Two years ago, we introduced Amazon SageMaker Studio, the industry’s first fully integrated development environment (IDE) for machine learning (ML). Amazon SageMaker Studio provides a single, web-based visual interface where you can perform all ML development steps, improving data science team productivity by up to 10 times
Many data scientists love the R project, an open-source ecosystem with more than 18,000 packages that is not just a programming language but is also an interactive environment for doing data science. RStudio is one of the most popular IDE among R developers for ML and data science projects. RStudio provides open-source tools for R and enterprise-ready professional software for data science teams to develop and share their work in the organization. But, building, securing, scaling and maintaining RStudio yourself is tedious and cumbersome.
Today, in collaboration with RStudio PBC, we are excited to announce the general availability of RStudio on Amazon SageMaker, the industry’s first fully managed RStudio Workbench IDE in the cloud. You can now bring your current RStudio license to easily migrate your self-managed RStudio environments to Amazon SageMaker in just a few simple steps. If you’d like to read more about this exciting collaboration, check out this blog from RStudio PBC.
With RStudio on Amazon SageMaker, administrators can have a simple experience to migrate their RStudio environments to integrate into Amazon SageMaker and bring existing RStudio licenses to manage through AWS License Manager. They can onboard both R and Python developers to the same Amazon SageMaker domain using AWS Single Sign-On (SSO) or AWS Identity and Access Management (IAM) and take it as a centralized place to configure both RStudio and Amzon SageMaker Studio.
So, data scientists have a freedom of choice between programming languages and coding interfaces to switch between RStudio and Amazon SageMaker Studio notebooks. All of their work, including code, datasets, repositories, and other artifacts are synchronized between the two environments through the underlying Amazon EFS storage.
Getting Started with RStudio on SageMaker
You now can launch the familiar RStudio Workbench with a simple click from Amazon SageMaker. Before getting started, your administrator needs to buy an appropriate license from RStudio PBC for end-users, set up your granted licenses in AWS License Manager, and create an Amazon SageMaker domain and user profile to launch RStudio on Amazon SageMaker. To learn all the administrator jobs, including managing licenses and monitoring usages, see a blog post of the setting up process, or Manage RStudio on Amazon SageMaker in the AWS documentation.
Once the required setup process is completed, you can open the RStudio Workbench from the new Launch app drop-down list in the created user list and select RStudio.
You will immediately see the RStudio Workbench home page and a list of sessions, projects, and published content on the home page. To create a new session, select the New Session button on the page, select a desired instance in the Instance Type dropdown list, and choose Start Session.
When you choose a compute instance type for a lightweight analysis that can be powered by two vCPU and four GiB memory, you can use a default ml.t3.medium instance. For a complex and large-scale ML modeling, you can choose a large instance with desired compute and memory from a wide array of ML instances available on Amazon SageMaker.
In a few minutes, your session will be ready for development in RStudio Workbench. When you launch your RStudio session, the Base R image serves as the basis of your instance. This Docker image includes R v4.0, AWS tools such as awscli, sagemaker, boto3 Python packages, and reticulate package for the interoperability between Python and R.
RStudio Connect is designed to allow data scientists to publish insights and dashboard and web applications from RStudio Workbench easily. RStudio Package Manager centrally manages the package repository for your organization so that data scientists can securely install packages faster while ensuring project reproducibility and repeatability.
Your administrator, for example, can create a repository and subscribe it to the built-in source named cran in RStudio Package Manager.
$ rspm sync –wait # Initiate a sync $ rspm create repo –name=prod-cran –description=’Access CRAN packages’ # Create a repository: $ rspm subscribe –repo=prod-cran –source=cran # Subscribe the repository to the cran source
When these steps are completed, you can use the prod-cran repository in the web interface of RStudio Package Manager.
Now, you can configure this repository to install and manage your packages in RStudio Workbench. You can also configure RStudio Connect to publish insights, dashboard and web applications from RStudio Workbench via RStudio Connect so that your collaborators can easily consume your work.
For example, you run the analysis inline to create an R Markdown that can be published to your collaborators. You can preview the slides while writing codes with the Preview button and publish it with the Publish icon in your RStudio session.
To learn more, see Host RStudio Connect and Package Manager for ML development in RStudio on Amazon SageMaker written by my colleagues, Michael Hsieh, Chayan Panda, and Farooq Sabir on the AWS Machine Learning Blog.
Integrating training jobs with Amazon SageMaker
One of the benefits of using RStudio on Amazon SageMaker is the integration of Amazon SageMaker features. Your RStudio and Jupyter Notebook instances of Amazon SageMaker allow you to share the same Amazon EFS file system. You can import R codes written in Jupyter Notebook or use the same files in both Jupyter Notebook and RStudio without having to move your files between the two.
