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Calculate inference units for an Amazon Rekognition Custom Labels model

Amazon Rekognition Custom Labels allows you to extend the object and scene detection capabilities of Amazon Rekognition to extract information from images that is uniquely helpful to your business. For example, you can find your logo in social media posts, identify your products on store shelves, classify machine parts in an assembly line, distinguish healthy…

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Amazon Rekognition Custom Labels allows you to extend the object and scene detection capabilities of Amazon Rekognition to extract information from images that is uniquely helpful to your business. For example, you can find your logo in social media posts, identify your products on store shelves, classify machine parts in an assembly line, distinguish healthy and infected plants, or detect your animated characters in videos.

Amazon Rekognition Custom Labels provides a simple end-to-end experience where you start by labeling a dataset. Amazon Rekognition Custom Labels then builds a custom machine learning (ML) model for you by inspecting the data and selecting the right ML algorithm. After your model is trained, you can start using it immediately for image analysis. You start a model by calling the StartProjectVersion API and providing the minimum number of inference units (IUs) to use. A single IU represents one unit of compute power. The number of images you can process with a single IU in a certain time frame depends on many factors, such as the size of the images processed and the complexity of the custom model. As you start the model, you can provide a higher number of IUs to increase the transactions per second (TPS) throughput of your model. Amazon Rekognition Custom Labels then provisions multiple compute resources in parallel to process your images more quickly.

However, determining the right number of IUs for your workload is tricky, because over-provisioned IUs causes unnecessary cost, and insufficient IUs result in exceeding provisioned throughput. Due to the lack of information of calculating appropriate IU, some customers tend to over-provision IUs to ensure their workloads run without any exception errors. This can be quite costly. Other customers spend a lot of time adding IUs until their workloads run smoothly. In this post, we show you how to calculate the IUs needed to meet your workload performance requirement at the lowest possible cost.

Understanding inference units

For this post, we use a commonly seen customer scenario to explain the concept of IUs.

For a specific use case in object/scene detection or classification, you train an Amazon Rekognition Custom Labels model. After the model is trained, you need to start the model for inference. Let’s assume you start your custom model at 2:00 PM and end at 5:00 PM, and choose to provision 1 IU, your total inference hours billed is 3 hours. Assuming that the model allows you to analyze five concurrent images per second (5 TPS), you can process 54,000 (5*3,600*3) images in 3 hours.

Now let’s assume that you want to process twice the number of images (108,000) in 3 hours using the same model. You can start the model for the same duration of 3 hours and provision 2 IUs. Similarly, if you need to process 54,000 images but want to reduce the processing time from 3 hours to 1 hour, you can provision 3 IUs, which means processing 15 images per second and then stopping the model after 1 hour. Your total inference hours billed would be 6 hours (3 hours elapsed * 2 IUs) and 3 hours (1 hour elapsed * 3 IUs), respectively. Because your throughput and cost are based on the provisioned IUs per hour, it’s important to calculate the right IUs needed for your workload.

After you train an Amazon Rekognition Custom Labels model, you can start the model with one or more IU. If your load of requests is higher than the maximum supported TPS based on the provisioned IU, Amazon Rekognition Custom Labels returns an exception called ProvisionedThroughputExceededException for all requests over the max TPS, which indicates that the model is maximally utilized. In general, max TPS depends on the trained custom model, input images, and number of IUs provisioned. Therefore, you can determine required IUs by calculating max TPS. To do this, you can start the model with 1 IU, and progressively increase input requests until the ProvisionedThroughputExceededException exception is raised. After you get the max TPS throughput of the model, you can use it to calculate the overall IUs needed for your workload. For example, if the max TPS throughput is 5 TPS and you need to process 15 images per second, you have to start the model with 3 IUs.

Solution overview

In the following sections, we discuss how to calculate max TPS throughput of an Amazon Rekognition Custom Labels model. Then you can calculate the exact IUs needed as you start the model to process your images.

We walk through the following high-level steps:

  1. Train a model using Amazon Rekognition Custom Labels.
  2. Start your model.
  3. Launch an Amazon Elastic Compute Cloud (Amazon EC2) instance and set up your test environment.
  4. Create a test script.
  5. Add sample image(s) and run the script.
  6. Review the program output.

