v0.7.0

ZenML release 0.39.0 introduces several big new features:

• The zenml stack recipe CLI commands now support fine-grained handling of individual stack components.
• Artifacts are now automatically visualized in the dashboard.
• Materializers received an overhaul: a new cloudpickle default materializer was added that works for arbitrary objects, and a pycaret materializer that can handle various modeling frameworks in a unified format.

The release also contains many bug fixes and quality-of-life improvements, such as new settings options for the SageMaker and Kubernetes orchestrators.

Individual Stack Component Deployment

In this release, we've enhanced the ZenML stack recipe CLI to support conditional deployment, destruction, and configuration of individual stack components. Users can now quickly deploy and destroy components with options for each flavor, and pass a config file for custom variables. The new output CLI command allows users to retrieve outputs from their recipes. Overall, this update streamlines deploying and managing stack components by providing a more efficient and user-friendly experience.

Artifact Visualization

Artifact visualizations are now automatically extracted by ZenML and embedded in the ZenML dashboard. Visualizations can now be defined by overriding the save_visualizations method of the materializer that handles an artifact. These visualizations are then automatically shown in the dashboard and can also be displayed in Jupyter notebooks using the new visualize post-execution method.

Default Cloudpickle Materializer

ZenML now uses cloudpickle under the hood to save/load artifacts that other materializers cannot handle. This makes it even easier to get started with ZenML since you no longer need to define custom materializers if you just want to experiment with some new data types.