Monthly Archives: June 2022

Today, customers can raise support tickets through multiple channels like – web, mobile, chat-bots, emails, or phone calls. When a support ticket is raised by a customer, it is processed and assigned to a category based on the information provided in the ticket. It is then routed to the support group for resolution according to the category of the ticket. It is estimated that a high number of support tickets are usually not routed to the right group due to incorrect ticket categorization. Incorrectly assigned tickets cause delay in overall resolution time, often resulting in severe customer dissatisfaction. It may also have other widespread impacts like financial, operational, or other business repercussions. Hence, ticket classification is an essential task for every organization these days. Although you may classify tickets manually, but it is prone to error, not cost-effective, and does not scale.

AWS Managed Services (AMS) uses Amazon Comprehend custom classifications to categorize inbound requests by resource and operation type based on how the customer described their issue. Amazon Comprehend is a natural language processing (NLP) service that uses machine learning (ML) to uncover valuable insights and connections in text. AMS utilizes custom classifiers to label customer requests with appropriate issue types, resource type, and resource action, thereby routing customer tickets to the SMEs. Amazon Comprehend classification is utilized to find opportunities for new internal automation tools that AMS engineers can use to fulfill customer requirements to reduce manual effort and chances of manual errors. The classification data is stored in an Amazon Redshift cluster and used to analyze customer requests and find new automation tool candidates. This automation results in increased operational efficiency and reduced cost.

In this post, we show how managed service providers can use Amazon Comprehend to classify and route the tickets, provide suggestions based on the classification, and utilize the classification data.

Solution overview

The following diagram shows the solution architecture.

The workflow is as follows:

1. A customer submits the ticket.
2. The ticket system receives the ticket from the customer, and invokes the ticket classifier AWS Lambda function with the ticket details. Lambda is a serverless, event-driven compute service that lets you run code for virtually any type of application or backend service without provisioning or managing servers. Lambda is chosen for the solution to reduce cost and maintenance effort.
3. The ticket classifier Lambda function classifies the ticket with Amazon Comprehend using the ticket title and description. With Amazon Comprehend, you can train the NLP model and provide both batch and real-time classifiers without provisioning and maintaining infrastructure.
4. The ticket classifier Lambda function pushes the ticket classification data to the Amazon Redshift cluster via Amazon Kinesis Data Firehose. Kinesis Data Firehose is an extract, transform, and load (ETL) service that captures, transforms, and delivers streaming data to data lakes, data stores, and analytics services. Amazon Redshift uses SQL to analyze structured and semi-structured data across data warehouses, operational databases, and data lakes, using AWS-designed hardware and ML to deliver the best price performance at any scale. Kinesis Data Firehose delivers data to an Amazon Simple Storage Service (Amazon S3) bucket first and then issues an Amazon Redshift COPY command to load the data into an Amazon Redshift cluster.
5. The ticket classifier Lambda function invokes the ticket handler Lambda function.
6. The ticket handler Lambda function runs code to help the ticket handling. In this example, it returns the recommended materials for handling the ticket based on the classification.
7. Ticket analysis can be done with Amazon QuickSight. From ticket analysis, you can find out the top requested ticket type. Based on the analysis, you can discover ticket trends and opportunities to automate top ticket types. QuickSight is a cloud-scale business intelligence (BI) service that you can use to deliver easy-to-understand insights to the people who you work with, wherever they are.

In the following sections, we walk you through the steps to implement the solution, integrate the ticket classification infrastructure with your ticketing system, and use the classification data with QuickSight.

Implement the solution

In this section, we walk through the steps to provision your solution resources and create the necessary infrastructure.

Configure Amazon Comprehend

In this step, we train two new Amazon Comprehend custom classification models: Operation and Resource, and create a real-time analysis endpoint for each model.

Upload the training data

To upload the training data, complete the following steps:

1. Download ticket_training_data.zip and unzip the file.
   This folder contains the following two files:
   • training_data_operations.csv – This file is a two-column CSV file that we use to train the Operation classification model. The first column contains class, and the second column contains document.
   • training_data_resources.csv – This file is a two-column CSV file that we use to train the Resource classification model. Like the training_data_operations.csv file, the first column contains class, and the second column contains document.
2. On the Amazon S3 console, create a new bucket for Amazon Comprehend. Because S3 bucket names are globally unique, you need to create a unique name for the bucket. For this post, we call it comprehend-ticket-training-data. Enable server-side encryption and block public access when creating the bucket.
3. Upload training_data_operations.csv and training_data_resources.csv to the new S3 bucket.

