This section lists basic limits and throttling thresholds in Azure Machine Learning.
Important
Azure Machine Learning doesn't store or process your data outside of the region where you deploy.
Workspaces
Limit
Value
Workspace name
2-32 characters
Experiments
Limit
Value
Name
256 characters
Description
5,000 characters
Number of tags
50
Length of tag key
250 characters
Length of tag value
1000 characters
Artifact location
1024 characters
Runs
Limit
Value
Runs per workspace
10 million
RunId/ParentRunId
256 characters
DataContainerId
261 characters
DisplayName
256 characters
Description
5,000 characters
Number of properties
50
Length of property key
100 characters
Length of property value
1,000 characters
Number of tags
50
Length of tag key
100
Length of tag value
1,000 characters
CancelUri / CompleteUri / DiagnosticsUri
1,000 characters
Error message length
3,000 characters
Warning message length
300 characters
Number of input datasets
200
Number of output datasets
20
Custom environments
Limit
Value
Number of files in Docker build context
100
Total files size in Docker build context
1 MB
Metrics
Limit
Value
Metric names per run
50
Metric rows per metric name
1 million
Columns per metric row
15
Metric column name length
255 characters
Metric column value length
255 characters
Metric rows per batch uploaded
250
Note
If you are hitting the limit of metric names per run because you are formatting variables into the metric name, consider instead to use a row metric where one column is the variable value and the second column is the metric value.
Artifacts
Limit
Value
Number of artifacts per run
10 million
Max length of artifact path
5,000 characters
Models
Limit
Value
Number of models per workspace
5 million model containers/versions (including previously deleted models)