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Your guide to AI and ML at AWS re:Invent 2021

It’s almost here! Only 9 days until AWS re:Invent 2021, and we’re very excited to share some highlights you might enjoy this year. The AI/ML team has been working hard to serve up some amazing content and this year, we have more session types for you to enjoy. Back in person, we now have chalk…



It’s almost here! Only 9 days until AWS re:Invent 2021, and we’re very excited to share some highlights you might enjoy this year. The AI/ML team has been working hard to serve up some amazing content and this year, we have more session types for you to enjoy. Back in person, we now have chalk talks, workshops, builders’ sessions, and our traditional breakout sessions. Last year we hosted the first-ever machine learning (ML) keynote, and we are continuing the tradition. We also have more interactive and fun events happening with our AWS DeepRacer League and AWS BugBust Challenge. There are over 200 AI/ML sessions, including breakout sessions with customers such as Aon Corporation, Qualtrics, Shutterstock, and Bloomberg.

To help you plan your agenda for this year’s re:Invent, here are some highlights of the AI/ML track. You can also get the scoop from some of our AI/ML Community Heroes. So buckle up, and start registering for your favorite sessions.

Swami Sivasubramanian keynote

Join Swami Sivasubramanian, Vice President, Machine Learning, AWS on an exploration of what it takes to put data in action with an end-to-end data strategy including the latest news on databases, analytics, and ML.

Accelerate innovation with machine learning leadership session

With the rise in compute power and data proliferation, ML has moved from the peripheral to being a core part of businesses and organizations across industries. AWS customers use ML and AI services to make accurate predictions, get deeper insights from their data, reduce operational overhead, improve customer experiences, and create entirely new lines of business. In this session, hear from Bratin Saha, Vice President, Machine Leaning, AWS and explore how AWS services can help you move from idea to production with ML.

AI/ML session preview

Here’s a preview of some of the different sessions we’re offering this year by session type. You can always log in to the event portal to favorite or register for any of these sessions, or search the catalog for over 200 other sessions available.

Breakout sessions

Prepare data for ML with ease, speed, and accuracy (AIM319)

Join this session to learn how to prepare data for ML in minutes using Amazon SageMaker. SageMaker offers tools to simplify data preparation so that you can label, prepare, and understand your data. Walk through a complete data-preparation workflow, including how to label training datasets using SageMaker Ground Truth, as well as how to extract data from multiple data sources, transform it using the prebuilt visualization templates in SageMaker Data Wrangler, and create model features. Also, learn how to improve efficiency by using SageMaker Feature Store to create a repository to store, retrieve, and share features.

Achieve high performance and cost-effective model deployment (AIM408)

To maximize your ML investments, high performance and cost-effective techniques are needed to scale model deployments. In this session, learn about the deployment options available in Amazon SageMaker, including optimized infrastructure choices; real-time, asynchronous, and batch inferences; multi-container endpoints; multi-model endpoints; auto scaling; model monitoring; and CI/CD integration for your ML workloads. Discover how to choose a better inference option for your ML use case. Then, hear from Goldman Sachs about how they use SageMaker for fast, low-latency, and scalable deployments to provide relevant research content recommendations for their clients.

Implementing MLOps practices with Amazon SageMaker, featuring Vanguard (AIM320)

Implementing MLOps practices helps data scientists and operations engineers collaborate to prepare, build, train, deploy, and manage models at scale. During this session, explore the breadth of MLOps features in Amazon SageMaker that help you provision consistent model development environments, automate ML workflows, implement CI/CD pipelines for ML, monitor models in production, and standardize model governance capabilities. Then, hear from Vanguard as they share their journey enabling MLOps to achieve ML at scale for their polyglot model development platforms using SageMaker features, including SageMaker projects, SageMaker Pipelines, SageMaker Model Registry, and SageMaker Model Monitor.

Enhancing the customer experience with Amazon Personalize (AIM204)

Personalizing content for a customer online is key to breaking through the noise. Yet, brands face challenges that often prevent them from providing these seamless, relevant experiences. Learn how easy it is to use Amazon Personalize to tailor product and content recommendations to ensure that your users are getting the content they want, leading to increased engagement and retention.

AI/ML for sustainability innovation: Insight at the edge (AIM207)

As climate change, wildlife conservation, public health, racial and economic equity, and new energy solutions become increasingly interdependent, scalable solutions are needed for actionable analysis at the intersection of these fields. In this session, learn how the power of AI/ML and IoT can be brought as close as possible to the challenging edge environments that provide data to create these insights. Also learn how AWS puts AI/ML in the hands of the largest-scale fisheries on the planet, and how organizations can leverage data to support more sustainable, resilient supply chains.