Create two new models

To create your models, complete the following steps:

1. On the Amazon Comprehend console, choose Custom classification in the navigation pane.
2. Choose Create new model.
3. Provide the following information:
   1. For Model name, enter ticket-classification-operation.
   2. For Language, choose English.
   3. For Classifier mode, select Using Single-label mode.
   4. For Data format, select CSV file.
   5. For Training dataset, enter the S3 path for training_data_operations.csv.
   6. For Test data source, select Autosplit.
      Autosplit automatically selects 10% of your provided training data to use as testing data.
   7. For IAM Role, select Create an IAM role.
   8. For Permissions to access, choose the training, test, and output data (if specified) in your S3 buckets.
   9. For Name suffix, enter ticket-classification.
4. Choose Create.
   
5. Choose Create new model again to create your resource classification model.
6. Provide the following information:
   1. For Model name, enter ticket-classification-resource.
   2. For Language, choose English.
   3. For Classifier mode, select Using Single-label mode.
   4. For Data format, select CSV file.
   5. For Training dataset, enter the S3 path for training_data_resources.csv.
   6. For Test data source, select Autosplit.
   7. For IAM Role, select Use an existing IAM role.
   8. For Role name, choose AmazonComprehendServiceRole-ticket-classification.
7. Choose Create.
   

Amazon Comprehend is now processing the CSV files and using them to train custom classifiers. We then use these to help classify customer tickets. The larger and more accurate our training data is, the more accurate the classifier will be.

Wait for the version status to show as Trained as below. It may take up to 1 hour to complete, depending on the size of the training data.

Create Amazon Comprehend endpoints

Amazon Comprehend endpoints are billed in 1-second increments, with a minimum of 60 seconds. Charges continue to incur from the time you start the endpoint until it’s deleted, even if no documents are analyzed. For more information, see Amazon Comprehend Pricing. To create your endpoints, complete the following steps:

1. On the Amazon Comprehend console, choose Endpoints in the navigation pane.
2. Choose Create endpoint to create your operation classification endpoint.
3. Provide the following information:
   1. For Endpoint name, enter ticket-classification-operation.
   2. For Custom model type, select Custom classification.
   3. For Classifier model, choose ticket-classification-operation.
   4. For Version, choose No Version Name.
   5. For Number of inference units (IUs), enter 1.
4. Choose Create endpoint.
   
5. Choose Create endpoint again to create the resource classification endpoint.
6. Provide the following information:
   1. For Endpoint name, enter ticket-classification-resource.
   2. For Custom model type, select Custom classification.
   3. For Classifier model, choose ticket-classification-resource.
   4. For Version, choose No Version Name.
   5. For Number of inference units (IUs), enter 1.
7. Choose Create endpoint.
   

After you create both endpoints, wait until the status for both shows as Active.

Test the Amazon Comprehend endpoints with real-time analysis

To test your endpoints, complete the following steps:

1. On the Amazon Comprehend console, choose Real-time analysis in the navigation pane.
2. For Analysis type¸ select Custom.
3. For Endpoint¸ choose ticket-classification-operation.
4. For Input text, enter the following:

   Hi support,
   Please update the timezone to UTC on i-abcd1234.
   Thanks.

5. Choose Analyze.
   The results show that the Update class has the highest confidence score.
6. Change Endpoint to ticket-classification-resource and choose Analyze again.

The results show that the EC2 class has the highest confidence score.

Create a secret for the Amazon Redshift cluster password

In this step, we create an AWS Secrets Manager secret for your Amazon Redshift cluster password. Secrets Manager helps you protect secrets needed to access your applications, services, and IT resources. The service enables you to easily rotate, manage, and retrieve database credentials, API keys, and other secrets throughout their lifecycle. In this post, we store the Amazon Redshift cluster password in a Secrets Manager secret.

1. On the Secrets Manager console, choose Secrets in the navigation pane.
2. Choose Store a new secret.
3. For Secret type, select Other type of secret.
   
4. Under Key/value pairs, set your key as password and value as your Amazon Redshift cluster password.
   The password must be between 8–64 characters in length and contain at least one uppercase letter, one lowercase letter, and one number. It can be any printable ASCII character except ‘ (single quote), “ (double quote), , /, @, or space.
5. Choose Next.
   
6. For Secret name, enter ClassificationRedshiftClusterPassword.
7. Choose Next.
8. In the Secret rotation section, choose Next.
9. Review your secret configuration and choose Store.

Provision your infrastructure with AWS CloudFormation

In this step, we provision the infrastructure for the solution using an AWS CloudFormation stack.

