Creates a schedule that regularly starts Amazon SageMaker Processing Jobs to monitor the data captured for an Amazon SageMaker Endoint
Creates a schedule that regularly starts Amazon SageMaker Processing Jobs to monitor the data captured for an Amazon SageMaker Endoint.
sagemaker_create_monitoring_schedule(MonitoringScheduleName, MonitoringScheduleConfig, Tags)
MonitoringScheduleName |
[required] The name of the monitoring schedule. The name must be unique within an AWS Region within an AWS account. |
MonitoringScheduleConfig |
[required] The configuration object that specifies the monitoring schedule and defines the monitoring job. |
Tags |
(Optional) An array of key-value pairs. For more information, see Using Cost Allocation Tags in the AWS Billing and Cost Management User Guide. |
A list with the following syntax:
list( MonitoringScheduleArn = "string" )
svc$create_monitoring_schedule(
MonitoringScheduleName = "string",
MonitoringScheduleConfig = list(
ScheduleConfig = list(
ScheduleExpression = "string"
),
MonitoringJobDefinition = list(
BaselineConfig = list(
BaseliningJobName = "string",
ConstraintsResource = list(
S3Uri = "string"
),
StatisticsResource = list(
S3Uri = "string"
)
),
MonitoringInputs = list(
list(
EndpointInput = list(
EndpointName = "string",
LocalPath = "string",
S3InputMode = "Pipe"|"File",
S3DataDistributionType = "FullyReplicated"|"ShardedByS3Key",
FeaturesAttribute = "string",
InferenceAttribute = "string",
ProbabilityAttribute = "string",
ProbabilityThresholdAttribute = 123.0,
StartTimeOffset = "string",
EndTimeOffset = "string"
)
)
),
MonitoringOutputConfig = list(
MonitoringOutputs = list(
list(
S3Output = list(
S3Uri = "string",
LocalPath = "string",
S3UploadMode = "Continuous"|"EndOfJob"
)
)
),
KmsKeyId = "string"
),
MonitoringResources = list(
ClusterConfig = list(
InstanceCount = 123,
InstanceType = "ml.t3.medium"|"ml.t3.large"|"ml.t3.xlarge"|"ml.t3.2xlarge"|"ml.m4.xlarge"|"ml.m4.2xlarge"|"ml.m4.4xlarge"|"ml.m4.10xlarge"|"ml.m4.16xlarge"|"ml.c4.xlarge"|"ml.c4.2xlarge"|"ml.c4.4xlarge"|"ml.c4.8xlarge"|"ml.p2.xlarge"|"ml.p2.8xlarge"|"ml.p2.16xlarge"|"ml.p3.2xlarge"|"ml.p3.8xlarge"|"ml.p3.16xlarge"|"ml.c5.xlarge"|"ml.c5.2xlarge"|"ml.c5.4xlarge"|"ml.c5.9xlarge"|"ml.c5.18xlarge"|"ml.m5.large"|"ml.m5.xlarge"|"ml.m5.2xlarge"|"ml.m5.4xlarge"|"ml.m5.12xlarge"|"ml.m5.24xlarge"|"ml.r5.large"|"ml.r5.xlarge"|"ml.r5.2xlarge"|"ml.r5.4xlarge"|"ml.r5.8xlarge"|"ml.r5.12xlarge"|"ml.r5.16xlarge"|"ml.r5.24xlarge",
VolumeSizeInGB = 123,
VolumeKmsKeyId = "string"
)
),
MonitoringAppSpecification = list(
ImageUri = "string",
ContainerEntrypoint = list(
"string"
),
ContainerArguments = list(
"string"
),
RecordPreprocessorSourceUri = "string",
PostAnalyticsProcessorSourceUri = "string"
),
StoppingCondition = list(
MaxRuntimeInSeconds = 123
),
Environment = list(
"string"
),
NetworkConfig = list(
EnableInterContainerTrafficEncryption = TRUE|FALSE,
EnableNetworkIsolation = TRUE|FALSE,
VpcConfig = list(
SecurityGroupIds = list(
"string"
),
Subnets = list(
"string"
)
)
),
RoleArn = "string"
),
MonitoringJobDefinitionName = "string",
MonitoringType = "DataQuality"|"ModelQuality"|"ModelBias"|"ModelExplainability"
),
Tags = list(
list(
Key = "string",
Value = "string"
)
)
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