Get started with AWS computer vision services (AIM204)

This session provides an overview of AWS computer vision services and demonstrates how these pretrained and customizable ML capabilities can help you get started quickly—no ML expertise required. Learn how to deploy these models onto the device of your choice to run an inference locally or use cloud APIs for your specific computing needs. Learn first-hand how Shutterstock uses AWS computer vision services to create performance at scale for media analysis, content moderation, and quality inspection use cases.

Chalk talk sessions

Build an ML-powered demand planning system using Amazon Forecast (AIM310)

This chalk talk explores how you can use Amazon Forecast to build an ML-powered, fully automated demand planning system for your business or your multi-tenant SaaS platform without needing any ML expertise. Forecast automatically generates highly accurate forecasts using ML, explains the drivers behind those forecasts, and keeps your ML models always up to date to capture new trends.

Hello, is it conversational AI you’re looking for? (AIM305)

Customers calling in for support expect a personalized experience and a quick resolution to their issue. With chatbots, you can provide automated and human-like conversational experiences for your customers. In this chalk talk, discuss strategies to design personalized experiences using Amazon Lex and Amazon Polly. Explore how to design conversation paths, customize responses, integrate with your applications, and enable self-service use cases to scale your customer support functions.

Harness the power of ML to protect your business with Amazon Fraud Detector (AIM308)

How does more than 20 years of Amazon experience fighting fraud translate into an AI service that can help companies detect more online fraud faster? In this session, learn how Amazon Fraud Detector transforms raw data into highly accurate ML-based fraud detection models. Then, discover how the service does data preparation and validation, feature engineering, data enrichment, and model training and tuning. Finally, with actual customer examples across a wide range of industries and fraud use cases, find out how the service makes deployment easy.

Deep learning applications with PyTorch (AIM404)

By using PyTorch in Amazon SageMaker, you have a flexible deep learning framework combined with a fully managed ML solution that allows you to transition seamlessly from research prototyping to production deployment. In this session, hear from the PyTorch team on the latest features and library releases. Also, learn how to develop with PyTorch using SageMaker for key use cases, such as using a BERT model for natural language processing (NLP) and instance segmentation for fine-grained computer vision with distributed training and model parallelism.

Explore, analyze, and process data using Jupyter notebooks (AIM324)

Before using a dataset to train a model, you need to explore, analyze, and preprocess it. During this chalk talk, learn how to use Amazon SageMaker to complete these tasks in a Jupyter notebook environment.

Machine learning at the edge with Amazon SageMaker (AIM410)

More ML models are being deployed on edge devices such as robots and smart cameras. In this chalk talk, dive into building computer vision (CV) applications at the edge for predictive maintenance, industrial IoT, and more. Learn how to operate and monitor multiple models across a fleet of devices. Also walk through the process to build and train CV models with Amazon SageMaker and how to package, deploy, and manage them with SageMaker Edge Manager. The chalk talk also covers edge device setup and MLOps lifecycle with over-the-air model updates and data capture to the cloud.

Builders’ sessions

Build and deploy a custom computer vision model in 60 minutes (AIM314)

Amazon Rekognition Custom Labels is an automated ML feature that enables customers to quickly train their own custom models for detecting business-specific objects and scenes from images—no ML expertise is required. In this builders’ session, learn how to use Amazon Rekognition Custom Labels to build and deploy your own computer vision model and push it to an application to showcase inference on images from a camera feed. Bring your laptop and an AWS account.

Easily label training data for machine learning at scale (AIM406)

Join this session to learn how to create high-quality labels while also reducing your data labeling costs by up to 70%. This builders’ session walks through the different workflow options in Amazon SageMaker Ground Truth, such as automatic labeling and assistive labeling features like auto-segmentation and image label verification. It also details how to build highly accurate training datasets for company brand logos, so you can build an ML model for company brand protection.

Workshop sessions

Develop your ML project with Amazon SageMaker (AIM402)

In this workshop, learn how to develop a full ML project end to end with Amazon SageMaker. Start with data exploration and analysis, data cleansing, and feature engineering with SageMaker Data Wrangler. Then, store features in SageMaker Feature Store, extract features for training with SageMaker Processing, train a model with SageMaker training, and then deploy it with SageMaker hosting. Also, learn how to use SageMaker Studio as an IDE and SageMaker Pipelines for orchestrating the ML workflow.

