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Amazon Polly adds Arabic language support

On April 17th, 2019 Amazon Polly launched an Arabic female text-to-speech (TTS) voice called Zeina. This voice is clear and natural-sounding. The voice masters tongue twisters, and it can whisper, just like all other Amazon Polly products. Let’s hear Zeina introduce herself:

Listen now

Voiced by Amazon Polly

Hello, my name is Zeina, I am the Arabic Amazon Polly voice. Very nice to meet you.

مَرْحَباً، اِسْمِي زينة، أَنا اَلْصَوْتُ اَلْعَرَبِيُّ فِي أمازون بولي، سَعِدْتُ بِلِقائِكُم.

And here’s a tongue twister to demonstrate Zeina’s strengths:

Listen now

Voiced by Amazon Polly

The prince of princes ordered to drill a well in the desert, how many R’s in this sentence?

أَمَرَ أَمِيرُ اَلْأُمَراءِ، بِحَفْرِ بِئْرٍ فِي اَلْصَحْراءِ. فَكَمْ راءً فِي ذٰلِكَ؟

Arabic is one of the most widely spoken languages in the world, but – it’s not really a single language at all. It consists of 30 dialects, including its universal form, which is Modern Standard Arabic (MSA). As a result, it’s classified as a macrolanguage and is estimated to be used by over 400 million speakers. Zeina follows the MSA pronunciation, which is the common broadcasting standard across the region. MSA might sometimes sound formal because it differs from day-to-day speaking style. However, it’s the linguistic thread that links the Arabic native-speakers worldwide.

Arabic is written from right to left and includes 28 letters. Short vowels (diacritics) are not part of the Arabic alphabet. As a result, one written form might be pronounced in several different ways with every option carrying its own meaning and representing a different part of speech. Vocalization can’t be performed in isolation because correct pronunciation depends heavily on the linguistic context of each word. In a real life situation Arabic readers add diacritics during reading to disambiguate words and to pronounce them correctly. In the TTS voice development process Arabic requires a diacritizer that predicts the diacritics. The Amazon Arabic TTS voice handles unvocalized Arabic content thanks to the in-build diacritizer. If a customer provides vocalized input, Zeina generates the corresponding audio as well.

Emirates NBD, one of the leading banks in the Middle East, is using Amazon Polly to develop new voice banking solutions to better serve its customers. Suvo Sarkar, Senior Executive Vice President and Group Head – Retail Banking & Wealth Management said, “Emirates NBD has been an early mover in the region in introducing an AI powered virtual assistant, helping customers calling the bank to converse in natural language and access required services quickly. We are now integrating Amazon Polly in English with our automated call center for its quality and lifelike voice and to further enhance customer interactions, and looking to integrate Amazon Polly in Arabic soon. Such technologies will also help us improve our internal efficiencies while delivering better customer experiences.”

“The launch of Arabic support for Amazon Polly comes at a great time as we are gearing up to launch Arabic as a new language on Duolingo. Zeina delivers accurate and natural sounding speech that is important for teaching a language, and matches the quality that we’ve become accustomed to using Amazon Polly for the other languages that we offer,” said Hope Wilson, Learning Scientist at Duolingo – a globally operating eLearning platform offering a portfolio of 84 language courses for more than 30 distinct languages.

“Amazon Polly’s Arabic voice Zeina is impressive,” said Andreas Dolinsek, CTO at iTranslate, a leading translation and dictionary app that offers text (or even object) translation as well as voice-to-voice conversations in over 100 languages. Andreas noted that “we’re taking it into production immediately to replace our current solution, as it will bring vast improvements to the text-to-speech Arabic service that we are offering.”

Amazon Polly is a cloud service that uses advanced deep learning technologies to offer a range of 59 voices in 29 languages to convert written content into human-like speech. The service supports companies in developing digital products that use speech synthesis for a variety of use cases, including automated contact centers, language learning platforms, translation apps, and reading of articles.

 


About the Author

Marta Smolarek is a Program Manager in the Amazon Text-to-Speech team. At work she connects the dots. In her spare time, she loves to go camping with her family.

 

 

 

 

Your guide to Amazon re:MARS: Jeff Bezos, Andrew Ng, Robert Downey Jr. and more…  

The inaugural Amazon re:MARS event pairs the best of what’s possible today with perspectives on the future of machine learning, automation, robotics, and space travel. Based on the exclusive MARS event founded by Jeff Bezos, Amazon re:MARS brings together the world of business and technology in a premier thought-leadership event. With more than 100 sessions, business leaders have the opportunity to hear best practices for implementing emerging technology for business value. For developers, re:MARS offers technical breakout sessions and hands-on workshops that dive deep into AI and robotics tools from AWS and Amazon Alexa.

You’ll also hear from leading experts across science, academia, and business. Speakers such as Jeff Bezos, founder and CEO of Amazon; Andrew Ng, founder and CEO of deeplearning.ai and Landing AI; Robert Downey Jr, actor and producer; and Colin Angle, chairman, CEO and founder of iRobot, will share the latest research and scientific advancements, industry innovations, and their perspectives on how these domains will evolve.

Register today for Amazon re:MARS and visit the session catalog for the latest lineup! There’s a lot in the works. Here’s a taste of the breakout topics and technical content for beginners and advanced technical builders.

Cross-industry sessions for decision makers and technical builders

Precision medicine for healthier lives
Keith Bigelow, GM of Analytics, GE Healthcare

GE Healthcare has developed machine learning models using Amazon SageMaker to track and predict brain devel­opment in a growing fetus. Powered by these models, the new offering SonoCNS drives the placement of the probe to evaluate congenital and neurological issues in the fetus, for example, to accurately measure and understand brain volume growth. Using Amazon SageMaker for machine learning, GE Healthcare’s operators can quickly detect abnormalities, helping to save babies’ lives and give parents peace of mind. For hospitals, this also translates to improved productivity, efficiency, and accuracy.

Intelligent identity and access management
Paul Hurlocker, Vice President, Center for Machine Learning, Capital One

As one of the largest banks in the U.S., Capital One prioritizes a responsible and well-managed data environment and ecosystem. To meet these needs, Capital One has combined machine learning and native AWS graph capabilities to build a platform that proactively informs access levels for individual associates and teams. The platform results in a faster, enhanced on-boarding process, workflow, and productivity for associates, and helps mitigate risk through proactive management of privileges and licenses.

A hype-free and cutting-edge discussion on autonomous driving
Matthew Johnson-Roberson, Associate Professor, University of Michigan

How close are we to fully autonomous vehicles? What would happen if we put the current technology on the road today? What are the problems that still need to be solved? This session will cover the latest advances in self-driving cars without the marketing, providing a true picture of how far we are from never touching a steering wheel again.