Train a model using Amazon Rekognition Custom Labels

You start by training a model in Amazon Rekognition Custom Labels for your use case. To learn more about how to create, train, evaluate, and use a model that detects objects, scenes, and concepts in images, refer to Getting started with Amazon Rekognition Custom Labels.

Start your model

After your model is trained, start the model with 1 IU. You can use the following command from the AWS Command Line Interface (AWS CLI) to start your model:

start-project-version –project-version-arn –min-inference-units 1

In addition to the AWS CLI, the following code snippet shows how you can also use the API to start your Amazon Rekognition Custom Labels model:

import boto3 client = boto3.client(‘rekognition’) response = client.start_project_version(ProjectVersionArn=’string’,MinInferenceUnits=1)

Launch an EC2 instance and set up your test environment

Launch an EC2 instance that you use to run a script that uses a sample image to call the model we started in the previous step. You can follow the steps in the quick start guide to launch an EC2 instance. Although the guide uses an instance type of t2.micro, you should use a compute-optimized instance type such as C5 to run this test.

After you connect to the EC2 instance, run the following commands from the terminal to install the required dependencies:

sudo yum install python3 sudo yum install gcc sudo yum install python3-devel sudo pip3 install locust sudo pip3 install boto3

Create a test script

Create a Python file named tps.py with the following code:

from os import walk import inspect import time import functools import gevent.monkey gevent.monkey.patch_all() import argparse from pathlib import Path import boto3 as boto3 from botocore.config import Config from locust import task, constant_pacing, events, LoadTestShape from locust.env import Environment from locust.stats import stats_printer, stats_history from locust.log import setup_logging from locust import User setup_logging(“INFO”, None) ”’ This script will: 1. Read list of images from a base path 2. Run step load 3. Print the stats 4. Stop runs when non-zero faliure rate is observed 5. Use last run to calculate maximum TPS ”’ image_base_path = None project_version_arn = None aws_region = None class WebserviceUser(User): wait_time = constant_pacing(0) def detection_tests(self, image_path): try: start_time = time.time() with open(image_path, ‘rb’) as image: r = self.client.detect_custom_labels(ProjectVersionArn= project_version_arn, Image={‘Bytes’: image.read()}) except Exception as exception: total_time = int((time.time() – start_time) * 1000) print(exception) self.environment.events.request_failure.fire(request_type=”GET”, name=inspect.stack()[0][3], response_time=total_time, response_length=0, exception=exception) else: total_time = int((time.time() – start_time) * 1000) self.environment.events.request_success.fire(request_type=”GET”, name=inspect.stack()[0][3], response_time=total_time, response_length=0) def __init__(self, *args, **kwargs): for (dirpath, dirnames, filenames) in walk(image_base_path): self.images = [Path(image_base_path) / filename for filename in filenames] break super(WebserviceUser, self).__init__(*args, **kwargs) config = Config( retries={ ‘max_attempts’: 1, ‘mode’: ‘standard’ } ) self.client = boto3.client( ‘rekognition’, aws_region, config=config ) def detection_tests(image_path, arg): try: start_time = time.time() with open(image_path, ‘rb’) as image: r = self.client.detect_custom_labels(ProjectVersionArn= project_version_arn, Image={‘Bytes’: image.read()}) except Exception as exception: total_time = int((time.time() – start_time) * 1000) print(f’image: {image_path}, exception: {exception}’) self.environment.events.request_failure.fire(request_type=”GET”, name=inspect.stack()[0][3], response_time=total_time, response_length=0, exception=exception) else: total_time = int((time.time() – start_time) * 1000) self.environment.events.request_success.fire(request_type=”GET”, name=inspect.stack()[0][3], response_time=total_time, response_length=0) self.tasks = [functools.partial(detection_tests, image) for image in self.images] def run_load(user_count, spawn_rate): # setup Environment and Runner env = Environment(user_classes=[WebserviceUser]) local_runner = env.create_local_runner() # # start a WebUI instance env.create_web_ui(“127.0.0.1”, 8089) # start a greenlet that periodically outputs the current stats gevent.spawn(stats_printer(env.stats)) # start a greenlet that save current stats to history gevent.spawn(stats_history, env.runner) # start the test env.runner.start(user_count, spawn_rate=spawn_rate) # in 60 seconds stop the runner gevent.spawn_later(30, lambda: env.runner.quit()) # wait for the greenlets env.runner.greenlet.join() # stop the web server for good measures env.web_ui.stop() # Sleep so that history is up to date time.sleep(5) # NOTE: Max TPS calculated from last run. last_stats = env.stats.history[-1] max_tps = last_stats[‘current_rps’] – last_stats[‘current_fail_per_sec’] p95_latency = last_stats[‘response_time_percentile_95’] failure_tps = last_stats[‘current_fail_per_sec’] print(f’Max supported TPS: {max_tps}’) print(f’95th percentile response time: {p95_latency}’) return max_tps, p95_latency, failure_tps if __name__ == ‘__main__’: parser = argparse.ArgumentParser(description=’Script to find the max TPS supported by a project version’) parser.add_argument(‘–images’, type=str, help=’path to folder with images’, required=True) parser.add_argument(‘–project-version-arn’, type=str, help=’Project Version arn to run loadtest against’, required=True) parser.add_argument(‘–region’, type=str, help=’Project Version arn to run loadtest against’, required=True) args = parser.parse_args() image_base_path = args.images project_version_arn = args.project_version_arn aws_region = args.region user_count = 10 failure_tps = 0 # NOTE: If max TPS is not reached in 3 iterations the customer might be running with >1 IU max_iterations = 3 # NOTE: Advanced users can replace with custom shape & LocalRunner to find maxima # https://docs.locust.io/en/stable/generating-custom-load-shape.html while failure_tps <= 0 and max_iterations >= 0: max_tps, p95_latency, failure_tps = run_load(user_count, user_count/10) user_count *= 2 max_iterations -= 1