Upload the Lambda function code

Before launching the CloudFormation stack, upload your Lambda function code:

1. Download lambda_code.zip
2. On the Amazon S3 console, open the bucket that you created.
3. Upload lambda_code.zip.

Create your CloudFormation stack

To provision resources with AWS CloudFormation, complete the following steps:

1. Download cloudformation_template.json.
2. On the AWS CloudFormation console, choose Create stack.
3. Select With new resources (standard).
4. For Template source, choose Upload a template file.
5. Choose the downloaded CloudFormation template.
6. Choose Next.
7. For Stack name, enter Ticket-Classification-Infrastructure.
8. In the Parameters section, enter the following values:
   1. For ClassificationRedshiftClusterNodeType, enter the Amazon Redshift cluster node type. dc2.large is the default.
   2. For ClassificationRedshiftClusterPasswordSecretName, enter the Secrets Manager secret name that stores the Amazon Redshift cluster password.
   3. For ClassificationRedshiftClusterSubnetId, enter the subnet ID where the Amazon Redshift Cluster is hosted. The subnet must be within the VPC which you mentioned in the ClassificationRedshiftClusterVpcId parameter.
   4. For ClassificationRedshiftClusterUsername, enter the Amazon Redshift cluster user name.
   5. For ClassificationRedshiftClusterVpcId, enter the VPC ID where the Amazon Redshift cluster is hosted.
   6. For LambdaCodeS3Bucket, enter the S3 bucket name where you uploaded the Lambda code.
   7. For LambdaCodeS3Key, enter the Amazon S3 key of the deployment package.
   8. For QuickSightRegion, enter the Region for QuickSight. The Region for QuickSight should be consistent with the Region you’re using for Amazon Comprehend and the S3 bucket.
9. Choose Next.
10. In the Configure stack options section, choose Next.
11. In the Review section, select I acknowledge that AWS CloudFormation might create IAM resources.
12. Choose Create stack.

Configure your Amazon Redshift cluster

In this step, you enable audit logging and add the new table to the Amazon Redshift cluster created through the CloudFormation template.

Audit logging is not turned on by default in Amazon Redshift. When you turn on logging on your cluster, Amazon Redshift exports logs to Amazon CloudWatch, which capture data from the time audit logging is enabled to the present time. Each logging update is a continuation of the previous logs.

Enable audit logging

You can skip this step if you don’t need audit logging for your Amazon Redshift cluster.

1. On the Amazon Redshift console, choose Clusters in the navigation pane.
2. Choose the Amazon Redshift cluster starting with classificationredshiftcluster-.
3. On the Properties tab, choose Edit.
4. Choose Edit audit logging.
5. For Configure audit logging¸ choose Turn on.
6. For Log expert type, choose CloudWatch.
7. Select all log types.
8. Choose Save changes.

Create new table

To create a new table, complete the following steps:

1. On the Amazon Redshift console, choose Query data.
2. Choose Query in query editor v2.
3. On the Database page, choose your cluster.
   
4. For Database, enter ticketclassification.
5. Enter the user name and password you configured in the CloudFormation stack parameters.
6. Choose Create connection.
   
7. When the connection is made, choose the plus sign and open a new query window.
8. Enter the following query:

   CREATE TABLE tickets(
     id             VARCHAR(50)   NOT NULL,
     title          VARCHAR(1000) NOT NULL,
     description    VARCHAR(5000) NOT NULL,
     creation_time  TIMESTAMPTZ   NOT NULL,
     operation      VARCHAR(5000) NULL,
     resource       VARCHAR(5000) NULL
   );

9. Choose Run.

Test the classification infrastructure

Now the infrastructure for ticket classification is ready. Before integrating with your ticket system, let’s test the classification infrastructure.

Run the test

To run the test, complete the following steps:

1. On the Lambda console, choose Functions in the navigation pane.
2. Choose the function that starts with Ticket-Classification-Inf-TicketClassifier.
3. On the Test tab, choose Test event.
4. For Name, enter TestTicket.
5. Enter the following test data:

   {
     "TicketId": "00000001",
     "TicketTitle": "Update the timezone",
     "TicketDescription": "Hi support, Please update the timezone to UTC on i-abcd1234. Thanks.",
     "TicketCreationTime": "2020-12-04 03:09:00-08"
   }

6. Choose Test.
   

The ticket is classified, and the classification data is stored in the Amazon Redshift cluster. After the classification, the ticket handler Lambda function runs, which handles the ticket based on the classification, including recommending materials to support engineers.

Check the ticket classifier test log

To check the test log, complete the following steps:

1. In the result section of the test, choose Logs, or choose View logs in CloudWatch on the Monitor tab.
2. Choose the log stream.

You can view the logs in the following screenshot, which shows the output from Amazon Comprehend and the final top classification of the ticket. In this example, the test ticket is classified as Resource=EC2, Operation=Update.

