Usage Guidelines

This section calls out some important guidelines you should keep in mind when you start deploying models with Cloudera Data Science Workbench.

Model Code
Models in Cloudera Data Science Workbench are designed to run any code that is wrapped into a function.
Model Artifacts
Once you start building larger models, make sure you are storing these model artifacts in HDFS, S3, or any other external storage. Do not use the project filesystem to store large output artifacts.
Resouce Consumption and Scaling
Models should be treated as any other long-running applications that are continuously consuming memory and computing resources. If you are unsure about your resource requirements when you first deploy the model, start with a single replica, monitor its usage, and scale as needed.
Security Considerations
As stated previously, models do not impose any limitations on the code they can run. Additionally, models run with the permissions of the user that creates the model (same as sessions and jobs). Therefore, be conscious of potential data leaks especially when querying underlying data sets to serve predictions.
Deployment Considerations
Cloudera Data Science Workbench does not currently support high availability for models. Additionally, there can only be one active deployment per model at any given time. This means you should plan for model downtime if you want to deploy a new build of the model or re-deploy with more/less replicas.

Parent topic: Models