| Column Name | Column Data Type |
|---|
eq Input Column
The equal to operator. The MLModel results will have FilterVariable values that exactly match the value specified with EQ. | VARCHAR |
filter_variable Input Column
Use one of the following variables to filter a list of MLModel: -
CreatedAt - Sets the search criteria to MLModel creation date. -
Status - Sets the search criteria to MLModel status. -
Name - Sets the search criteria to the contents of MLModel Name. -
IAMUser - Sets the search criteria to the user account that invoked the MLModel creation. -
TrainingDataSourceId - Sets the search criteria to the DataSource used to train one or more MLModel. -
RealtimeEndpointStatus - Sets the search criteria to the MLModel real-time endpoint status. -
MLModelType - Sets the search criteria to MLModel type: binary, regression, or multi-class. -
Algorithm - Sets the search criteria to the algorithm that the MLModel uses. -
TrainingDataURI - Sets the search criteria to the data file(s) used in training a MLModel. The URL can identify either a file or an Amazon Simple Storage Service (Amazon S3) bucket or directory. | VARCHAR |
ge Input Column
The greater than or equal to operator. The MLModel results will have FilterVariable values that are greater than or equal to the value specified with GE. | VARCHAR |
gt Input Column
The greater than operator. The MLModel results will have FilterVariable values that are greater than the value specified with GT. | VARCHAR |
le Input Column
The less than or equal to operator. The MLModel results will have FilterVariable values that are less than or equal to the value specified with LE. | VARCHAR |
lt Input Column
The less than operator. The MLModel results will have FilterVariable values that are less than the value specified with LT. | VARCHAR |
ne Input Column
The not equal to operator. The MLModel results will have FilterVariable values not equal to the value specified with NE. | VARCHAR |
prefix Input Column
A string that is found at the beginning of a variable, such as Name or Id. For example, an MLModel could have the Name 2014-09-09-HolidayGiftMailer. To search for this MLModel, select Name for the FilterVariable and any of the following strings for the Prefix: -
2014-09 -
2014-09-09 -
2014-09-09-Holiday | VARCHAR |
sort_order Input Column
A two-value parameter that determines the sequence of the resulting list of MLModel. -
asc - Arranges the list in ascending order (A-Z, 0-9). -
dsc - Arranges the list in descending order (Z-A, 9-0). Results are sorted by FilterVariable. | VARCHAR |
_aws_profile Input Column
The AWS profile defines the AWS identity used. It can be defined via credentials or by assuming a IAM role. | STRUCT(
"type" VARCHAR,
"name" VARCHAR,
"account_id" VARCHAR,
"via_profile_name" VARCHAR,
"assumed_role_arn" VARCHAR,
"organization" STRUCT(
"account_name" VARCHAR,
"id" VARCHAR,
"tags" STRUCT(
"key" VARCHAR,
"value" VARCHAR
)[],
"master_account" STRUCT(
"id" VARCHAR,
"email" VARCHAR
),
"parents" STRUCT(
"type" VARCHAR,
"id" VARCHAR,
"name" VARCHAR,
"tags" STRUCT(
"key" VARCHAR,
"value" VARCHAR
)[]
)[]
)
) |
Show child fields- _aws_profile.account_id
The AWS account id - _aws_profile.assumed_role_arn
The ARN of the assumed role - _aws_profile.name
The unique name of the profile. - _aws_profile.organization
Information about this profile's membership in the AWS organization. Show child fields- _aws_profile.organization.account_name
The name of account speciifed by the organization - _aws_profile.organization.id
The organization id - _aws_profile.organization.master_account
Show child fields- _aws_profile.organization.master_account.email
The organization master account email address - _aws_profile.organization.master_account.id
The organization master account id
- _aws_profile.organization.parents[]
Show child fields- _aws_profile.organization.parents[].id
The id of the parent - _aws_profile.organization.parents[].name
The name of the parent - _aws_profile.organization.parents[].tags[]
Show child fields- _aws_profile.organization.parents[].tags[].key
- _aws_profile.organization.parents[].tags[].value
- _aws_profile.organization.parents[].type
The type of parent can be an organization unit or a root
- _aws_profile.organization.tags[]
Show child fields- _aws_profile.organization.tags[].key
- _aws_profile.organization.tags[].value
- _aws_profile.type
The type of profile, either 'credentials' or 'assumed_role' - _aws_profile.via_profile_name
This IAM role for this profile is assumed by first utilizing another profile with this name to obtain credentials.