Check the ticket classification output in the Amazon Redshift cluster

To validate the output in your cluster, complete the following steps:

1. On the Amazon Redshift query editor v2 console, choose the plus sign to open a new query window.
2. Enter the following query:

   SELECT * FROM "tickets";

3. Choose Run.

The following screenshot shows the ticket classification. If it’s not available yet, wait for a few minutes and retry (Kinesis Data Firehose needs some time to push the data). We can now use this data in QuickSight.

Check the ticket handler test log

After the ticket classifier pushes the classification data in the Amazon Redshift cluster, the ticket handler Lambda function runs, which handles the ticket based on the classification, including recommending materials to support engineers. In this example, the ticket handler returns recommended materials including the runbook, AWS documentation, and SSM documents so support can refer to them when handling the ticket. You can integrate the output with your ticket handling system, and you can customize the handling processes in the Lambda function code. In this step, we check what recommendations were made.

1. On the Lambda console, choose Functions in the navigation pane.
2. Choose the Lambda function that starts with Ticket-Classification-Inf-TicketHandlerLambdaFunct.
3. On the Monitor tab, choose View logs in CloudWatch.
4. Choose the log stream.

The following screenshot shows the logs. You can see the output from Amazon Comprehend and the list of recommended AWS documents and SSM documents for the ticket classified as Update EC2. You can add your own runbooks, documents, SSM documents, or any other materials in the Lambda function code.

Integrate the ticket classification infrastructure with your ticketing system

In this section, we walk through the steps to integrate your ticketing classification infrastructure with your ticketing system and customize your configuration.

Most ticketing systems have a trigger feature, which allows you to run code when the ticket is submitted. Set up your ticketing system to invoke the ticket classifier Lambda function with the following formatted input:

{
  "TicketId": "{{ Ticket ID }}",
  "TicketTitle": "{{ Ticket Title }}",
  "TicketDescription": "{{ Ticket Description }}",
  "TicketCreationTime": "{{ Ticket Creation Time. e.g. 2020-12-04 03:09:00-08 }}"
}

If you want to customize the input, modify the ticket classifier Lambda function code. You need to add or remove parameters (lines 90–105) and customize the input for Amazon Comprehend (lines 15–17).

You can customize the ticket handler Lambda function to run automation or edit the recommendations. For example, you can add the internal comment to the ticket with the recommendations. To customize, open the ticket handler Lambda code, and edit lines 68–70 and 75–81.

Use classification data with QuickSight

After you integrate the ticket classification infrastructure with your ticket system, the ticket classification data is stored in the Amazon Redshift cluster. You can use QuickSight to check this data and generate reports. In this example, we generate a QuickSight analysis with the classification data.

Sign up for QuickSight

If you don’t already have QuickSight, sign up with the following steps:

1. On the QuickSight console, choose Sign up for QuickSight.
2. Choose Standard.
3. Under QuickSight region, choose the Region you configured in the CloudFormation parameter QuickSightRegion.
4. Under Account info, enter your QuickSight account name and notification email address.
5. Under QuickSight access to AWS services, select Amazon Redshift.
6. If you want to allow access and autodiscovery for other resources, select them as well.
7. Choose Finish.
   
8. Choose Go to Amazon QuickSight after you’re signed up.

Connect your Amazon Redshift cluster to QuickSight

To connect your cluster to QuickSight as a data source, complete the following steps:

1. On the QuickSight console, choose Datasets in the navigation pane.
2. Choose New dataset.
3. Choose Redshift Auto-discovered.
4. Provide the following information:
   1. For Data source name, enter ticketclassification.
   2. For Instance ID, choose the Amazon Redshift cluster starting with classificationredshiftcluster-.
   3. For Connection type, choose Public network.
   4. For Database name, enter ticketclassification.
   5. Enter the Amazon Redshift cluster user name and password you configured in the CloudFormation stack parameters.
5. Choose Validate connection to see if the connection works.
   If it doesn’t work, this is likely due to using the wrong user name and password, or the QuickSight Region is different from what you specified in the CloudFormation stack.
6. Choose Create data source.
   
7. In the Choose your table section, select the tickets table.
8. Choose Select.
   
9. Select Import to SPICE for quicker analytics.
   SPICE is the QuickSight Super-fast, Parallel, In-memory Calculation Engine. It’s engineered to rapidly perform advanced calculations and serve data. Importing (also called ingesting) your data into SPICE can save time and money. For more information on SPICE, refer to Importing Data into SPICE. If you get the error “Not enough SPICE capacity,” purchase more SPICE capacity. For more information, refer to Purchasing SPICE capacity in an AWS Region.
10. Choose Visualize.
    