End-to-end 3D machine learning on Amazon SageMaker (AIM414)

As lidar sensors become more accessible and cost-effective, customers increasingly use point cloud data in new spaces like autonomous driving, robotics, and augmented reality. The growing availability of lidar sensors has increased use of point cloud data for ML tasks like 3D object detection, segmentation, object synthesis, and reconstruction. This workshop features Amazon SageMaker Ground Truth and explains how to ingest raw 3D point cloud data, label it, train a 3D object detection model, and deploy the model. The model in this session will be trained on an autonomous vehicle dataset.

AI workflow automation for document processing (AIM316)

Mortgage packets have hundreds of documents in various layouts and formats. With ML, you can set up a document-processing pipeline to automate mortgage application workflows like extracting text from W2s, paystubs, and deeds; classifying documents; or using custom entity recognition to pull out specific data points. In this workshop, learn various ways to use optical character recognition (OCR), NLP, and human-in-the-loop services to build a document-processing pipeline to automate mortgage applications—saving time, reducing manual effort, and improving ROI for your organization.

Boost the value of your media content with ML-powered search (AIM315)

Consumers rely on content not only to entertain but also to educate and facilitate purchasing decisions. To meet this demand, media content production is exploding. However, the process of producing, distributing, and monetizing this content is often complex, expensive, and time-consuming. Applying artificial intelligence and ML capabilities like image and video analysis, audio transcription, machine translation, and text analytics can solve many of these problems. In this workshop, utilize ML to extract detailed metadata from content and make it available for search, discovery, and editing use cases.

Instantly detect and diagnose anomalies within your business data (AIM302)

Anomalies in business data often indicate potential issues or even opportunities. ML can help you detect anomalies and then act on them proactively. In this workshop, learn how Amazon Lookout for Metrics automatically detects anomalies across thousands of metrics in near-real time and reduces false alarms.

Join the first annual AWS BugBust re:Invent Challenge and help set a Guinness record

The largest code fixing challenge is here! Python and Java developers of all skill levels can compete to fix software bugs, earn points, and win an array of prizes including Amazon Echo Dots, hoodies, and the grand prize of $1,500 USD. As you bust bugs, you also become part of an attempt to set the record for the largest bug fixing challenge with the Guinness World Records. All registered participants who fix even one bug will receive exclusive prizes and a certificate from AWS and Guinness to commemorate their contribution. Let the bug busting begin! You can join the challenge virtually or in-person at the AWS BugBust Hub in the main expo. Register now for free.

AWS DeepRacer: The fastest way to get rolling with machine learning

Developers of all skill levels from beginners to experts can get hands-on with ML by using AWS DeepRacer to train models in a cloud-based 3D racing simulator. Racers from virtually anywhere in the world can compete in the AWS DeepRacer League, the first global autonomous racing league driven by reinforcement learning. The race is on now! Sign in to AWS DeepRacer and compete in the AWS re:Invent Open for prizes and glory now through December 31, 2021. Tune in to the AWS DeepRacer League Championships on Twitch November 19 and 22 to see the 40 fastest developers of the 2021 season compete live. Learn from the best as they vie for a chance to advance to the Championship Cup Finale during Swami Sivasubramanian’s keynote on December 1, where they will race for their shot at $20,000 USD in cash prizes and the right to hoist the Championship Cup!

For those attending re:Invent in Las Vegas, don’t miss out on the opportunity to take your model from Sim2Real (simulation to reality) on the AWS DeepRacer Speedway inside the content hub at Caesar’s Forum. Upload your model and race a 1/18th scale autonomous RC car on a physical track. Stop by Tuesday afternoon to participate in the livestreamed wildcard race for a chance to win a trip back for re:Invent 2022. No model? No problem! The all-new AWS DeepRacer Arcade is available in the expo, where you can get literally get in the driver’s seat and take the wheel in this educational racing game. Take a spin on the virtual track and then compete against a featured AWS DeepRacer autonomous model in this arcade racing experience, with prizes and giveaways galore. Shift into the fast lane on your ML learning journey with AWS DeepRacer.

Head over to the re:Invent portal to build your schedule so you’re ready to hit the ground running. Be sure to stop by and talk to our experts at the AI/ML booth, or chat with the speakers after sessions. We can’t wait to see you in Las Vegas!