Add sample image(s) and run the script

Create a folder named images and add at least one sample image. This folder of images is used for inference using the model trained with Amazon Rekognition Custom Labels.

Run the Python script to calculate model TPS throughput:

python3 ./tps.py –images ./images –project-version-arn –region

Review the program output

This program gradually increases the load of requests using the representative images to maximally utilize the model. It creates multiple threads in parallel, and adds new threads over time. It runs for a few minutes and prints tabular formatted statistical information including number of requests, number of failed requests, latency statistics (average, min, max, median), average requests per second, and average failures per second. When it completes all the runs, it prints the max TPS (Max supported TPS) and 95th percentile latency in milliseconds (95th percentile response time).

Let’s assume that you get max TPS throughput of the model as 3, and you plan to process 12 images per second. In this case, you can start the model with 4 IUs to achieve the desired throughput.

Conclusion

In this post, we showed how to calculate the IUs needed to meet your requirement of workload performance at the lowest possible cost. By right-sizing the IU for your model, you ensure that you can process images with the required throughput and not pay extra by avoiding over-provisioning resources. To learn more about Amazon Rekognition Custom Labels use cases and other features, refer to Key features.

In addition to optimizing the IU, if your workload requires processing images in batches (such as once a day or week, or at scheduled times during the day), you can provision your custom model at scheduled times. The post Batch image processing with Amazon Rekognition Custom Labels shows you how to build a cost-optimal batch solution with Amazon Rekognition Custom Labels that provisions your custom model at scheduled times, processes all your images, and deprovisions your resources to avoid incurring extra cost.

About the Authors

Ditesh Kumar is a Software Developer working for Amazon Rekognition Custom Labels. He is focused on building scalable Computer Vision services and adding new features to enhance usability and adoption of Custom Labels. In his spare time Ditesh is a big fan of hiking and traveling and enjoys spending time with his family.

Kashif Imran is a Principal Solutions Architect at Amazon Web Services. He works with some of the largest AWS customers who are taking advantage of AI/ML to solve complex business problems. He provides technical guidance and design advice to implement computer vision applications at scale. His expertise spans application architecture, serverless, containers, NoSQL, and machine learning.