Create a ticket classification analysis report

Once you finish dataset creation, you can see the new QuickSight analysis. In this section, we walk through the steps to create a ticket classification analysis report, including a pivot table, pie charts, and line charts.

1. Choose AutoGraph.
2. Under Visual types, choose the pivot table.
3. Drag operation from Fields list to Rows.
4. Drag resource from Fields list to Columns.
5. On the Add menu, choose Add visual.
6. Under Visual types, choose the pie chart.
7. Drag operation from Fields list to Group/Color.
8. On the Add menu, choose Add visual again.
9. Under Visual types, choose the pie chart again.
10. Drag resource from Fields list to Group/Color.
11. On the Add menu, choose Add visual again.
12. Under Visual types, choose the line chart.
13. Drag creation_time from Fields list to X axis.
14. Drag operation from Fields list to Color.
15. On the Add menu, choose Add visual again.
16. Under Visual types, choose the line chart again.
17. Drag creation_time from Fields list to X axis.
18. Drag operation from Fields list to Color.
19. Resize and reorder the charts as needed.
20. Choose Save as.
21. Enter a name for your analysis and choose Save.

Congratulations! Your first ticket analysis is ready. Once you have more data, the analysis will look like the following screenshot.

Clean up

In this step, we clean up the resources we created with various services.

Amazon Comprehend

To delete your endpoints, complete the following steps:

1. On the Amazon Comprehend console, choose Endpoints in the navigation pane.
2. Select the endpoint ticket-classification-operation.
3. Choose Delete and follow the prompts.
4. Repeat these steps to delete the ticket-classification-resource endpoint.
   Next, delete the custom classifications you created.
5. Choose Custom classification in the navigation pane.
6. Select the classification ticket-classification-operation.
7. Select No Version Name.
8. Choose Delete and follow the prompts.
9. Repeat these steps to delete the ticket-classification-resource classification.

Amazon S3

Next, clean up the S3 bucket you created.

1. On the Amazon S3 console, select the bucket you created.
2. Delete all the objects in the bucket.
3. Delete the bucket.

Amazon QuickSight

Delete the QuickSight analyses and dataset you created.

1. On the QuickSight console, choose Analyses in the navigation pane.
2. Choose the options icon (three dots) on the analysis you created.
3. Choose Delete and follow the prompts.
4. Choose Datasets in the navigation pane.
5. Choose the tickets dataset.
6. Choose Delete dataset and follow the prompts.

AWS CloudFormation

Clean up the resources you created as part of the CloudFormation stack.

1. On the AWS CloudFormation console, choose Stacks in the navigation pane.
2. Choose the Ticket-Classification-Infrastructure stack.
3. On the Resources tab, choose the physical ID of ClassificationDeliveryStreamS3Bucket.
   The Amazon S3 console opens.
4. Delete any objects in this bucket.
5. Return to the AWS CloudFormation console, choose Delete, and follow the prompts.

AWS Secrets Manager

Lastly, delete the Secrets Manager secret.

1. On the Secrets Manager console, select the secret ClassificationRedshiftClusterPassword.
2. On the Actions menu, choose Delete secret.
3. Set the waiting period as 7 days and choose Schedule Delete.

Your secret will be automatically deleted after 7 days.

Conclusion

In this post, you learned how to utilize AWS services to create an automatic classification and recommendation system. This solution will help your organizations build the following workflow:

1. Classify customer requests.
2. Recommend automated solutions.
3. Analyze customer request classifications and discover top customer requests.
4. Release a new automated solution and increase the automation rate.

For more information about Amazon Comprehend, see Amazon Comprehend Documentation. You can also discover other Amazon Comprehend features and get inspiration from other AWS blog posts about using Amazon Comprehend beyond classification.

About the Authors

Seongyeol Jerry Cho is a Senior Systems Development Engineer at AWS Managed Services based in Sydney, Australia. He focuses on building highly scalable and automated cloud operations software using a variety of technologies, including machine learning. Outside of work, he enjoys travel, camping, reading, cooking, and running.

Manu Sasikumar is a Sr. Systems Engineer Manager with AWS Managed Services. Manu and his team focus on building powerful and easy-to-use automations to reduce manual effort, and build AI and ML-based solutions for managing customer requests. Outside of work, he loves spending his spare time with his family, as well as being part of various humanitarian and volunteer activities.

Read More

This is a guest post by Andrew Degenholtz, CEO and Founder of eMagazines, the parent company of ReadAlong.ai. eMagazines’ technology seamlessly transforms print products into premium digital and audio experiences. Leveraging Amazon technology, ReadAlong.ai offers a simple, turn-key way for publishers to add audio to their websites with a single line of code.

eMagazines supports publishers in bringing high-quality journalism content to readers across digital platforms. Our ReadAlong.ai brand allows our customers to deepen their connection to readers by adding audio to traditional text-first publishing formats. In March 2020, we helped TIME for Kids launch a digital version of its popular magazine for school-aged kids. This premium subscription product helped their users transition to digital when the pandemic forced schools to close and families needed high-quality educational tools to supplement classroom learning materials.