About the Authors

Andrea Youmans is a Product Marketing Manager on the AI Services team at AWS. Over the past 10 years she has worked in the technology and telecommunications industries, focused on developer storytelling and marketing campaigns. In her spare time, she enjoys heading to the lake with her husband and Aussie dog Oakley, tasting wine and enjoying a movie from time to time.


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Customize pronunciation using lexicons in Amazon Polly

Amazon Polly is a text-to-speech service that uses advanced deep learning technologies to synthesize natural-sounding human speech. It is used in a variety of use cases, such as contact center systems, delivering conversational user experiences with human-like voices for automated real-time status check, automated account and billing inquiries, and by news agencies like The Washington…




Amazon Polly is a text-to-speech service that uses advanced deep learning technologies to synthesize natural-sounding human speech. It is used in a variety of use cases, such as contact center systems, delivering conversational user experiences with human-like voices for automated real-time status check, automated account and billing inquiries, and by news agencies like The Washington Post to allow readers to listen to news articles.

As of today, Amazon Polly provides over 60 voices in 30+ language variants. Amazon Polly also uses context to pronounce certain words differently based upon the verb tense and other contextual information. For example, “read” in “I read a book” (present tense) and “I will read a book” (future tense) is pronounced differently.

However, in some situations you may want to customize the way Amazon Polly pronounces a word. For example, you may need to match the pronunciation with local dialect or vernacular. Names of things (e.g., Tomato can be pronounced as tom-ah-to or tom-ay-to), people, streets, or places are often pronounced in many different ways.

In this post, we demonstrate how you can leverage lexicons for creating custom pronunciations. You can apply lexicons for use cases such as publishing, education, or call centers.

Customize pronunciation using SSML tag

Let’s say you stream a popular podcast from Australia and you use the Amazon Polly Australian English (Olivia) voice to convert your script into human-like speech. In one of your scripts, you want to use words that are unknown to Amazon Polly voice. For example, you want to send Mātariki (Māori New Year) greetings to your New Zealand listeners. For such scenarios, Amazon Polly supports phonetic pronunciation, which you can use to achieve a pronunciation that is close to the correct pronunciation in the foreign language.

You can use the Speech Synthesis Markup Language (SSML) tag to suggest a phonetic pronunciation in the ph attribute. Let me show you how you can use SSML tag.

First, login into your AWS console and search for Amazon Polly in the search bar at the top. Select Amazon Polly and then choose Try Polly button.

In the Amazon Polly console, select Australian English from the language dropdown and enter following text in the Input text box and then click on Listen to test the pronunciation.

I’m wishing you all a very Happy Mātariki.

Sample speech without applying phonetic pronunciation:

If you hear the sample speech above, you can notice that the pronunciation of Mātariki – a word which is not part of Australian English – isn’t quite spot-on. Now, let’s look at how in such scenarios we can use phonetic pronunciation using SSML tag to customize the speech produced by Amazon Polly.

To use SSML tags, turn ON the SSML option in Amazon Polly console. Then copy and paste following SSML script containing phonetic pronunciation for Mātariki specified inside the ph attribute of the tag.

I’m wishing you all a very Happy Mātariki.

With the tag, Amazon Polly uses the pronunciation specified by the ph attribute instead of the standard pronunciation associated by default with the language used by the selected voice.

Sample speech after applying phonetic pronunciation:

If you hear the sample sound, you’ll notice that we opted for a different pronunciation for some of vowels (e.g., ā) to make Amazon Polly synthesize the sounds that are closer to the correct pronunciation. Now you might have a question, how do I generate the phonetic transcription “” for the word Mātariki?

You can create phonetic transcriptions by referring to the Phoneme and Viseme tables for the supported languages. In the example above we have used the phonemes for Australian English.

Amazon Polly offers support in two phonetic alphabets: IPA and X-Sampa. Benefit of X-Sampa is that they are standard ASCII characters, so it is easier to type the phonetic transcription with a normal keyboard. You can use either of IPA or X-Sampa to generate your transcriptions, but make sure to stay consistent with your choice, especially when you use a lexicon file which we’ll cover in the next section.

Each phoneme in the phoneme table represents a speech sound. The bolded letters in the “Example” column of the Phoneme/Viseme table in the Australian English page linked above represent the part of the word the “Phoneme” corresponds to. For example, the phoneme /j/ represents the sound that an Australian English speaker makes when pronouncing the letter “y” in “yes.”

