The model deployment process creates a set of workflow logs. Optionally, you can also configure the Logging service to capture access and predict logs.

In the following code snippets, the variable deployment is a ModelDeployment object. This object can be obtained from a call to .deploy() or .get_model_deployment().


The .show_logs() and .logs() methods in the ModelDeployment class exposes the predict and access logs. The parameter log_type accepts predict and access to specify which logs to return. When it’s not specified, the access logs are returned. The parameters time_start and time_end restrict the logs to time periods between those entries. The limit parameter limits the number of log entries that are returned.

Logs are not collected in real-time. Therefore, it is possible that logs have been emitted by the model deployment but are not currently available with the .logs() and .show_logs() methods.


This method returns a list of dictionaries where each element of the list is a log entry. Each element of the dictionary is a key-value pair from the log.

deployment.logs(log_type="access", limit=10)


This method returns a dataframe where each row represents a log entry.

deployment.show_logs(log_type="access", limit=10)


The .list_workflow_logs() provides a list of dictionaries that define the steps that were used to deploy the model. These are referred to as the workflow logs.

   "message": "Creating compute resource configuration.",
   "timestamp": "2021-04-21T20:45:27.609000+00:00"
   "message": "Creating compute resources.",
   "timestamp": "2021-04-21T20:45:30.237000+00:00"
   "message": "Creating load balancer.",
   "timestamp": "2021-04-21T20:45:33.076000+00:00"
   "message": "Compute resources are provisioned.",
   "timestamp": "2021-04-21T20:46:46.876000+00:00"
   "message": "Load balancer is provisioned.",
   "timestamp": "2021-04-21T20:53:54.764000+00:00"