Engines for Experiments and Models
In Cloudera Machine Learning, models, experiments, jobs, and sessions are all created and executed within the context of a project. We've described the different ways in which you can customize a project's engine environment for sessions and jobs in Environmental Variables. However, engines for models and experiments are completely isolated from the rest of the project.
Every time a model or experiment is kicked off, Cloudera Data Science Workbench creates a new isolated Docker image where the model or experiment is executed. This isolation in build and execution makes it possible for Cloudera Machine Learning to keep track of input and output artifacts for every experiment you run. In case of models, versioned builds give you a way to retain build history for models and a reliable way to rollback to an older version of a model if needed.
The following topics describe the engine build process that occurs when you kick off a model or experiment.