How TED uses AI to spread ideas farther and faster
Jenny Zurawell, Director, & Helena Batt, Deputy Director, TED Translators

TED Talks are a powerful way to share ideas and spark dialogue. To make TED content accessible, volunteer trans­lators need to subtitle more than 300,000 minutes of video this year alone. See how TED leverages Amazon Tran­scribe and Amazon Translate to speed up the creation of crowdsourced subtitles, expand the online reach of ideas, and transform subtitle production in media.

From seed to store: Using AI to optimize the indoor farms of the future
Irving Fain, Co-founder and CEO, Bowery Farms

For the last 10,000 years, large-scale agriculture has lived outdoors, optimized to withstand unpredictable environ­mental conditions and long supply chains. But what possibilities do you unlock when you can control every single environmental factor, from the light intensity to nutrient mix to air flow? In this talk, learn how Bowery Farms uses machine learning and computer vision to optimize indoor vertical farms and scale agricultural production to create higher yielding, better tasting, safer, and more sustainable locally-grown produce in cities around the world.

Futuring the farm to improve crop health
Peri Subrahmanya, IoT Product Manager, & Craig Williams, Principal Solution Architect, Bayer Crop Science

One third of all food produced globally is lost or wasted before it is consumed, according to the Food and Agriculture Organization of the United Nations (FAO). This equals a loss of $750 billion annually. With AWS IoT, Bayer Crop Sci­ence can prevent process loss in real time and use real-time data collection and analysis for its global seed business, collecting an average of one million traits per day during planting or harvest season.

AI, spatial understanding, robots, and the smart home
Chris Jones, Chief Technology Officer, iRobot

Consumers increasingly expect connected products in their home to deliver easy-to-use and personalized experiences tailored to their home and activity. To deliver such a personalized experience, the smart home needs to intelligently coordinate diverse connected devices located throughout the home. This talk will focus on how robots operating in homes today are ideally positioned to enable this intelligence by providing a constantly updated under­standing of the physical layout of the home and the locations of each connected device within the space.

Predicting weather to save energy costs
Andrew Stypa, Lead AI/ML Business Analyst, & Richard Scott, Global Marketing Director, Kinect Energy Group

Learn how Kinect Energy Group uses advanced machine learning capabilities to predict electric spot prices for re­gional power markets using the Amazon SageMaker DeepAR time-series forecasting model, incorporating historical pricing and weather data to drive the machine learning models. Improved price predictions assist with increased trading volumes for forward pricing contracts.

Creating the intelligent asset: Fusing IoT, robotics, and AI
Jason Crusan, Vice President of Technology, Woodside Energy

What if you could learn more about your facility from your tablet than by walking around it yourself? Through 4D interactive virtual worlds, Woodside’s “Intelligent Asset” offers an immersive experience in which operators can explore their facility remotely in real time. Learn how Woodside, the leading energy provider in Australia, combined the latest AWS services including Amazon Kinesis Video Streams, Amazon SageMaker, AWS RoboMaker, and AWS IoT.

Distributed AI: Alexa living on the edge
Ariya Rastrow, Principal Applied Scientist, Alexa Speech

Distributed edge learning, which leverages on-device computation for training models and centrally aggregated an­onymized updates, is a promising new approach capable of achieving customer-level personalization at scale while addressing privacy and trust concerns. Practitioners, employers, and users of AI should understand this new edge-first paradigm and how it will impact the discipline in the near future.

Applying space-based data and machine learning to the UN’s sustainability goals
Dr. Shay Har-Noy, Vice President, Maxar Technologies

The United Nations has outlined 17 Sustainable Development Goals that address global challenges such as poverty, hunger, and health. While the goals are focused on life on Earth, space-based data and machine learning are yield­ing insights. Learn how a combination of analytics and satellite imagery are helping solve pressing problems.

Enabling sustainable human life in space using AI
Dr. Natalie Rens, CEO & Founder, Astreia

Human settlement of space will pose one of the grandest challenges in history. We imagine entire communities living on the moon and beyond without depending on constant supervision or support from Earth. We’ll discuss the challenges for sustainable life in space, and our plan to use artificial intelligence to ensure the safety and wellbeing of our first space settlers.

From business intelligence to artificial intelligence
Elizabeth Gonzalez, Business Intelligence and Advanced Analytics Leader, INVISTA

INVISTA is a manufacturer of chemicals, polymers, fabrics, and fibers and delivers products and brands incorporated into your clothing, your car, and even your carpet. Join this session to learn about INVISTA’s transformative journey from BI to AI, where they will share their experience empowering data science by adjusting talent and processes and building a modern analytics platform. Experimentation with change manage­ment, project management, model maintenance, and development lifecycles has helped drive profitable innova­tions.

When SpongeBob met Alexa
Zach Johnson, Founder and CEO Xandra, Tim Adams VP, Emerging Products Lab, Viacom

Viacom and Xandra are collaborating to push the limits of voice design with a focus on exceptional user experience. Nickelodeon’s SpongeBob Challenge is one of the highest-rated Alexa skills for kids. Learn how to build delight and fun into an Alexa skill through conversation design, rich soundscapes, advanced game mechanics, and analytics.

Amazon Go: A technology deep dive
Ameet Vaswani, Senior Manager, Software Development, & Gerard Medioni Director, Research, Amazon Go

This technical session will outline the core technologies behind the custom-built Just Walk Out technology for Ama­zon Go. Learn about the algorithmic challenges in building a highly accurate customer-facing application using deep learning and computer vision, and the technical details of the high throughput services for Amazon Go that transfer gigabytes of video from stores to cloud systems.

Mitigate bias in machine learning models
Stefano Soatto, Director of Applied Science, AWS

Using real-world examples, this session will explore how to understand, measure, and systematically mitigate bias in machine learning models. Understanding these principles is an important part of building a machine learning strat­egy. This session will cover both the business and technical considerations.

Realizing nature’s secrets to make bug-like robots
Kaushik Jayaram, Postdoc Scholar, Harvard University

This session will take a closer look at the incredible bodies of cockroaches, geckos, and other small animals to exam­ine what it can teach robotics engineers. The session will also outline the latest developments in the field of mi­crorobotics, real-world applications of these robots, and hint at how close (or far) we are from realizing predictions from science fiction.

A chance encounter, sushi, robots, and the environment
Dr. Erika Angle, Co-Founder, Director of Education, Ixcela

Robots can help save our fragile planet. This session discusses the importance of leveraging robotic technology to save our oceans, detailing efforts underway to create an affordable, unmanned undersea robot designed to dive 1000 feet deep and control the lionfish popula­tion. Intended for use by fisherman, tourists and environmentalists, the RSE Guardian robot will address a serious environmental problem by creating an economically scalable solution for catching lionfish, establishing a new food source, and inspiring future generations in the process.