For example, you can run an R sample code including importing libraries, creating an Amazon SageMaker session, getting the IAM role, and importing and visualizing sample data. And then, it stores data on the S3 bucket, and triggers a training task with an XGBoost model by specifying the training container and defining an Amazon SageMaker Estimator. To learn more, see R sample codes in Amazon SageMaker.
# Import reticulate, readr and sagemaker libraries library(reticulate) library(readr) sagemaker <- import('sagemaker') # Create a sagemaker session session <- sagemaker$Session() # Get execution role role_arn <- sagemaker$get_execution_role() # Read a csv file from UCI public repository data_file <- 'http://archive.ics.uci.edu/ml/machine-learning-databases/abalone/abalone.data' # Copy data to a dataframe, rename columns, and show dataframe head data_csv <- read_csv(file = data_file, col_names = FALSE, col_types = cols()) names(data_csv) <- c('sex', 'length', 'diameter', 'height', 'whole_weight', 'shucked_weight', 'viscera_weight', 'shell_weight', 'rings') head(data_csv) # Visualize data have height equal to 0 library(ggplot2) options(repr.plot.width = 5, repr.plot.height = 4) ggplot(abalone, aes(x = height, y = rings, color = sex, alpha=0.5)) + geom_point() + geom_jitter() # Upload data to Amazon S3 bucket s3_train <- session$upload_data(path = data_csv, bucket = my_s3_bucket, key_prefix = 'r_hello_world_demo/data') s3_path = paste('s3://',bucket,'/r_hello_world_demo/data/abalone.csv',sep = '') # Train a XGBoost model, specify the training containers, and define an Amazon SageMaker Estimator container <- sagemaker$image_uris$retrieve(framework='xgboost', region= session$boto_region_name, version='latest') estimator <- sagemaker$estimator$Estimator(image_uri = container, role = role_arn, train_instance_count = 1L, train_instance_type = 'ml.m5.4xlarge', train_volume_size = 30L, train_max_run = 3600L, input_mode = 'File', output_path = s3_path)
RStudio on Amazon SageMaker is available in all AWS Regions where both Amazon SageMaker Studio and AWS License Manager are available. You can bring your own license of RStudio on Amazon SageMaker and pay for the underlying compute and storage resources within Amazon SageMaker or other AWS services, based on your usage.
To get started with RStudio on Amazon SageMaker, you can use AWS Free Tier. You can use 250 hours of ml.t3.medium instance on Amazon SageMaker Studio per month for the first two months. To learn more, see Amazon SageMaker Pricing page.
Give it a try, and please send us feedback either in the AWS forum for Amazon SageMaker or through your usual AWS support contacts.
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
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.
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
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
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 “mA:.tA:.ri.ki” 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
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:
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.
You can also use
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:
- On the Amazon Polly console, choose Lexicons in the navigation pane.
- Choose Upload lexicon.
- Enter a name for the lexicon and then choose a lexicon file.
- Choose the file to upload.
- 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.
- Choose Text-to-Speech in the navigation pane.
- Expand Additional settings.
- Turn on Customize pronunciation.
- 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.
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
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.
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…
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
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.
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!
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.
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.
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
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.
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.
The source text has been pre-matched with the translation memory beforehand. The data contains potential translation alternatives—represented as
- 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:
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.
Launch AWS CloudFormation stack
- Choose Launch Stack:
- For Stack name, enter a name.
- For ConfigBucketName, enter the S3 bucket containing the threshold configuration files.
- For ParameterStoreRoot, enter the root path of the parameters created by the parameters processing Lambda function.
- 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.
- 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.
- For WorkingBucketName, enter the S3 bucket Amazon Translate uses for input and output data.
- Choose Next.
Figure 4: CloudFormation stack details
- 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.
- Choose Next.
- On the Review page, select I acknowledge that this template might cause AWS CloudFormation to create IAM resources.
- 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.
- Download the following sample data.
- 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.
Figure 5: English to French sample file extract
- 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.
- 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,
This starts the translation flow.
- Open the Amazon Translate console.
A new job should appear with a status of In Progress.
Figure 6: In progress translation jobs on Amazon Translate console
- 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.
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.
Creating new career opportunities with Google Cloud
Fostering inclusive spaces through Disability Alliance
Facebook: Community Standards Enforcement Report Assessment Results
Build a cold start time series forecasting engine using AutoGluon
AWS Local Zones Are Now Open in Las Vegas, New York City, and Portland
Use deep learning frameworks natively in Amazon SageMaker Processing
Amazon2 months ago
Build a cold start time series forecasting engine using AutoGluon
Amazon7 months ago
AWS Local Zones Are Now Open in Las Vegas, New York City, and Portland
Amazon5 months ago
Use deep learning frameworks natively in Amazon SageMaker Processing
Amazon11 months ago
Build accurate ML training datasets using point-in-time queries with Amazon SageMaker Feature Store and Apache Spark