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AWS for Query.Farm

aws.machinelearning.get_ml_model

Example SQL Queries

SELECT * FROM
aws.machinelearning.get_ml_model
WHERE
"ml_model_id" = 'VALUE';

Description

Returns an MLModel that includes detailed metadata, data source information, and the current status of the MLModel.

GetMLModel provides results in normal or verbose format.

Table Definition

Column NameColumn Data Type
ml_model_id Required Input Column

The MLModel ID, which is same as the MLModelId in the request.

VARCHAR
verbose Input Column

Specifies whether the GetMLModel operation should return Recipe.

If true, Recipe is returned.

If false, Recipe is not returned.

BOOLEAN
_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
compute_time

The approximate CPU time in milliseconds that Amazon Machine Learning spent processing the MLModel, normalized and scaled on computation resources. ComputeTime is only available if the MLModel is in the COMPLETED state.

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

The epoch time when Amazon Machine Learning marked the MLModel as COMPLETED or FAILED. FinishedAt is only available when the MLModel is in the COMPLETED or FAILED state.

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
log_uri

A link to the file that contains logs of the CreateMLModel operation.

VARCHAR
message

A description of the most recent details about accessing the MLModel.

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 an e-commerce website?"

  • 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
recipe

The recipe to use when training the MLModel. The Recipe provides detailed information about the observation data to use during training, and manipulations to perform on the observation data during training.

Note: This parameter is provided as part of the verbose format.

VARCHAR
schema

The schema used by all of the data files referenced by the DataSource.

Note: This parameter is provided as part of the verbose format.

VARCHAR
score_threshold

The scoring threshold is used in binary classification MLModel models. It marks the boundary between a positive prediction and a negative prediction.

Output values greater than or equal to the threshold receive a positive result from the MLModel, such as true. Output values less than the threshold receive a negative response from the MLModel, such as false.

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

The epoch time when Amazon Machine Learning marked the MLModel as INPROGRESS. StartedAt isn't available if the MLModel is in the PENDING state.

TIMESTAMP_S
status

The current status of the MLModel. This element can have one of the following values:

  • PENDING - Amazon Machine Learning (Amazon ML) submitted a request to describe a MLModel.

  • INPROGRESS - The request is processing.

  • FAILED - The request did not run to completion. The ML model isn't usable.

  • COMPLETED - The request completed successfully.

  • DELETED - The MLModel is marked as deleted. It isn't usable.

VARCHAR
training_data_source_id

The ID of the training DataSource.

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 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. We strongly recommend that you shuffle your data.

  • sgd.l1RegularizationAmount - The coefficient regularization L1 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in a 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. It 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)