The open-source bionic leg: Constructing and controlling a prosthesis driven by AI
Elliott J. Rouse, Assistant Professor, Mechanical Engineering, University of Michigan

For decades, sci-fi movies have shown the promise of life with bionic limbs, but they are nowhere to be seen in to­day’s society. We have created an open-source bionic leg to help transform these robots from fiction to reality. This talk will focus on innovations in our design approach and showcase a leading AI-based control strategy for wearable robots. Finally, we’ll demo our open-source bionic leg in action with a participant on stage.

Solving Earth’s biggest problems with a cloud in space
Yvonne Hodge, Vice President of IT, Lockheed Martin Space

Can a cloud in space impact the world’s poverty? Are there ways to make agriculture more efficient? Can internet connectivity for the world change how the world lives? Join this interactive discussion as we consider new approach­es to solving Earth’s problems including how a cloud in space could positively impact our lives using space data.

Where will the road to space take you?
Patrick Zeitouni, Head of Advanced Development Programs, Blue Origin

This year, Blue Origin will send its first astronauts to space on its New Shepard rocket. Democratization of space is key to the company’s long-term mission to enable a future where millions of people are living and working in space, moving heavy industry off Earth to protect and preserve the planet for generations to come. To achieve this, the cost of access to space must be lowered, which is why Blue Origin is focusing on the development of operational­ly reusable rockets to send more humans to space than ever before. Join Patrick Zeitouni, the head of Advanced Development Programs for Blue Origin, on the journey to that future. Hear about operational reuse at work and the important part the Moon plays in humanity realizing this bright future.

AI and Robotics workshops for technical builders

Get started with machine learning using AWS DeepRacer
Ever wondered what it takes to create an autonomous race car? Come join us for this half-day workshop, and you’ll get hands-on experience with reinforcement learning. Developers with no prior machine learning experience will learn new skills and apply their knowledge in a fun and exciting way. You’ll join a pit crew where you will build and train machine learning models that you can then try out with our AWS DeepRacer autonomous race cars! Please bring your laptop and start your engines, the race is on!

Practical machine learning with Amazon SageMaker
Until recently, developing machine learning models took considerable time, effort, and expertise. In this workshop, you’ll learn a simple end-to-end approach to machine learning, from how to select the right algorithms and models for your business needs, how to prepare your data, then how to build, train, and deploy optimized models. Upon completion of this full-day workshop, you’ll have learned the latest machine learning concepts such as reinforcement learning and deep learning by using Amazon SageMaker for predictive insights.

re:Vegas Blackjack
In this session, you’ll use computer vision and machine learning to help your team win the re:MARS Blackjack Challenge. During this half-day course, you’ll form teams to build and train a neural network for computer vision using Amazon SageMaker, and develop an algorithm to make decisions that give your team the best chance to win. The team with the highest simulated earnings will win the re:MARS Blackjack Challenge and a coveted patch commemorating their experience.

Get started with robotics and AI
Teach a robot how to find a needle in a haystack. In this workshop, you’ll learn how to develop a robot that can roam around a room and identify objects it encounters, searching for a specific type of item. You will get hands-on with AWS RoboMaker, and learn how to connect robots to a huge variety of other AWS services, like Amazon Rekognition and Amazon Kinesis. Upon completion, you’ll have trained a robot to find what you’re looking for in a pile of irrelevant data.

Voice control for any “thing”
From microwaves to cars, we are headed towards a future surrounded by devices that can communicate with the world around them. In this hands-on session, you will learn how to add custom voice control to your connected devices with Alexa. Leave your laptop behind—bring your big ideas, and we’ll supply the hardware. You’ll create an Alexa built-in prototype, AWS IoT “thing,” and your own Alexa skill—all on a Raspberry Pi. You’ll walk out with your own voice-enabled prototype that interfaces with whatever inputs and outputs you can imagine.

Building the Starship Enterprise computer today
Nearly every science fiction story has shown us that voice interfaces are the future. This workshop will show you how to make that science fiction a reality. Bring your laptop, because this hands-on session will teach you the advanced topics required for creating compelling voice interfaces. You will learn how to build Alexa skills, how to design conversational experiences, and how to your brand and monetize your best content.

 


About the author

Cynthya Peranandam is a Principal Marketing Manager for AWS artificial intelligence solutions, helping customers use deep learning to provide business value. In her spare time she likes to run and listen to music.

 

 

Developer at the AWS DeepRacer League Singapore race sets new world record lap time

The AWS DeepRacer League, the world’s first autonomous racing league open to developers of all skill levels held a race in Singapore this week (April 10-11). This was the third of twenty races on the worldwide Summit Circuit.  Following the first two races in Santa Clara, California and Paris, France, excitement was building to see what the Singapore developer community could deliver. And they sure delivered, with the Singapore Champion Juv Chan setting a new world record lap time of 9.090 seconds. In fact, the top seven lap times on the Singapore Summit leaderboard all beat the prior leaderboard top spot (which was 10.43 seconds from Chris Miller in the Santa Clara race). Nice work Singapore!

Juv Chan’s AWS DeepRacer experience started back in November 2018, “I heard about AWS DeepRacer when it was launched at re:Invent 2018 and thought that this is a very interesting way to learn RL,” he said. The moment the Singapore Summit doors opened, Juv was the first racer on the track, setting the pace with a 12.930 second lap using one of the AWS-provided sample reinforcement learning (RL) models.

Getting that hands-on experience at the tracks fueled Juv’s desire to learn more, so he headed to the AWS DeepRacer workshop to dive into how to build his own custom RL model. This marked the beginning of a 24-hour learning and racing extravaganza for him! “I work as an AI developer for my job, but this is my first time exposed to RL. It’s really engaging and addictive,” said Juv.

Juv went home that night determined. He wanted to learn all he could about how to optimize his model further, so he took the AWS DeepRacer: Driven by Reinforcement Learning online training, where he found more tips and tricks on how to climb the leaderboard. Next, Juv put his new knowledge to the test by tweaking hyperparameters and tuning his model, then he trained it for 12 hours to get race-ready.

The AWS DeepRacer Singapore Speedway

The competition was hot on the second day where the rubber really hit the road for Juv and his DeepRacer model. He was first on the track again and immediately took the top spot with a 10.88 second lap. But, he made no assumption that this was enough to win and headed back to his laptop to continue optimizing his model performance. He was soon knocked off as more developers came with their custom models, and lap times in the 9-10 second range were recorded. At one point in the race Juv dropped down to 10th place on the leaderboard. Juv shared the philosophy behind his approach, “Fail fast, learn from mistakes and keep trying.” With that in mind, he came back to race two more times to secure the win. And secure the win he did, with 10 minutes of race time left he threw caution to the wind with the throttle and was victorious with a winning lap time of 09.090 seconds. Congratulations Juv!