Sherry Ding is a Senior AI/ML Specialist Solutions Architect. She has extensive experience in machine learning with a PhD degree in Computer Science. She mainly works with Public Sector customers on various AI/ML related business challenges, helping them accelerate their machine learning journey on the AWS Cloud. When not helping customers, she enjoys outdoor activities.-



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AWS Week in Review – May 16, 2022

This post is part of our Week in Review series. Check back each week for a quick roundup of interesting news and announcements from AWS! I had been on the road for the last five weeks and attended many of the AWS Summits in Europe. It was great to talk to so many of you…

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This post is part of our Week in Review series. Check back each week for a quick roundup of interesting news and announcements from AWS!

I had been on the road for the last five weeks and attended many of the AWS Summits in Europe. It was great to talk to so many of you in person. The Serverless Developer Advocates are going around many of the AWS Summits with the Serverlesspresso booth. If you attend an event that has the booth, say “Hi ” to my colleagues, and have a coffee while asking all your serverless questions. You can find all the upcoming AWS Summits in the events section at the end of this post.

Last week’s launches
Here are some launches that got my attention during the previous week.

AWS Step Functions announced a new console experience to debug your state machine executions – Now you can opt-in to the new console experience of Step Functions, which makes it easier to analyze, debug, and optimize Standard Workflows. The new page allows you to inspect executions using three different views: graph, table, and event view, and add many new features to enhance the navigation and analysis of the executions. To learn about all the features and how to use them, read Ben’s blog post.

Example on how the Graph View looks

Example on how the Graph View looks

AWS Lambda now supports Node.js 16.x runtime – Now you can start using the Node.js 16 runtime when you create a new function or update your existing functions to use it. You can also use the new container image base that supports this runtime. To learn more about this launch, check Dan’s blog post.

AWS Amplify announces its Android library designed for Kotlin – The Amplify Android library has been rewritten for Kotlin, and now it is available in preview. This new library provides better debugging capacities and visibility into underlying state management. And it is also using the new AWS SDK for Kotlin that was released last year in preview. Read the What’s New post for more information.

Three new APIs for batch data retrieval in AWS IoT SiteWise – With this new launch AWS IoT SiteWise now supports batch data retrieval from multiple asset properties. The new APIs allow you to retrieve current values, historical values, and aggregated values. Read the What’s New post to learn how you can start using the new APIs.

AWS Secrets Manager now publishes secret usage metrics to Amazon CloudWatch – This launch is very useful to see the number of secrets in your account and set alarms for any unexpected increase or decrease in the number of secrets. Read the documentation on Monitoring Secrets Manager with Amazon CloudWatch for more information.

For a full list of AWS announcements, be sure to keep an eye on the What’s New at AWS page.

Other AWS News
Some other launches and news that you may have missed:

IBM signed a deal with AWS to offer its software portfolio as a service on AWS. This allows customers using AWS to access IBM software for automation, data and artificial intelligence, and security that is built on Red Hat OpenShift Service on AWS.

Podcast Charlas Técnicas de AWS – If you understand Spanish, this podcast is for you. Podcast Charlas Técnicas is one of the official AWS podcasts in Spanish. This week’s episode introduces you to Amazon DynamoDB and shares stories on how different customers use this database service. You can listen to all the episodes directly from your favorite podcast app or the podcast web page.

AWS Open Source News and Updates – Ricardo Sueiras, my colleague from the AWS Developer Relation team, runs this newsletter. It brings you all the latest open-source projects, posts, and more. Read edition #112 here.

Upcoming AWS Events
It’s AWS Summits season and here are some virtual and in-person events that might be close to you:

You can register for re:MARS to get fresh ideas on topics such as machine learning, automation, robotics, and space. The conference will be in person in Las Vegas, June 21–24.

That’s all for this week. Check back next Monday for another Week in Review!