In this post, we share how we created an automated way for TIME for Kids to seamlessly add audio for early readers and pre-readers through ReadAlong.ai, which uses Amazon Polly technology.

Why did TIME for Kids decide to start creating audio narration of their articles?

The addition of audio with auto scrolling and highlighting of text supports pre-readers and those students still learning to read. Listening while reading supports vocabulary development and reading comprehension, and new words are more likely to be learned when both their oral and written forms are provided. A report from the National Center on Early Childhood Development, Teaching, and Learning states that developing brains need to hear language even before learning to talk, and that even infants’ brains are preparing to speak months before they say their first words. Not only that, but the report also revealed that listening to stories read aloud helps expand both the volume and variety of words entering young vocabularies and fields of comprehension. Experts at Scholastic report that being read to also helps early readers “focus on the sounds of words read without interruption and provides a model of fluent reading,” and also noted that resources like audio help children learn how to listen, a prerequisite to learning to read.

What was the business challenge we addressed?

TIME for Kids originally addressed pre-reader accessibility by hiring voice actors to record their stories. The earlier iteration of their audio play button used an HTML audio player without speed variation or the option to scroll the page or highlight the text. The experience was expensive and time-consuming, and the user experience wasn’t as engaging as it could be. TIME for Kids was also unable to see even basic data around play or completion rates.

Why Amazon Polly?

We chose Amazon Polly because its APIs and web services support our goal of automating processes and making things easy for our clients.

Amazon Polly’s neural text-to-speech synthesis does the best job of voicing words within the context of a sentence, and the consistency in speech quality allows for the automation of article rendering.

Additionally, Amazon Polly offers a responsive API and powerful SSML support. This offers support for those cases where more control is needed to change inflection, and in the event that text contains challenging names (people, brands, companies) or word and phrase replacements (reading out abbreviations or acronyms in a particular way).

Amazon Polly also supports speech marks, which are crucial for highlighting the text that is currently being read out.

For TIME for Kids, the Kevin voice was a clear winner. TIME for Kids loved the approachable sound of the Kevin voice—they wanted wanting a voice that sounded like a child’s in order to help establish a sense of connection with young readers. Hear an example of a TIME for Kids article using the Kevin voice.

The technical challenge

TIME for Kids needed an educational audio solution for their website. It needed to be a one-time setup that was highly automated and very low friction. The solution also needed to process new articles as they were added dynamically, on a daily basis. And when a user listens to the audio, the page needed to scroll along with the text and highlight the sentence currently being read out loud.

Part of our challenge was to reliably and programmatically identify which content should be read aloud. In a typical publishing context, the audio player needs to read the article title and content, but avoid reading the header and footer text, navigation bars, and certain kinds of ads or captions. Our page analysis solution combines positive and negative query selectors. For each configuration, defined by a set of articles that share the same structure and layout, the http://readalong.ai solution supports a set of allow list selectors and a set of deny list selectors that together capture the appropriate content for synthesizing speech.

Furthermore, the TIME for Kids website posed many technical challenges because some pages are available only for paying subscribers, whereas some are open to the public. TIME for Kids offers four grade-specific editions, teaching materials, curriculum guides, and weekly virtual learning plans for each issue, as well as worksheets and quizzes. Therefore, each article has multiple versions for different reading levels in both English and Spanish—some with as many as seven different reading levels in both languages.

Our solution

We created a simple drop-in script that allowed TIME for Kids to only add one line of code to the header of any page where they wanted to offer audio. The script automated everything from page content delivery to audio-synthesis to webpage integration. Since the start of the school year, we’ve added the Kevin and Lupe voices (for English and Spanish content, respectively) to thousands of articles on timeforkids.com.

Our solution allowed for automated content delivery and audio synthesizing, which meant no need to sign into a dashboard, FTP, Dropbox, or otherwise send new article content to ReadAlong.ai each time a new page was added. The user-friendly backend of the solution also allows TIME for Kids to easily make word replacements, including global rules, to give the audio synthesizer engine lexicon hints for context-based pronunciations and difficult names, brands, or acronyms.