Juv won a trip to compete in the AWS DeepRacer League finals at re:Invent 2019 in Las Vegas. I wonder if his 9.090 lap will still be the world record holder then? Developers, this is the time to beat!

The Singapore Summit Winners Podium

Tshiamo Rakgowa, a robotics enthusiast was the first runner-up, with a lap time of 9.420. He was followed closely by Wang Teng Lee, a software engineer with a 9.590 lap (+ 0.17 seconds back).  Both of these gentlemen also tuned and raced their models multiples times, experimenting their way to top spots on the leaderboard. The similarities don’t end there. By coincidence it turns out that all three of the leading racers are connected to a town called Kepong in Malaysia. In fact, it’s Juv and Wang’s childhood home town (they live in Singapore now), and it’s where Tshiamo calls home right now. Congratulations to Tshiamo and Wang, it was a very close race! Don’t forget we still have 17 races to go with 4 in Asia, including Seoul on April 17, Tokyo and Taipei both on June 12, and Hong Kong on June 26.

The Singapore Summit Winners: Juv Chan (center) Singapore Summit Champion, Tshiamo Rakgowa (left) First Runner Up, Wang Teng Lee (right) Second Runner Up

One day, three countries, three live races – Amsterdam, Dubai, and Seoul on April 17

On April 17, the AWS DeepRacer League will hold three AWS Summit races, on three different continents, all on one day. The Summits offer the opportunity to get hands-on with AWS DeepRacer. There will be multiple workshops and hours of live racing. You can register to attend now, and follow the action live on the day at www.deepracerleague.com. Coming soon is the AWS DeepRacer Virtual League. Get your first model ready today by taking the digital training course for reinforcement learning and AWS DeepRacer.

Developers, start your engines! Your journey to becoming a machine learning developer begins with the AWS DeepRacer League.

 


About the Author

Sally Revell is a Principal Product Marketing Manager for AWS DeepLens. She loves to work on innovative products that have the potential to impact people’s lives in a positive way. In her spare time, she loves to do yoga, horseback riding and being outdoors in the beauty of the Pacific Northwest.

 

 

Protagonist adopts Amazon Translate to expand analytics to multilingual content

This is a guest blog post by Bryan Pelley, COO of Protagonist. Protagonist, in their own words “helps organizations communicate more effectively through a data-driven understanding of public discourse.”

Protagonist is a pioneer of the art and science of understanding narratives. We define narratives as the beliefs that an audience holds that are  composed of an interrelated set of concepts, themes, images, and ideas that coalesce into a story. Narratives matter because they reflect the deeply held needs, wants, and desires that weigh heavily, both consciously and unconsciously, on human decision-making. Using Amazon Translate, Protagonist can analyze narratives in languages other than English, which enables us to win global customers.

The Protagonist Narrative Analytics platform uses natural language processing (NLP) and machine learning (ML), guided by human expertise, to surface, measure, and track the narratives that matter to our customers across traditional, social, and other types of online media. The following diagram illustrates our Narrative Analytics solution.

Protagonist has been limited, with a few exceptions, to analyzing English-only content, which we’ve seen as a limitation on the long-term growth of our business. Numerous customers and prospective customers have expressed serious interest in projects involving international narratives.  To create these narratives we would need to work with native language content.

In the past, we were able to do a small number of projects in foreign languages, primarily French and Spanish, thanks to fluent speakers on staff. In these cases, our team would either run the analysis on the content without translation, which limited the range of NLP tools we were able to use. Or, we manually translated a sample set of the overall corpus of content and ran our full suite of tools on the translated set. Sometimes we used a combination of both processes. However, this staff-based manual solution didn’t scale, and it was not efficient. Manually translating a sample of 1,000 media articles took about two weeks. This was a significant delay in providing timely narrative analysis to our customers.

Amazon Translate has changed that for us, enabling us to quickly and effectively translate multilingual content into English for analysis on our narrative platform. We tried a few other machine translation services in the past, but were unhappy with the performance, cost, and, in some cases, the requirement to commit to a long-term contract. Amazon Translate gives us the right combination of speed, accuracy of translation, cost effectiveness, and on-demand flexibility to meet our needs. What used to take two weeks or more to translate now can be done in minutes using Amazon Translate.

We piloted the Amazon Translate service in 2018 on a project for one of our customers, Omidyar Network (ON). One of ON’s major focus areas is property rights. They want to address the fact that a large percentage of the world’s population has limited or nonexistent protections for their property and resources. Naturally, to address a global issue like this, ON wants to understand the narratives that local populations around the world have about their rights, or lack of rights, to land and other property. Using international English language media sources, we were able to help ON gain an understanding of the narratives at play. As the following illustration indicates, analysis of English-only content showed that property rights narratives differed significantly by region, which prompted a desire for a deeper analysis of content in local native languages. For this reason, we saw ON’s property rights work as an ideal place to test Amazon Translate.

Peter Rabley, Venture Partner at Omidyar Network, describes their property rights efforts and the role of Protagonist:

“More than one billion people around the world lack legal rights to their land and property. However, it’s an issue that not enough people pay attention to because it seems too complicated, too complex to wrap your head around. We believe that by simplifying language and telling human interest stories, those in the field can raise greater awareness of the need—sparking innovative solutions, more financing and greater overall engagement. We needed a way to see what the initial conversations around property rights looked like globally in more than one language, and understand how better storytelling may have impacted those conversations over time. This is what Protagonist’s Narrative Analytics allows us to do, helping underscore the value of our investments and unlocking valuable insights for all of us working to advance property rights around the world. Importantly, Protagonist has been able to provide its Narrative Analytics in multiple languages including Spanish.”

As Peter notes, we initially chose to work with Amazon Translate on Spanish language content. We had experience working with Spanish content in the past and access to fluent Spanish speakers, so we could double-check the Amazon Translate outputs and easily identify and troubleshoot issues as they arose. Ultimately, that was not needed because the accuracy of the translations performed by the Amazon Translate service performed was high.

The performance of the Amazon Translate service met or exceeded our expectations. Initially, the API’s rate limit caused some concurrency issues for us because we kept unknowingly exceeding the limit. Since our pilot of the Amazon Translate service, AWS has added a metrics dashboard to the AWS Management Console that makes it easy for us to know if we’re exceeding the rate limit and make adjustments as necessary.