Customize pronunciation using lexicons

Phoneme tags are suitable for one-off situations to customize isolated cases, but these are not scalable. If you process huge volume of text, managed by different editors and reviewers, we recommend using lexicons. Using lexicons, you can achieve consistency in adding custom pronunciations and simultaneously reduce manual effort of inserting phoneme tags into the script.

A good practice is that after you test the custom pronunciation on the Amazon Polly console using the tag, you create a library of customized pronunciations using lexicons. Once lexicons file is uploaded, Amazon Polly will automatically apply phonetic pronunciations specified in the lexicons file and eliminate the need to manually provide a tag.

Create a lexicon file

A lexicon file contains the mapping between words and their phonetic pronunciations. Pronunciation Lexicon Specification (PLS) is a W3C recommendation for specifying interoperable pronunciation information. The following is an example PLS document:

Matariki Mātariki NZ New Zealand

Make sure that you use correct value for the xml:lang field. Use en-AU if you’re uploading the lexicon file to use with the Amazon Polly Australian English voice. For a complete list of supported languages, refer to Languages Supported by Amazon Polly.

To specify a custom pronunciation, you need to add a element which is a container for a lexical entry with one or more element and one or more pronunciation information provided inside element.

The element contains the text describing the orthography of the element. You can use a element to specify the word whose pronunciation you want to customize. You can add multiple elements to specify all word variations, for example with or without macrons. The element is case-sensitive, and during speech synthesis Amazon Polly string matches the words inside your script that you’re converting to speech. If a match is found, it uses the element, which describes how the is pronounced to generate phonetic transcription.

You can also use for commonly used abbreviations. In the preceding example of a lexicon file, NZ is used as an alias for New Zealand. This means that whenever Amazon Polly comes across “NZ” (with matching case) in the body of the text, it’ll read those two letters as “New Zealand”.

For more information on lexicon file format, see Pronunciation Lexicon Specification (PLS) Version 1.0 on the W3C website.

You can save a lexicon file with as a .pls or .xml file before uploading it to Amazon Polly.

Upload and apply the lexicon file

Upload your lexicon file to Amazon Polly using the following instructions:

  1. On the Amazon Polly console, choose Lexicons in the navigation pane.
  2. Choose Upload lexicon.
  3. Enter a name for the lexicon and then choose a lexicon file.
  4. Choose the file to upload.
  5. Choose Upload lexicon.

If a lexicon by the same name (whether a .pls or .xml file) already exists, uploading the lexicon overwrites the existing lexicon.

Now you can apply the lexicon to customize pronunciation.

  1. Choose Text-to-Speech in the navigation pane.
  2. Expand Additional settings.
  3. Turn on Customize pronunciation.
  4. Choose the lexicon on the drop-down menu.

You can also choose Upload lexicon to upload a new lexicon file (or a new version).

It’s a good practice to version control the lexicon file in a source code repository. Keeping the custom pronunciations in a lexicon file ensures that you can consistently refer to phonetic pronunciations for certain words across the organization. Also, keep in mind the pronunciation lexicon limits mentioned on Quotas in Amazon Polly page.

Test the pronunciation after applying the lexicon

Let’s perform quick test using “Wishing all my listeners in NZ, a very Happy Mātariki” as the input text.

We can compare the audio files before and after applying the lexicon.

Before applying the lexicon:

After applying the lexicon:


In this post, we discussed how you can customize pronunciations of commonly used acronyms or words not found in the selected language in Amazon Polly. You can use SSML tag which is great for inserting one-off customizations or testing purposes. We recommend using Lexicon to create a consistent set of pronunciations for frequently used words across your organization. This enables your content writers to spend time on writing instead of the tedious task of adding phonetic pronunciations in the script repetitively. You can try this in your AWS account on the Amazon Polly console.

Summary of resources

About the Authors

Ratan Kumar is a Solutions Architect based out of Auckland, New Zealand. He works with large enterprise customers helping them design and build secure, cost-effective, and reliable internet scale applications using the AWS cloud. He is passionate about technology and likes sharing knowledge through blog posts and twitch sessions.

Maciek Tegi is a Principal Audio Designer and a Product Manager for Polly Brand Voices. He has worked in professional capacity in the tech industry, movies, commercials and game localization. In 2013, he was the first audio engineer hired to the Alexa Text-To- Speech team. Maciek was involved in releasing 12 Alexa TTS voices across different countries, over 20 Polly voices, and 4 Alexa celebrity voices. Maciek is a triathlete, and an avid acoustic guitar player.


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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…




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…




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.


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!


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.


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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