In addition to positioning and styling the launcher and player to match the TIME for Kids site design, as part of the customization, we added functionality to highlight and scroll the text as the article is read aloud, which is another helpful tool to support children in learning to recognize words and connect them to sounds. We customized this feature to be visible but not distracting, so the audio and visual elements could work in tandem to aid young readers. To support this enhanced feature, we implemented the detailed word- and sentence-level metadata available in Amazon Polly to provide a fluid highlighting experience that helps readers follow along as they encounter new words and concepts. This allows the listener to identify what they’re hearing as they view the content as it’s highlighted on the browser.

We also created a default for the Amazon Polly Kevin and Lupe voices to start at a slower speed, so the default pacing is at .9x, rather than at 1x, as another way to help early readers and pre-readers better access the content. Listeners have the ability to lower the default voice speed to .75x or increase to 1.5x, in order to accommodate more reading levels.

Business benefits for the customer

With our product in place on their site, TIME for Kids was able to voice their content in a scalable way. They deliver content on an article-by-article basis in two different languages (English and Spanish) and in seven different reading levels.

They’re also now able to easily collect and analyze data in real time, including both play and completion rates, and view most popular articles as well as articles with the most audio engagement.

We now know that 55% of kids that click to listen to an article complete 100% of the article, and 66% of kids that listen to an article complete more than half of the article. These significant completion rates reinforce the benefit and confirm that listeners are comfortable with the technology and the voice is relatable. The ReadAlong.ai audio also helped TIME for Kids promote its advanced accessibility features, including key articles with Spanish translation and read-aloud functionality, because the presence of the audio is featured prominently on the preview of each article along with other benefits (such as Spanish translation).

Stacy Bien, Director of Curriculum for TIME for Kids, was impressed with both the solution and the engagement data, saying,

“This is really a thing of beauty. This solution will help so many early readers develop their reading skills and easily consume more content. For us, we’ve seen a huge lift in engagement. That, coupled with the ease of use and cost-effectiveness, makes this a slam dunk.”

Conclusion

ReadAlong.ai used Amazon Polly to help TIME for Kids streamline the process of adding high-quality audio voiceover content to its premium subscription product. Our solution enabled the customer to significantly improve product time, precision, and cost. For example, a voiceover artist typically spends 1 hour or more to record an article, edit the audio, and master the final audio output. Now, once the ReadAlong.ai script has been added to the site, when new articles are created, the content is automatically processed without any time spent by a voiceover artist, audio editor, or administrator. The audio reads articles precisely and rarely requires adjustments, creating a valuable and immeasurable savings of both time and cost.

Collected KPIs tell us that not only did this become an easy way for the TIME for Kids team to manage audio functionality, but that the end-users—children early in the development of their reading abilities—take to the functionality as another tool on their reading path.

About the Author

Andrew Degenholtz is CEO and Founder of eMagazines and ReadAlong.ai, and is President of ValueMags, which he founded in 1999. Degenholtz holds a master’s in marketing from Northwestern University and a B.A. from Muhlenberg College. Previously, he was a member of the Alliance for Audited Media digital edition task force, created to develop best practices for acquisition of digital magazine subscribers.

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We are excited to announce that in Amazon Forecast, you can now start your forecast horizon at custom starting points, including on Sundays for weekly forecasts. This allows you to more closely align demand planning forecasts to local business practices and operational requirements.

Forecast is a fully managed service that uses statistical and machine learning (ML) algorithms to deliver highly accurate time series forecasts. It uses state-of-the-art algorithms to predict future time series data based on historical data, and requires no ML experience. Typical Forecast applications include resource planning for inventory, workforce staffing, and web traffic. In this post, we review a new option that allows you to align forecasts with business and demand cycles, while reducing operational cost by offloading aggregation workflows.

To optimize demand planning, forecasts need to align with business operations. Previously, starting points for forecasts were fixed: daily forecasts assumed demand starting at midnight each day, weekly predictions assumed Monday as the first day of the week, and monthly predictions started on the first day of each month. These predefined starting points presented two challenges. First, if your business cycle began at a different point than the fixed value, you had to manually aggregate forecasts to your required starting point. For example, if your business week began on a Sunday and you wanted to produce weekly forecasts, you had to manually aggregate daily forecasts to a Sunday–Saturday week. This additional work added cost and compute time, and presented opportunities for errors. Second, the training data and forecast periods weren’t consistent; if your data reflects a demand cycle that begins on Sundays, the predictor and forecast should also use Sunday as the starting point.

Custom forecast horizon starting points now align business operations and forecasts, eliminating the need for manual aggregation work and saving cost and compute. If you have a business week starting on Sundays, you can automatically aggregate daily data to generate weekly forecasts that begin on Sundays. Or you can begin daily forecasts starting at 9:00 AM. Predictors can now be aligned with your ground truth data, providing consistency between inputs and forecasts. Forecast horizon starting points are easily defined when training new predictors via the Forecast console or using Forecast APIs.

