ZenML

Connecting model training pipelines to deploying models in production is regarded as a difficult milestone on the way to achieving Machine Learning operations maturity for an organization. ZenML rises to the challenge and introduces a novel approach to continuous model deployment that renders a smooth transition from experimentation to production.

Use MLflow Tracking to automatically ensure that you're capturing data, metadata and hyperparameters that contribute to how you are training your models. Use the UI interface to compare experiments, and let ZenML handle the boring setup details.

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.