Working with machine learning transforms - AWS Glue
Transform propertiesAdding and editing machine learning transforms Viewing transform detailsTeach transforms using labels

Working with machine learning transforms

You can use AWS Glue to create custom machine learning transforms that can be used to cleanse your data. You can use these transforms when you create a job on the AWS Glue console.

For information about how to create a machine learning transform, see Record matching with AWS Lake Formation FindMatches.

Transform properties

To view an existing machine learning transform, sign in to the AWS Management Console, and open the AWS Glue console at https://console.aws.amazon.com/glue/. In the navigation pane under Data Integration and ETL, choose Data classification tools > Record Matching.

The properties for each transform:

Transform name

The unique name you gave the transform when you created it.

ID

A unique identifier of the transform.

Label count

The number of labels in the labeling file that was provided to help teach the transform.

Status

Indicates whether the transform is Ready or Needs training. To run a machine learning transform successfully in a job, it must be Ready.

Created

The date the transform was created.

Modified

The date the transform was last updated.

Description

The description supplied for the transform, if one was provided.

AWS Glue version

The version of AWS Glue used.

Run ID

The unique name you gave the transform when you created it.

Task type

The type of machine learning transform; for example, Find matching records.

Status

Indicates the status of the task run. Possible statuses include:

  • Starting

  • Running

  • Stopping

  • Stopped

  • Succeeded

  • Failed

  • Timeout

Error

If the status is Failed, an error message is displayed describing the reason for the failure.

Adding and editing machine learning transforms

You can view, delete, set up and teach, or tune a transform on the AWS Glue console. Select the check box next to the transform in the list, choose Action, and then choose the action that you want to take.

Creating a new ML transform

To add a new machine learning transform, choose Create transform. Follow the instructions in the Add job wizard. For more information, see Record matching with AWS Lake Formation FindMatches.

Step 1. Set transform properties.

  1. Enter the name and description (optional).

  2. Optionally, set security configuration. See Using data encryption with machine learning transforms.

  3. Optionally, set Task execution settings. Task execution settings allow you to customize how the task is run. Select the Worker type, number of workers, task timeout (in minutes), the number of retries, and the AWS Glue version.

  4. Optionally, set Tags. Tags are labels that you can assign to an AWS resource. Each tag consists of a key and an optional value. Tags can be used to search and filter your resource or track your AWS costs.

Step 2. Choose table and primary key.

  1. Choose the AWS Glue Catalog database and table.

  2. Choose a primary key from the selected table. The primary key column typically contains a unique identifier for every record in the data source.

Step 3. Select tuning options.

  1. For Recall vs. precision, choose the tuning value to tune the transform to favor recall or precision. By default, Balanced is selected, but you can choose to favor recall or favor precision, or choose Custom and enter a value between 0.0 and 1.0 (inclusive).

  2. For Lower cost vs. accuracy, choose the tuning value to favor lower cost or accuracy, or choose Custom and enter a value between 0.0 and 1.0 (inclusive).

  3. For Match enforcement, choose Force output to match labels if you want to teach the ML transform by forcing the output to match the labels used.

Step 4. Review and create.

  1. Review the options for steps 1 – 3.

  2. Choose Edit for any step that needs to be modified. Choose Create transform to complete the create transform wizard.

Using data encryption with machine learning transforms

When adding a machine learning transform to AWS Glue, you can optionally specify a security configuration that is associated with the data source or data target. If the Amazon S3 bucket used to store the data is encrypted with a security configuration, specify the same security configuration when creating the transform.

You can also choose to use server-side encryption with AWS KMS (SSE-KMS) to encrypt the model and labels to prevent unauthorized persons from inspecting it. If you choose this option, you're prompted to choose the AWS KMS key by name, or you can choose Enter a key ARN. If you choose to enter the ARN for the KMS key, a second field appears where you can enter the KMS key ARN.

Note

Currently, ML transforms that use a custom encryption key aren't supported in the following Regions:

  • Asia Pacific (Osaka) - ap-northeast-3

Viewing transform details

Viewing transform properties

The Transform properties page includes attributes of your transform. It shows you the details about the transform definition, including the following:

  • Transform name shows the name of the transform.

  • Type lists the type of the transform.

  • Status displays whether the transform is ready to be used in a script or job.

  • Force output to match labels displays whether the transform forces the output to match the labels provided by the user.

  • Spark version is related to the AWS Glue version you chose in the Task run properties when adding the transform. AWS Glue 1.0 and Spark 2.4 is recommended for most customers. For more information, see AWS Glue Versions.