We noticed that AWS has been very thoughtful in keeping Amazon Translate API parameters very flexible, so that when languages are added we can easily integrate the newly supported languages in our data workflows. Specifically, AWS keeps the Python package Boto3 very stable, which allows us to update to the latest version of Boto3 without the worry of breaking existing functionality.

Overall, using Amazon Translate provided several advantages over our previous human-based translation solution. We were able to eliminate the need for time-consuming manual translation. Amazon Translate was able to complete in a matter of minutes translation tasks that would have taken us 60 hours or more in the past. This meant we could expand the amount of content we analyzed with our full suite of tools from a sample of a few hundred articles to tens or hundreds of thousands of articles. We were able to effectively leverage our NLP tools that were trained with only English language corpuses, such as Narrative Richness, cluster analysis, sentiment scoring, and topic modeling. The ability to accurately analyze large amounts of foreign language content using our English-language-trained NLP tools on the translated materials represents a significant cost and time savings for us.

But perhaps most importantly, Amazon Translate provides cost-effective access to a range of languages that we haven’t been able to work with before, including Arabic, Chinese, and Russian. This opens up a wide range of customers and opportunities that we couldn’t have supported before. We’re in active discussions with several large customers on global narrative projects that would make extensive use of the Amazon Translate capabilities. We’re excited to continue working with Amazon Translate and exploring the new opportunities that the service brings.

 

Get started with the AWS Live Streaming with Automated Multi-Language Subtitling solution

Live Streaming with Automated Multi-Language Subtitling is a solution that automatically generates multi-language subtitles for live streaming video content in real time. You can use this solution as-is, customize the solution to meet your specific use case, or work with AWS Partner Network (APN) partners to implement an end-to-end subtitling workflow.

Based on the Live Streaming on AWS solution, the implementation adds machine learning services Amazon Transcribe and Amazon Translate into the mix. The solution enables the last-mile addition of automatically generated subtitles to live over the top (OTT) channels without having to hire a dedicated transcriptionist, which could be too costly to make subtitles available in general. The solution is available as open source for anyone who wants to expand the basic architecture, adding custom features to fit the solution into their workflow. The GitHub repository can be found here.

Additional AWS Solutions offerings are available on the AWS Solutions webpage, where customers can browse solutions by product category or industry to find AWS-vetted, automated, and turnkey reference implementations that address specific business needs.

Note: The solution described in this blog post uses Amazon Transcribe Streaming, AWS MediaLive, and AWS MediaPackage, which are currently available only in specific AWS Regions. Therefore, you must launch this solution in an AWS Region where all of these services are available. For the most current AWS service availability by Region, see AWS service offerings by region.

Step 1: Deploy the Live Streaming with Automated Multi-Language Subtitling solution

Sign into the AWS Management Console and then head over to the Live Streaming with Automated Multi-Language Subtitling Solution page. Choose Launch solution in the AWS Console.

Step 2: Launch the AWS CloudFormation template

The stack can also be launched with the Launch Solution in the documentation guide.

Step 3: On the Select Template page, choose Next

Step 4: Input information on the Specify Details page

  1. Choose a name for your stack.
  2. Choose what input format you want to use.
  3. If you are using HLS pull put in your input URLs. Example: https://s3.amazonaws.com/yourbucketname/index.m3u8
  4. Choose the languages you want as subtitles. For example if you want English, Spanish, and German you would enter: en, es, de.

The supported output subtitle languages are listed here. For information on the inputs see the documentation guide.

Step 5: On the Options page, choose Next

Choose the Next button on the options page.

Then, check that you accept that AWS CloudFormation will create IAM resources and choose Create. 

Note that this CloudFormation takes about 20 minutes to deploy.

Step 6: Solution should show deployed now

You should see CREATE_COMPLETE in the status area.

The screenshot of the solution deployed page should say CREATE_COMPLETE under the status area for the solution.

After waiting a minute for the AWS MediaLive channel to start you can copy and paste the HLSEndpoint URL ending in m3u8 into Safari or an online test player, such as Video.JS.

I took the HLS stream output ending in m3u8 and pasted it into my Safari browser search bar. The subtitle selector on the bottom right allows a user to select different languages for the subtitles.

Conclusion

We have shown you how easy it is to set up your Live Stream with automatically generated subtitles from Amazon Transcribe. For more information about AWS Media Services or this solution follow these links:


About the Author

Eddie Goynes is a Technical Marketing Engineer for AWS Elemental. He is an AWS Cloud and Live Video Streaming technical expert.

 

 

 

Extending Amazon SageMaker factorization machines algorithm to predict top x recommendations

Amazon SageMaker gives you the flexibility that you need to address sophisticated business problems with your machine learning workloads. Built-in algorithms help you get started quickly.  In this blog post we’ll outline how you can extend the built-in factorization machines algorithm to predict top x recommendations.

This approach is ideal when you want to generate a set number of recommendations for users in a batch fashion. For example, you can use this approach to generate the top 20 products that a user is likely to buy from a large set of users and product purchase information. You can then store the recommendations in a database for further use, such as dashboard display or personalized email marketing. You can also automate the steps outlined in this blog for periodic retraining and prediction using AWS Batch or AWS Step Functions.

A factorization machine is a general-purpose supervised learning algorithm that you can use for both classification and regression tasks. This algorithm was designed as an engine for recommendation systems. It extends the collaborative filtering approach by learning a quadratic function over the features while restricting second order coefficients to a low rank structure. This restriction is well-suited for large and sparse data because it avoids overfitting and is highly scalable, so that a typical recommendation problem with millions of input features will have millions of parameters rather than trillions

The model equation for factorization machines is defined as:

Model parameters to be estimated are:

where, n is the input size and k is the size of the latent space. These estimated model parameters are used to extend the model.

Model extension

The Amazon SageMaker factorization machines algorithm allows you to predict a score for a pair, such as user, item, based on how well the pair matches. When you apply a recommendation model, you often want to provide a user as input and receive a list of the top x items that best match the user’s preferences. When the number of items is moderate, you can do this by querying the model for user, item for all possible items. However, this approach doesn’t scale well when the number of items is large. In this scenario, you can use the Amazon SageMaker k-nearest neighbors (k-NN) algorithm to speed up top x prediction tasks.

The following diagram provides a high-level overview of the steps covered in this blog post, which include building a factorization machines model, repackaging model data, fitting a k-NN model, and producing top x predictions.

You can also download a companion Jupyter notebook to follow along. Each of the following sections corresponds to a section in the notebook so that you can run the code for each step as you read.

Step 1: Building a factorization machines model

See Part 1 of the companion Jupyter notebook for steps to build a factorization machines model. To learn more about building factorization machines models, see the Factorization Machines documentation.