— Marcia



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Personalize your machine translation results by using fuzzy matching with Amazon Translate

A person’s vernacular is part of the characteristics that make them unique. There are often countless different ways to express one specific idea. When a firm communicates with their customers, it’s critical that the message is delivered in a way that best represents the information they’re trying to convey. This becomes even more important when…

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A person’s vernacular is part of the characteristics that make them unique. There are often countless different ways to express one specific idea. When a firm communicates with their customers, it’s critical that the message is delivered in a way that best represents the information they’re trying to convey. This becomes even more important when it comes to professional language translation. Customers of translation systems and services expect accurate and highly customized outputs. To achieve this, they often reuse previous translation outputs—called translation memory (TM)—and compare them to new input text. In computer-assisted translation, this technique is known as fuzzy matching. The primary function of fuzzy matching is to assist the translator by speeding up the translation process. When an exact match can’t be found in the TM database for the text being translated, translation management systems (TMSs) often have the option to search for a match that is less than exact. Potential matches are provided to the translator as additional input for final translation. Translators who enhance their workflow with machine translation capabilities such as Amazon Translate often expect fuzzy matching data to be used as part of the automated translation solution.

In this post, you learn how to customize output from Amazon Translate according to translation memory fuzzy match quality scores.

Translation Quality Match

The XML Localization Interchange File Format (XLIFF) standard is often used as a data exchange format between TMSs and Amazon Translate. XLIFF files produced by TMSs include source and target text data along with match quality scores based on the available TM. These scores—usually expressed as a percentage—indicate how close the translation memory is to the text being translated.

Some customers with very strict requirements only want machine translation to be used when match quality scores are below a certain threshold. Beyond this threshold, they expect their own translation memory to take precedence. Translators often need to apply these preferences manually either within their TMS or by altering the text data. This flow is illustrated in the following diagram. The machine translation system processes the translation data—text and fuzzy match scores— which is then reviewed and manually edited by translators, based on their desired quality thresholds. Applying thresholds as part of the machine translation step allows you to remove these manual steps, which improves efficiency and optimizes cost.

Machine Translation Review Flow

Figure 1: Machine Translation Review Flow

The solution presented in this post allows you to enforce rules based on match quality score thresholds to drive whether a given input text should be machine translated by Amazon Translate or not. When not machine translated, the resulting text is left to the discretion of the translators reviewing the final output.

Solution Architecture

The solution architecture illustrated in Figure 2 leverages the following services:

  • Amazon Simple Storage Service – Amazon S3 buckets contain the following content:
    • Fuzzy match threshold configuration files
    • Source text to be translated
    • Amazon Translate input and output data locations
  • AWS Systems Manager – We use Parameter Store parameters to store match quality threshold configuration values
  • AWS Lambda – We use two Lambda functions:
    • One function preprocesses the quality match threshold configuration files and persists the data into Parameter Store
    • One function automatically creates the asynchronous translation jobs
  • Amazon Simple Queue Service – An Amazon SQS queue triggers the translation flow as a result of new files coming into the source bucket

Solution Architecture Diagram

Figure 2: Solution Architecture

You first set up quality thresholds for your translation jobs by editing a configuration file and uploading it into the fuzzy match threshold configuration S3 bucket. The following is a sample configuration in CSV format. We chose CSV for simplicity, although you can use any format. Each line represents a threshold to be applied to either a specific translation job or as a default value to any job.

default, 75 SourceMT-Test, 80

The specifications of the configuration file are as follows:

  • Column 1 should be populated with the name of the XLIFF file—without extension—provided to the Amazon Translate job as input data.
  • Column 2 should be populated with the quality match percentage threshold. For any score below this value, machine translation is used.
  • For all XLIFF files whose name doesn’t match any name listed in the configuration file, the default threshold is used—the line with the keyword default set in Column 1.

Auto-generated parameter in Systems Manager Parameter Store

Figure 3: Auto-generated parameter in Systems Manager Parameter Store

When a new file is uploaded, Amazon S3 triggers the Lambda function in charge of processing the parameters. This function reads and stores the threshold parameters into Parameter Store for future usage. Using Parameter Store avoids performing redundant Amazon S3 GET requests each time a new translation job is initiated. The sample configuration file produces the parameter tags shown in the following screenshot.

The job initialization Lambda function uses these parameters to preprocess the data prior to invoking Amazon Translate. We use an English-to-Spanish translation XLIFF input file, as shown in the following code. It contains the initial text to be translated, broken down into what is referred to as segments, represented in the source tags.