History, Estimate quality and Tags tabs

Transform details include the information that you defined when you created the transform. To view the details of a transform, select the transform in the Machine learning transforms list, and review the information on the following tabs:

  • History

  • Estimate quality

  • Tags

History

The History tab shows your transform task run history. Several types of tasks are run to teach a transform. For each task, the run metrics include the following:

  • Run ID is an identifier created by AWS Glue for each run of this task.

  • Task type shows the type of task run.

  • Status shows the success of each task listed with the most recent run at the top.

  • Error shows the details of an error message if the run was not successful.

  • Start time shows the date and time (local time) that the task started.

  • End time shows the date and time (local time) that the task ended.

  • Logs links to the logs written to stdout for this job run.

    The Logs link takes you to Amazon CloudWatch Logs. There you can view the details about the tables that were created in the AWS Glue Data Catalog and any errors that were encountered. You can manage your log retention period on the CloudWatch console. The default log retention is Never Expire. For more information about how to change the retention period, see Change Log Data Retention in CloudWatch Logs in the Amazon CloudWatch Logs User Guide.

  • Label file shows a link to Amazon S3 for a generated labeling file.

Estimate quality

The Estimate quality tab shows the metrics that you use to measure the quality of the transform. Estimates are calculated by comparing the transform match predictions using a subset of your labeled data against the labels you have provided. These estimates are approximate. You can invoke an Estimate quality task run from this tab.

The Estimate quality tab shows the metrics from the last Estimate quality run including the following properties:

  • Area under the Precision-Recall curve is a single number estimating the upper bound of the overall quality of the transform. It is independent of the choice made for the precision-recall parameter. Higher values indicate that you have a more attractive precision-recall tradeoff.

  • Precision estimates how often the transform is correct when it predicts a match.

  • Recall upper limit estimates that for an actual match, how often the transform predicts the match.

  • F1 estimates the transform's accuracy between 0 and 1, where 1 is the best accuracy. For more information, see F1 score in Wikipedia.

  • The Column importance table show the column names and importance score for each column. Column importance helps you understand how columns contribute to your model, by identifying which columns in your records are being used the most to do the matching. This data may prompt you to add to or change your labelset to raise or lower the importance of columns.

    The Importance column provides a numerical score for each column, as a decimal not greater than 1.0.

For information about understanding quality estimates versus true quality, see Quality estimates versus end-to-end (true) quality.

For more information about tuning your transform, see Tuning machine learning transforms in AWS Glue.

Quality estimates versus end-to-end (true) quality

AWS Glue estimates the quality of your transform by presenting the internal machine-learned model with a number of pairs of records that you provided matching labels for but that the model has not seen before. These quality estimates are a function of the quality of the machine-learned model (which is influenced by the number of records that you label to “teach” the transform). The end-to-end, or true recall (which is not automatically calculated by the ML transform) is also influenced by the ML transform filtering mechanism that proposes a wide variety of possible matches to the machine-learned model.

You can tune this filtering method primarily by specifying the Lower Cost-Accuracy tuning value. As the tuning value gets closer to favor Accuracy, the system does a more thorough and expensive search for pairs of records that might be matches. More pairs of records are fed to your machine-learned model, and your ML transform's end-to-end or true recall gets closer to the estimated recall metric. As a result, changes in the end-to-end quality of your matches as a result of changes in the cost/accuracy tradeoff for your matches will typically not be reflected in the quality estimate.

Tags

Tags are labels that you can assign to an AWS resource. Each tag consists of a key and an optional value. Tags can be used to search and filter your resource or track your AWS costs.

Teach transforms using labels

You can teach your ML transform using labels (examples) by choosing Teach transform from the ML transform details page. When you teach your machine learning algorithm by providing examples (called labels), you can choose existing labels to use, or create a labeling file.

The screenshot shows a wizard screen for Teach the transform using labels.

  • Labeling – If you have labels, choose I have labels. If you do not have labels, you can still continue with the next step in generating a labeling file.

  • Generate labeling file – AWS Glue extracts records from your source data and suggest potential matching records. You choose the Amazon S3 bucket to store the generated label file. Choose Generate labeling file to start the process. When done, choose Download labeling file. The downloaded file will have a column for labels where you can fill in the labels.

  • Upload labels from Amazon S3 – Choose the completed labeling file from the Amazon S3 bucket where the label file is stored. Then, choose to either append the labels to your existing labels or to overwrite your existing labels. Choose Upload labeling file from Amazon S3.