Step 2: Repackaging model data

The Amazon SageMaker factorization machines algorithm leverages Apache MXNet deep learning framework. In this section, we’ll cover how to repackage the model data using MXNet. 

Extract the factorization machines model

First, you’ll download the factorization model, and then you’ll decompress it for constructing an MXNet object. The main purpose of the MXNet object is to extract the model data.

#Download FM model 
os.system('aws s3 cp '+{Model location} + './')

#Extract files from the model. Note: the companion notebook outlines the extraction steps.

Extract model data

The input to a factorization machines model is a list of vectors xu + xi representing user u and item i coupled with a label, such as a user rating for a movie. The resulting input matrix will include sparse one-hot encoded values for users, items, and any additional features you may want to add.

The factorization machines model output consists of three N-dimensional arrays (ndarrays):

  • V – a (N x k) matrix, where:
    • k is the dimension of the latent space
    • N is the total count of users and items
  • w – an N-dimensional vector
  • b – a single number: the bias term

Complete the steps below to extract the model output from the MXNet object.

#Extract model data
m = mx.module.Module.load('./model', 0, False, label_names=['out_label'])
V = m._arg_params['v'].asnumpy()
w = m._arg_params['w1_weight'].asnumpy()
b = m._arg_params['w0_weight'].asnumpy()

Prepare data to build a k-NN model

Now you can repackage the model data extracted from the factorization machines model to build a k-NN model. This process will create two datasets:

  • Item latent matrix – for building the k-NN model
  • User latent matrix – for inference
nb_users = <num users>
nb_movies = <num items>

# item latent matrix - concat(V[i], w[i]).  
knn_item_matrix = np.concatenate((V[nb_users:], w[nb_users:]), axis=1)
knn_train_label = np.arange(1,nb_movies+1)

#user latent matrix - concat (V[u], 1) 
ones = np.ones(nb_users).reshape((nb_users, 1))
knn_user_matrix = np.concatenate((V[:nb_users], ones), axis=1)

Step 3: Fitting a k-NN model

Now you can upload the k-NN model input data to Amazon S3, create a k-NN model, and save it so that it can be used in Amazon SageMaker. The model will also come in handy for calling batch transforms, as described in the following steps.

The k-NN model uses the default index_type (faiss.Flat). This model is precise, but it can be slow for large datasets. In such cases, you may want to use a different index_type parameter for an approximate but faster answer. For more information about index types, see either the k-NN documentation or this Amazon Sagemaker Examples notebook.

#upload data
knn_train_data_path = writeDatasetToProtobuf(knn_item_matrix, bucket, knn_prefix, train_key, "dense", knn_train_label)

# set up the estimator
nb_recommendations = 100
knn = sagemaker.estimator.Estimator(get_image_uri(boto3.Session().region_name, "knn"),
    get_execution_role(),
    train_instance_count=1,
    train_instance_type=instance_type,
    output_path=knn_output_prefix,
    sagemaker_session=sagemaker.Session())

#set up hyperparameters
knn.set_hyperparameters(feature_dim=knn_item_matrix.shape[1], k=nb_recommendations, index_metric="INNER_PRODUCT", predictor_type='classifier', sample_size=nb_movies)
fit_input = {'train': knn_train_data_path}
knn.fit(fit_input)
knn_model_name =  knn.latest_training_job.job_name
print "created model: ", knn_model_name

# save the model so that you can reference it in the next step during batch inference
sm = boto3.client(service_name='sagemaker')
primary_container = {
    'Image': knn.image_name,
    'ModelDataUrl': knn.model_data,
}
knn_model = sm.create_model(
        ModelName = knn.latest_training_job.job_name,
        ExecutionRoleArn = knn.role,
        PrimaryContainer = primary_container)

Step 4: Predicting Top x recommendations for all users

The Amazon SageMaker batch transform feature lets you generate batch predictions at scale. For this example, you’ll start by uploading user inference input to Amazon S3, and then you’ll trigger a batch transform.

#upload inference data to S3
knn_batch_data_path = writeDatasetToProtobuf(knn_user_matrix, bucket, knn_prefix, train_key, "dense")
print "Batch inference data path: ",knn_batch_data_path

# Initialize the transformer object
transformer =sagemaker.transformer.Transformer(
    base_transform_job_name="knn",
    model_name=knn_model_name,
    instance_count=1,
    instance_type=instance_type,
    output_path=knn_output_prefix,
    accept="application/jsonlines; verbose=true"
)

# Start a transform job:
transformer.transform(knn_batch_data_path, content_type='application/x-recordio-protobuf')
transformer.wait()

# Download output file from s3
s3_client.download_file(bucket, inference_output_file, results_file_name)

The resulting output file will contain predictions for all users. Each line item in the output file is a JSON line containing item IDs and distances for a specific user.

Here’s a sample output for a user. You can store the recommended movie IDs to your database for further use.

Recommended movie IDs for user #1 : [509, 1007, 96, 210, 208, 505, 268, 429, 182, 189, 57, 132, 482, 165, 615, 527, 196, 269, 528, 83, 176, 166, 194, 520, 661, 246, 180, 659, 496, 173, 9, 435, 474, 192, 493, 48, 211, 656, 489, 181, 251, 124, 89, 510, 22, 183, 316, 185, 197, 23, 170, 168, 963, 190, 1039, 56, 79, 136, 519, 651, 484, 275, 654, 641, 523, 478, 302, 223, 313, 187, 1142, 134, 100, 498, 272, 285, 191, 515, 408, 178, 199, 114, 480, 603, 172, 169, 174, 427, 513, 657, 318, 357, 511, 12, 50, 127, 479, 98, 64, 483]

Movie distances for user #1 : [1.8703, 1.8852, 1.8933, 1.905, 1.9166, 1.9185, 1.9206, 1.9239, 1.928, 1.9304, 1.9411, 1.9452, 1.947, 1.9528, 1.963, 1.975, 1.9985, 2.0117, 2.0205, 2.0211, 2.0227, 2.0583, 2.0959, 2.0986, 2.1064, 2.1126, 2.1157, 2.119, 2.1208, 2.124, 2.1349, 2.1356, 2.1413, 2.1423, 2.1521, 2.1577, 2.1618, 2.176, 2.1819, 2.1879, 2.1925, 2.2463, 2.2565, 2.2654, 2.2979, 2.3289, 2.3366, 2.3398, 2.3617, 2.3654, 2.3855, 2.386, 2.3867, 2.4198, 2.4431, 2.46, 2.462, 2.4643, 2.4729, 2.4959, 2.5334, 2.5359, 2.5362, 2.542, 2.5428, 2.5934, 2.5953, 2.598, 2.6575, 2.6735, 2.6879, 2.7038, 2.7259, 2.7432, 2.8112, 2.8707, 2.871, 2.9378, 2.9728, 3.0175, 3.0231, 3.0254, 3.0259, 3.0325, 3.0414, 3.1033, 3.2729, 3.3406, 3.392, 3.3982, 3.4196, 3.4452, 3.4684, 3.4743, 3.6265, 3.7013, 3.7711, 3.7736, 3.8898, 4.0698]