Consent Form CONSENT FORM FORMULARIO DE CONSENTIMIENTO Screening Visit: Screening Visit Selección

The source text has been pre-matched with the translation memory beforehand. The data contains potential translation alternatives—represented as tags—alongside a match quality attribute, expressed as a percentage. The business rule is as follows:

  • Segments received with alternative translations and a match quality below the threshold are untouched or empty. This signals to Amazon Translate that they must be translated.
  • Segments received with alternative translations with a match quality above the threshold are pre-populated with the suggested target text. Amazon Translate skips those segments.

Let’s assume the quality match threshold configured for this job is 80%. The first segment with 99% match quality isn’t machine translated, whereas the second segment is, because its match quality is below the defined threshold. In this configuration, Amazon Translate produces the following output:

Consent Form FORMULARIO DE CONSENTIMIENTO CONSENT FORM FORMULARIO DE CONSENTIMIENTO Screening Visit: Visita de selección Screening Visit Selección

In the second segment, Amazon Translate overwrites the target text initially suggested (Selección) with a higher quality translation: Visita de selección.

One possible extension to this use case could be to reuse the translated output and create our own translation memory. Amazon Translate supports customization of machine translation using translation memory thanks to the parallel data feature. Text segments previously machine translated due to their initial low-quality score could then be reused in new translation projects.

In the following sections, we walk you through the process of deploying and testing this solution. You use AWS CloudFormation scripts and data samples to launch an asynchronous translation job personalized with a configurable quality match threshold.

Prerequisites

For this walkthrough, you must have an AWS account. If you don’t have an account yet, you can create and activate one.

Launch AWS CloudFormation stack

  1. Choose Launch Stack:
  2. For Stack name, enter a name.
  3. For ConfigBucketName, enter the S3 bucket containing the threshold configuration files.
  4. For ParameterStoreRoot, enter the root path of the parameters created by the parameters processing Lambda function.
  5. For QueueName, enter the SQS queue that you create to post new file notifications from the source bucket to the job initialization Lambda function. This is the function that reads the configuration file.
  6. For SourceBucketName, enter the S3 bucket containing the XLIFF files to be translated. If you prefer to use a preexisting bucket, you need to change the value of the CreateSourceBucket parameter to No.
  7. For WorkingBucketName, enter the S3 bucket Amazon Translate uses for input and output data.
  8. Choose Next.

    Figure 4: CloudFormation stack details

  9. Optionally on the Stack Options page, add key names and values for the tags you may want to assign to the resources about to be created.
  10. Choose Next.
  11. On the Review page, select I acknowledge that this template might cause AWS CloudFormation to create IAM resources.
  12. Review the other settings, then choose Create stack.

AWS CloudFormation takes several minutes to create the resources on your behalf. You can watch the progress on the Events tab on the AWS CloudFormation console. When the stack has been created, you can see a CREATE_COMPLETE message in the Status column on the Overview tab.

Test the solution

Let’s go through a simple example.

  1. Download the following sample data.
  2. Unzip the content.

There should be two files: an .xlf file in XLIFF format, and a threshold configuration file with .cfg as the extension. The following is an excerpt of the XLIFF file.

English to French sample file extract

Figure 5: English to French sample file extract

  1. On the Amazon S3 console, upload the quality threshold configuration file into the configuration bucket you specified earlier.

The value set for test_En_to_Fr is 75%. You should be able to see the parameters on the Systems Manager console in the Parameter Store section.

  1. Still on the Amazon S3 console, upload the .xlf file into the S3 bucket you configured as source. Make sure the file is under a folder named translate (for example, /translate/test_En_to_Fr.xlf).

This starts the translation flow.

  1. Open the Amazon Translate console.

A new job should appear with a status of In Progress.

Auto-generated parameter in Systems Manager Parameter Store

Figure 6: In progress translation jobs on Amazon Translate console

  1. Once the job is complete, click into the job’s link and consult the output. All segments should have been translated.

All segments should have been translated. In the translated XLIFF file, look for segments with additional attributes named lscustom:match-quality, as shown in the following screenshot. These custom attributes identify segments where suggested translation was retained based on score.

