Model governance using Apache Atlas

To address governance challenges, Cloudera Machile Learning uses Apache Atlas to automatically collect and visualize lineage information for data used in Machine Learning (ML) workflows — from training data to model deployments.

Lineage is a visual representation of the project. The lineage information includes visualization of the relationships between model entities such as code, model builds, deployments, and so on. and the processes that carry out transformations on the data involved, such as create project, build model, deploy model, and so on.

The Apache Atlas type system has pre-built governance features that can be used to define ML metadata objects. A type in Atlas is a definition of the metadata stored to describe a data asset or other object or process in an environment. For ML governance, Cloudera has designed new Atlas types that capture ML entities and processes as Atlas metadata objects.

In addition to the definition of the types, Atlas also captures the relationship between the entities and processes to visualize the end-to-end lineage flow, as shown in the following image. The blue hexagons represent an entity (also called the noun) and the green hexagons represent a process (also called the verb).

The ML metadata definition closely follows the actual machine learning workflow. Training data sets are the starting point for a model lineage flow. These data sets can be tables from a data warehouse or an embedded csv file. Once a data set has been identified, the lineage follows into training, building and deploying the model.

See ML operations entities created in Atlas for a list of

See ML operations entities created in Atlas for a list of metadata that Atlas collects from each CML workspace. Metadata is collected from machine learning projects, model builds, and model deployments, and the processes that create these entities.