Multiple features and categories scenario

The framework in this blog applies to a scenario with user and item IDs. However, your data may include additional information, such as user and item features. For example, you might know the user’s age, zip code, or gender. For the item, you might have a category, a movie genre, or important keywords from a text description. In these multiple-feature and category scenarios, you can use the following to extract user and item vectors:

  • encode xi with both the users and user features:
    ai =concat(VT · xi , wT · xi)
  • encode xu with items and item features:
    au =concat(VT · xu, 1)

Then use ai to build the k-NN model and au for inference.

Conclusion

Amazon SageMaker gives developers and data scientists the flexibility to build, train, and deploy machine learning models quickly. Using the framework outlined above, you can build a recommendation system for predicting the top x recommendations for users in a batch fashion and cache the output in a database. In some cases you may need to apply further filtering on predictions or filter out some of the predictions based on user responses over the time. This framework is flexible enough to modify for such use cases.


About the Authors

Zohar Karnin is a Principal Scientist in Amazon AI. His research interests are in the areas of large scale and online machine learning algorithms. He develops infinitely scalable machine learning algorithms for Amazon SageMaker.

 

 

 

 

Rama Thamman is a Sr. Solution Architect with the Strategic Accounts team. He works with customers to build scalable cloud and machine learning solutions on AWS.

 

 

 

 

 

 

Amazon Comprehend now supports resource tagging for custom models

Amazon Comprehend customers are solving a variety of use cases with custom classification and entity type models. For example, customers are building classifiers to organize their daily customer feedback into categories like “loyalty,” “sales,” or “product defect.” Custom entity models enable customers to analyze text for their own terms and phrases, such as product IDs from their inventory system. Amazon Comprehend removed the complexity from creating these models. All that’s required is a CSV file with labels and example text.

Because of this big step forward in ease of use, more employees across more teams are creating custom models for their projects. With this proliferation of more models across more teams, you need to be able to itemize usage and costs associated with each model for internal billing and usage management.

With this release, you can now assign resource tags to Amazon Comprehend custom classifier and custom entity models. Tagging these resources helps identify, track, and itemize their usage and costs. For example, there might be one model for sales text analysis and another model for marketing text analysis. With the resource tagging feature, you can provide the tab label on the custom model resource when you create your new models using either the SDK or with no code in the AWS Management Console. When usage and billing gets generated against the model, you can see usage and costs itemized using these resource tags.

You can add resource tags during custom model creation. The following example shows how to add tags to a custom model while you’re preparing to train the model.

To learn more about tagging custom classifiers and custom entity types, read Custom Comprehend.


About the author

Nino Bice is a Sr. Product Manager leading product for Amazon Comprehend, AWS’s natural language processing service.

 

 

 

 

 

 

Amazon SageMaker automatic model tuning now supports random search and hyperparameter scaling

We are excited to introduce two highly requested features to automatic model tuning in Amazon SageMaker: random search and hyperparameter scaling. This post describes these features, explains when and how to enable them, and shows how they can improve your search for hyperparameters that perform well. If you are in a hurry, you’ll be happy to know that the defaults perform very well in most cases. But if you’re curious to know more and want more manual control, keep reading.

If you’re new to Amazon SageMaker automatic model tuning, see the Amazon SageMaker Developer Guide.

For a working example of how to use random search and logarithmic scaling of hyperparameters, see the example Jupyter notebook on GitHub.

Random search

Use random search to tell Amazon SageMaker to choose hyperparameter configurations from a random distribution.

The main advantage of random search is that all jobs can be run in parallel. In contrast, Bayesian optimization, the default tuning method, is a sequential algorithm that learns from past trainings as the tuning job progresses. This highly limits the level of parallelism. The disadvantage of random search is that it typically requires running considerably more training jobs to reach a comparable model quality.

In Amazon SageMaker, enabling random search is as simple as setting the Strategy field to Random when you create a tuning job, as follows:

{
    "ParameterRanges": {...}
    "Strategy": "Random",
    "HyperParameterTuningJobObjective": {...}
}

If you use the AWS SDK for Python (Boto), set strategy="Random" in the HyperparameterTuner class:

tuner = HyperparameterTuner(
    sagemaker_estimator,
    objective_metric_name,
    hyperparameter_ranges,
    max_jobs=20,
    max_parallel_jobs=20,
    strategy="Random"
)

The following plot compares the hyperparameters chosen by random search, on the left, with those chosen by Bayesian optimization, on the right. In this example, we tuned the XGBoost algorithm, using the bank marketing dataset as prepared in our model tuning example notebook. For easy visualization, we tuned just two hyperparameters, alpha and lambda. The color of the visualized points shows the quality of the corresponding models, where yellow corresponds to models with better area under the curve (AUC) scores, and violet indicates a worse AUC.

The plot clearly shows that Bayesian optimization focuses most of its trainings on the region of the search space that produces the best models. Only occasionally does the algorithm explore new, unexplored regions. Random search, on the other hand, chooses the hyperparameters uniformly at random.

The following graph compares the quality of random search and Bayesian optimization on the preceding example. The lines show the best model score so far (on the vertical axes, where lower is better) as more training jobs are performed (on the horizontal axis). Each experiment was replicated 50 times, and the average of these replications was plotted. This is necessary to get accurate results, because the random nature of model-tuning algorithms can have a big effect on tuning performance.

You can see that Bayesian optimization requires one-fourth as many training jobs to reach the same level of performance as random search. You can expect similar results for most tuning jobs.

To see why it’s important to average multiple replications to get reliable results in any comparison, look at the following graphs of single replications. Each run has identical settings, and all variation is due to the internal use of different random seeds. The five samples are taken from the curves averaged in the preceding discussion.

As you can see, hyperparameter tuning curves look very different from other common learning curves seen in machine learning. In particular, they show much greater variance. From just these five samples, you can’t conclude much. If you’re ever in a situation where you’re comparing hyperparameter tuning methods, keep this in mind.

What about grid search? Grid search is similar to random search in that it chooses hyperparameter configurations blindly. But it’s usually less effective because it leads to almost duplicate training jobs if some of the hyperparameters don’t influence the results much.