Custom attributes identifying segments where suggested translation was retained based on score

Figure 7: Custom attributes identifying segments where suggested translation was retained based on score

These were derived from the translation memory according to the quality threshold. All other segments were machine translated.

You have now deployed and tested an automated asynchronous translation job assistant that enforces configurable translation memory match quality thresholds. Great job!

Cleanup

If you deployed the solution into your account, don’t forget to delete the CloudFormation stack to avoid any unexpected cost. You need to empty the S3 buckets manually beforehand.

Conclusion

In this post, you learned how to customize your Amazon Translate translation jobs based on standard XLIFF fuzzy matching quality metrics. With this solution, you can greatly reduce the manual labor involved in reviewing machine translated text while also optimizing your usage of Amazon Translate. You can also extend the solution with data ingestion automation and workflow orchestration capabilities, as described in Speed Up Translation Jobs with a Fully Automated Translation System Assistant.

About the Authors

Narcisse Zekpa is a Solutions Architect based in Boston. He helps customers in the Northeast U.S. accelerate their adoption of the AWS Cloud, by providing architectural guidelines, design innovative, and scalable solutions. When Narcisse is not building, he enjoys spending time with his family, traveling, cooking, and playing basketball.

Dimitri Restaino is a Solutions Architect at AWS, based out of Brooklyn, New York. He works primarily with Healthcare and Financial Services companies in the North East, helping to design innovative and creative solutions to best serve their customers. Coming from a software development background, he is excited by the new possibilities that serverless technology can bring to the world. Outside of work, he loves to hike and explore the NYC food scene.



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Enhance the caller experience with hints in Amazon Lex

We understand speech input better if we have some background on the topic of conversation. Consider a customer service agent at an auto parts wholesaler helping with orders. If the agent knows that the customer is looking for tires, they’re more likely to recognize responses (for example, “Michelin”) on the phone. Agents often pick up…

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We understand speech input better if we have some background on the topic of conversation. Consider a customer service agent at an auto parts wholesaler helping with orders. If the agent knows that the customer is looking for tires, they’re more likely to recognize responses (for example, “Michelin”) on the phone. Agents often pick up such clues or hints based on their domain knowledge and access to business intelligence dashboards. Amazon Lex now supports a hints capability to enhance the recognition of relevant phrases in a conversation. You can programmatically provide phrases as hints during a live interaction to influence the transcription of spoken input. Better recognition drives efficient conversations, reduces agent handling time, and ultimately increases customer satisfaction.

In this post, we review the runtime hints capability and use it to implement verification of callers based on their mother’s maiden name.

Overview of the runtime hints capability

You can provide a list of phrases or words to help your bot with the transcription of speech input. You can use these hints with built-in slot types such as first and last names, street names, city, state, and country. You can also configure these for your custom slot types.

You can use the capability to transcribe names that may be difficult to pronounce or understand. For example, in the following sample conversation, we use it to transcribe the name “Loreck.”

Conversation 1

IVR: Welcome to ACME bank. How can I help you today?

Caller: I want to check my account balance.

IVR: Sure. Which account should I pull up?

Caller: Checking

IVR: What is the account number?

Caller: 1111 2222 3333 4444

IVR: For verification purposes, what is your mother’s maiden name?

Caller: Loreck

IVR: Thank you. The balance on your checking account is 123 dollars.

Words provided as hints are preferred over other similar words. For example, in the second sample conversation, the runtime hint (“Smythe”) is selected over a more common transcription (“Smith”).

Conversation 2

IVR: Welcome to ACME bank. How can I help you today?

Caller: I want to check my account balance.

IVR: Sure. Which account should I pull up?

Caller: Checking

IVR: What is the account number?

Caller: 5555 6666 7777 8888

IVR: For verification purposes, what is your mother’s maiden name?

Caller: Smythe

IVR: Thank you. The balance on your checking account is 456 dollars.

If the name doesn’t match the runtime hint, you can fail the verification and route the call to an agent.

Conversation 3

IVR: Welcome to ACME bank. How can I help you today?

Caller: I want to check my account balance.

IVR: Sure. Which account should I pull up?

Caller: Savings

IVR: What is the account number?

Caller: 5555 6666 7777 8888

IVR: For verification purposes, what is your mother’s maiden name?

