.

Pipelines help you think and act better when it comes to how you execute your machine learning training workflows.