Artifact management
Managing your data with ZenML.
Artifact management
Artifact Versioning, Caching, and Lineage
Each time a ZenML pipeline runs, the system first checks if there have been any changes in the inputs, outputs, parameters, or configuration of the pipeline steps. Each step in a run gets a new directory in the artifact store:
With ZenML, you can easily trace an artifact back to its origins and understand the exact sequence of executions that led to its creation, such as a trained model. This feature enables you to gain insights into the entire lineage of your artifacts, providing a clear understanding of how your data has been processed and transformed throughout your machine-learning pipelines. With ZenML, you can ensure the reproducibility of your results, and identify potential issues or bottlenecks in your pipelines. This level of transparency and traceability is essential for maintaining the reliability and trustworthiness of machine learning projects, especially when working in a team or across different environments.
By tracking the lineage of artifacts across environments and stacks, ZenML enables ML engineers to reproduce results and understand the exact steps taken to create a model. This is crucial for ensuring the reliability and reproducibility of machine learning models, especially when working in a team or across different environments.
Artifact Management with Materializers
Visualizing Artifacts
Materializers can also generate visualizations for your data. By overriding the save_visualizations()
method in your custom materializer, you can create tailored visualizations for specific artifact types. These visualizations can be viewed in the ZenML dashboard or interactively explored in Jupyter notebooks using the visualize()
method of an artifact.
ZenML automatically saves visualizations for many common data types, allowing you to view them in the ZenML dashboard. This provides an intuitive way to explore and understand the artifacts generated by your ML pipelines, making it easier to identify patterns, trends, and potential issues in your data and models.
By leveraging ZenML's artifact management, caching, lineage tracking, and visualization capabilities, you can gain valuable insights into your models, streamline your experimentation process, and ensure the reproducibility and reliability of your machine-learning workflows.