Caller: Jane

IVR: There is an issue with your account. For support, you will be forwarded to an agent.

Solution overview

Let’s review the overall architecture for the solution (see the following diagram):

  • We use an Amazon Lex bot integrated with an Amazon Connect contact flow to deliver the conversational experience.
  • We use a dialog codehook in the Amazon Lex bot to invoke an AWS Lambda function that provides the runtime hint at the previous turn of the conversation.
  • For the purposes of this post, the mother’s maiden name data used for authentication is stored in an Amazon DynamoDB table.
  • After the caller is authenticated, the control is passed to the bot to perform transactions (for example, check balance)

In addition to the Lambda function, you can also send runtime hints to Amazon Lex V2 using the PutSession, RecognizeText, RecognizeUtterance, or StartConversation operations. The runtime hints can be set at any point in the conversation and are persisted at every turn until cleared.

Deploy the sample Amazon Lex bot

To create the sample bot and configure the runtime phrase hints, perform the following steps. This creates an Amazon Lex bot called BankingBot, and one slot type (accountNumber).

  1. Download the Amazon Lex bot.
  2. On the Amazon Lex console, choose Actions, Import.
  3. Choose the file BankingBot.zip that you downloaded, and choose Import.
  4. Choose the bot BankingBot on the Amazon Lex console.
  5. Choose the language English (GB).
  6. Choose Build.
  7. Download the supporting Lambda code.
  8. On the Lambda console, create a new function and select Author from scratch.
  9. For Function name, enter BankingBotEnglish.
  10. For Runtime, choose Python 3.8.
  11. Choose Create function.
  12. In the Code source section, open lambda_function.py and delete the existing code.
  13. Download the function code and open it in a text editor.
  14. Copy the code and enter it into the empty function code field.
  15. Choose deploy.
  16. On the Amazon Lex console, select the bot BankingBot.
  17. Choose Deployment and then Aliases, then choose the alias TestBotAlias.
  18. On the Aliases page, choose Languages and choose English (GB).
  19. For Source, select the bot BankingBotEnglish.
  20. For Lambda version or alias, enter $LATEST.
  21. On the DynamoDB console, choose Create table.
  22. Provide the name as customerDatabase.
  23. Provide the partition key as accountNumber.
  24. Add an item with accountNumber: “1111222233334444” and mothersMaidenName “Loreck”.
  25. Add item with accountNumber: “5555666677778888” and mothersMaidenName “Smythe”.
  26. Make sure the Lambda function has permissions to read from the DynamoDB table customerDatabase.
  27. On the Amazon Connect console, choose Contact flows.
  28. In the Amazon Lex section, select your Amazon Lex bot and make it available for use in the Amazon Connect contact flow.
  29. Download the contact flow to integrate with the Amazon Lex bot.
  30. Choose the contact flow to load it into the application.
  31. Make sure the right bot is configured in the “Get Customer Input” block.
  32. Choose a queue in the “Set working queue” block.
  33. Add a phone number to the contact flow.
  34. Test the IVR flow by calling in to the phone number.

Test the solution

You can now call in to the Amazon Connect phone number and interact with the bot.

Conclusion

Runtime hints allow you to influence the transcription of words or phrases dynamically in the conversation. You can use business logic to identify the hints as the conversation evolves. Better recognition of the user input allows you to deliver an enhanced experience. You can configure runtime hints via the Lex V2 SDK. The capability is available in all AWS Regions where Amazon Lex operates in the English (Australia), English (UK), and English (US) locales.

To learn more, refer to runtime hints.

About the Authors

Kai Loreck is a professional services Amazon Connect consultant. He works on designing and implementing scalable customer experience solutions. In his spare time, he can be found playing sports, snowboarding, or hiking in the mountains.

Anubhav Mishra is a Product Manager with AWS. He spends his time understanding customers and designing product experiences to address their business challenges.

Sravan Bodapati is an Applied Science Manager at AWS Lex. He focuses on building cutting edge Artificial Intelligence and Machine Learning solutions for AWS customers in ASR and NLP space. In his spare time, he enjoys hiking, learning economics, watching TV shows and spending time with his family.



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