Define custom forecast horizon starting periods

The forecast horizon, also called frequency, is the length of time for which a forecast is made, and is bounded by a starting and ending point. In Forecast, you can now select specific starting points for daily, weekly, monthly, and yearly forecast horizons when training new predictors. These starting points—also called boundary values—are selected at one frequency unit finer than the forecast horizon, as shown in the following table.

With custom starting points, you can align forecasts to start at specific points in time that match your business processes and ground truth data, for example, the month of May, the 15th of the month, Sundays, or 15:00 hours. For forecast horizons coarser than the provided time series frequency, Forecast aggregates the time series data based on the custom starting point. For example:

• When generating daily forecasts from hourly data with a 9:00 AM starting period, forecasts are aggregated with hourly data each day between 9:00 AM to the following day at 8:00 AM
• When generating weekly forecasts from daily data with a Sunday starting period, forecasts are aggregated with daily data each week from Sunday to the following Saturday
• When generating monthly forecasts from daily data with a starting day of the 15th of the month, forecasts are aggregated with daily data from the 15th of the current month to the 14th of the next month
• When generating yearly forecasts from monthly data with a starting month of May, forecasts are aggregated with monthly data from May of the current year to April of next year

Available forecast frequencies

The following screenshots show examples of custom daily, weekly, monthly, and yearly forecast frequencies and starting points (the Time alignment boundary field on the Forecast console).

Specify custom forecast horizon starting points

You can define custom forecast horizon starting points when creating a new predictor. The following steps demonstrate how to do this using the Forecast console. We also offer a sample notebook that provides an example of how to integrate this new setting into your workflows.

1. On the Forecast console, choose View dataset groups, and then Create dataset group.
2. Create your dataset group, a target time series dataset, and load your data.
   You’re redirected to the Forecast console as your data is loaded.
3. After your target time series dataset is loaded into your dataset group and active, choose Start under Train a predictor.
4. In the Train predictor section, provide values for the Name, Forecast frequency, and Forecast horizon fields.
5. In the optional Time alignment boundary field, specify the starting point the predictor uses for the forecast.
   The values in this list depend on the Forecast frequency value you choose. In this example, we create weekly forecasts with a 1-week horizon, with Sunday as the starting day of the week and of the forecast.
   
6. Provide other optional configurations as needed and choose Create.
   
   After you create the predictor, you can create your forecast.
7. In the navigation pane, under your dataset group choose Predictors.
8. Select your new predictor.
9. Choose Create forecast.
   
10. Provide the necessary details and choose Start to create your forecast.
    
11. When the forecast is complete, choose Create forecast export to export the results.
    

The following screenshots are samples of the original input file (left) and the exported forecast results (right). The input file is at an hourly frequency, whereas the forecast is produced at a weekly frequency, beginning with Sunday as the first day of the week. This is an example of Forecast automatically aggregating over two levels of forecast frequencies (from hours to days).

Conclusion

Custom forecast horizon starting points in Forecast allow you to produce forecasts that align with your specific operational requirements. Work weeks start on different days in different regions, requiring forecasts that begin on days other than Mondays, and that are aligned with ground truth training and ongoing data. Or you may want to generate hourly forecasts that reflect a demand cycle beginning at 7:00 AM each day, for example.

Forecast also automatically aggregates fine-grained forecasts to higher-level frequencies (such as days into weeks). This allows you to produce forecasts aligned with your operations, and saves you costs by removing the need to stand up and manage aggregation workflows.

Custom starting points are optional. If you don’t provide specific starting points, forecasts start at default times. Specific forecast horizon starting points are only available with AutoPredictor. For more information, refer to New Amazon Forecast API that creates up to 40% more accurate forecasts and provides explainability and CreateAutoPredictor.

To learn more about forecast frequencies, refer to Data aggregation for different forecast frequencies. All these new capabilities are available in all Regions where Forecast is publicly available. For more information about Region availability, see AWS Regional Services.

About the Authors

Dan Sinnreich is a Sr. Product Manager for Amazon Forecast. He is focused on democratizing low-code/no-code machine learning and applying it to improve business outcomes. Outside of work, he can be found playing hockey, trying to improve his tennis serve, scuba diving, and reading science fiction.

Paras Arora is a Software Development Engineer in the Amazon Forecast Team. He is passionate about building cutting edge AI/ML solutions in the cloud. In his spare time, he enjoys hiking and traveling.

Chetan Surana is a Software Development Engineer in the Amazon Forecast team. His interests lie at the intersection of machine learning and software development, applying thoughtful design and engineering skills to solve problems. Outside of work, he enjoys photography, hiking, and cooking.

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