|
_aws_region Input Column
The AWS region to use. | VARCHAR |
algorithm
The algorithm used to train the MLModel. The following algorithm is supported: | VARCHAR |
compute_time
Long integer type that is a 64-bit signed number. | BIGINT |
created_at
The time that the MLModel was created. The time is expressed in epoch time. | TIMESTAMP_S |
created_by_iam_user
The AWS user account from which the MLModel was created. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account. | VARCHAR |
endpoint_info
The current endpoint of the MLModel. | STRUCT(
"peak_requests_per_second" BIGINT,
"created_at" TIMESTAMP_S,
"endpoint_url" VARCHAR,
"endpoint_status" VARCHAR
) |
Show child fields- endpoint_info.created_at
The time that the request to create the real-time endpoint for the MLModel was received. The time is expressed in epoch time. - endpoint_info.endpoint_status
The current status of the real-time endpoint for the MLModel. This element can have one of the following values: -
NONE - Endpoint does not exist or was previously deleted. -
READY - Endpoint is ready to be used for real-time predictions. -
UPDATING - Updating/creating the endpoint. - endpoint_info.endpoint_url
The URI that specifies where to send real-time prediction requests for the MLModel. Note: The application must wait until the real-time endpoint is ready before using this URI. - endpoint_info.peak_requests_per_second
The maximum processing rate for the real-time endpoint for MLModel, measured in incoming requests per second.
|
finished_at
A timestamp represented in epoch time. | TIMESTAMP_S |
input_data_location_s3
The location of the data file or directory in Amazon Simple Storage Service (Amazon S3). | VARCHAR |
last_updated_at
The time of the most recent edit to the MLModel. The time is expressed in epoch time. | TIMESTAMP_S |
message
A description of the most recent details about accessing the MLModel. | VARCHAR |
ml_model_id
The ID assigned to the MLModel at creation. | VARCHAR |
ml_model_type
Identifies the MLModel category. The following are the available types: -
REGRESSION - Produces a numeric result. For example, "What price should a house be listed at?" -
BINARY - Produces one of two possible results. For example, "Is this a child-friendly web site?". -
MULTICLASS - Produces one of several possible results. For example, "Is this a HIGH-, LOW-, or MEDIUM-risk trade?". | VARCHAR |
name
A user-supplied name or description of the MLModel. | VARCHAR |
score_threshold
| DOUBLE |
score_threshold_last_updated_at
The time of the most recent edit to the ScoreThreshold. The time is expressed in epoch time. | TIMESTAMP_S |
size_in_bytes
Long integer type that is a 64-bit signed number. | BIGINT |
started_at
A timestamp represented in epoch time. | TIMESTAMP_S |
status
The current status of an MLModel. This element can have one of the following values: -
PENDING - Amazon Machine Learning (Amazon ML) submitted a request to create an MLModel. -
INPROGRESS - The creation process is underway. -
FAILED - The request to create an MLModel didn't run to completion. The model isn't usable. -
COMPLETED - The creation process completed successfully. -
DELETED - The MLModel is marked as deleted. It isn't usable. | VARCHAR |
training_data_source_id
The ID of the training DataSource. The CreateMLModel operation uses the TrainingDataSourceId. | VARCHAR |
training_parameters
A list of the training parameters in the MLModel. The list is implemented as a map of key-value pairs. The following is the current set of training parameters: -
sgd.maxMLModelSizeInBytes - The maximum allowed size of the model. Depending on the input data, the size of the model might affect its performance. The value is an integer that ranges from 100000 to 2147483648. The default value is 33554432. -
sgd.maxPasses - The number of times that the training process traverses the observations to build the MLModel. The value is an integer that ranges from 1 to 10000. The default value is 10. -
sgd.shuffleType - Whether Amazon ML shuffles the training data. Shuffling the data improves a model's ability to find the optimal solution for a variety of data types. The valid values are auto and none. The default value is none. -
sgd.l1RegularizationAmount - The coefficient regularization L1 norm, which controls overfitting the data by penalizing large coefficients. This parameter tends to drive coefficients to zero, resulting in sparse feature set. If you use this parameter, start by specifying a small value, such as 1.0E-08. The value is a double that ranges from 0 to MAX_DOUBLE. The default is to not use L1 normalization. This parameter can't be used when L2 is specified. Use this parameter sparingly. -
sgd.l2RegularizationAmount - The coefficient regularization L2 norm, which controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If you use this parameter, start by specifying a small value, such as 1.0E-08. The value is a double that ranges from 0 to MAX_DOUBLE. The default is to not use L2 normalization. This parameter can't be used when L1 is specified. Use this parameter sparingly. | MAP(VARCHAR, VARCHAR) |