Hyperparameter scaling

In practice, you often have hyperparameters whose value can meaningfully span multiple orders of magnitude. If I asked you to manually try a few different step sizes for a deep learning algorithm to explore the effect of varying this hyperparameter, you would likely choose powers of 10 (such as 1.0, 0.1, 0.01, …) rather than equidistant values (such as 0.1, 0.2, 0.3, …). We know from experience that the latter is unlikely to change the behavior of the algorithm much. For many hyperparameters, changing the order of magnitude yields much more interesting variation.

To try values that vary in order of magnitude, set a hyperparameter’s scaling type to Logarithmic.

The following graph shows the results of applying log scaling to the hyperparameters used in the preceding example. The left plot shows the results of using random search. The right plot shows the results of using Bayesian optimization.

To manually specify a scaling type, set the ScalingType of hyperparameter ranges to Logarithmic or ReverseLogarithmic (more about this type later). The range definitions for your tuning job configuration will look similar to the following:

"ContinuousParameterRanges": [
    {
      "Name": "learning_rate",
      "MinValue": "0.00001",
      "MaxValue" : "1.0",
      "ScalingType": "Logarithmic"
    },
    ...
]

For the AWS SDK for Python (Boto), the equivalent is:

ContinuousParameter(0.00001, 1.0, scaling_type="Logarithmic")

Reverse log

The momentum hyperparameter, which is common in deep learning, isn’t well served by linear scaling or by plain log scaling. Commonly, you’d want to explore values such as 0.9, 0.99, 0.999, …. In other words, you are interested in values increasingly close to 1.0. In this case, we recommend that you set the ScalingType  to ReverseLogarithmic. This tells Amazon SageMaker to internally apply the transformation log(1.0 - value) to all values.

Automatic scaling

When selecting automatic scaling (the Auto setting), Amazon SageMaker uses log scaling or reverse logarithmic scaling whenever the appropriate choice is clear from the hyperparameter ranges. If not, it falls back to linear scaling.

When using automatic scaling, if you specify 0 as the minimum hyperparameter value, Amazon SageMaker will never choose to use logarithmic scaling. Instead, it is recommended to select log scaling explicitly, and use a minimum value greater than 0. For example, don’t use 0 as the minimum regularization value. Instead, use a value like 1e-8, which is nearly equivalent and allows you to use log scaling.

Warping

The Amazon SageMaker Bayesian optimization engine has an additional internal feature, called warping. Warping is closely related to the configurable scaling options described in this post. Amazon SageMaker applies the internal warping function to each hyperparameter along with any specified scaling types. The warping function is learned as the tuning job progresses depending on what best describes the data. This means that this warping function improves as the tuning job progresses, while hyperparameter scaling is applied from the start.

Internal warping can learn a much larger family of transformations compared with the three transformations supported by hyperparameter scaling, as shown in the following figure. The image on the left shows the three transformations that you can specify by setting the scaling type. The image on the right shows a few examples of transformations that can be learned internally through warping, and which are learned in addition to any scaling type you choose.

Choosing the correct scaling type is particularly important when using random search, because Amazon SageMaker doesn’t apply internal warping when you use random search.

Summary

If you require a higher degree of parallelism than is supported by Bayesian optimization, you can use random search. But keep in mind that, in most cases, it’s more cost effective to use the default Bayesian optimization strategy.

If you are unsure which hyperparameter scaling type to use, stick to automatic scaling. If the hyperparameters can meaningfully vary by multiple orders of magnitude, use logarithmic scaling. If you are interested in values that are increasingly close to 1.0, use reverse logarithmic scaling. Using the correct scaling type can significantly speed up your search for well-performing hyperparameters.


About the Author

Fela Winkelmolen works as an applied scientists for Amazon AI and was part of the team that launched Automatic Model Tuning in Amazon SageMaker.

 

 

 

 

 

 

 

 

Amazon Comprehend now support KMS encryption

Amazon Comprehend is a fully managed natural language processing (NLP) service that enables text analytics for important workloads. For example, analyzing market research reports for key market indicators or data that contains PII information. Customers that work with highly sensitive, encrypted data can now easily enable Comprehend to work with this encrypted data via an integration with the AWS Key Management Service.

AWS KMS makes it easy for you to create and manage keys and control the use of encryption across a wide range of AWS services and in your applications. AWS KMS is a secure and resilient service that uses FIPS 140-2 validated hardware security modules to protect your keys. AWS KMS is integrated with AWS CloudTrail to provide you with logs of all key usage to help meet your regulatory and compliance needs.

To enable Comprehend to use KMS keys to access data, the feature can be configured via the AWS Management console or the SDK and supports Amazon Comprehend asynchronous training and inference jobs. To get started you first need to create a key in the AWS KMS service.  To learn more about how to create KMS keys, please visit: https://docs.aws.amazon.com/kms/latest/developerguide/create-keys.html

When you are configuring an asynchronous job, you can specify the KMS encryption key the Comprehend should use to access your data in S3. Below is an example of selecting a key with the alias “Comprehend” as part of configuring job details, in the Amazon Comprehend console:

To manage your AWS KMS keys, please visit the AWS KMS management portal or use the KMS SDK.  For more information, please visit: AWS Key Management Service. To learn more about how to configure Comprehend jobs to work with KMS keys, please visit our documentation:


About the author

Nino Bice is a Sr. Product Manager leading product for Amazon Comprehend, AWS’s natural language processing service.

 

 

 

 

 

 

AWS DeepRacer League hits the road for more fun and excitement for developers!

From developer to machine learning developer

The AWS DeepRacer League is the world’s first autonomous racing league open to developers of all skill levels and it kicked off last week in Santa Clara, California. Chris Miller was crowned our first champion of the 2019 season. Chris is the founder of Cloud Brigade, based in Santa Cruz, California, and he came to the AWS Summit specifically to learn more about machine learning.

At AWS, we are committed to putting machine learning in the hands of all developers of all skill levels, making their experiences with machine learning fun and easy. At Santa Clara, our top three finishers all built a model in one of the onsite workshops and had a lot of fun doing it.

Chris Miller achieved a winning lap time of 10.43 seconds, and will now be advancing the finals at re:Invent 2019 where he will race to win the AWS DeepRacer Championship Cup. Before he arrived at the AWS Summit, he had no experience with machine learning.

Chris says, “When I got here today, I had no experience with machine learning, but that’s exactly what I came here to learn and what a great way to learn machine learning.”

Rahul Shah from Fremont, California came in second place. He was pleasantly surprised by how successful his model was and had a lot of fun with AWS DeepRacer. Rahul has been working with machine learning for the past few years, but this was his first time working with reinforcement learning.

“Working on this was easy, and any developer would be able to have success. The DeepRacer event is a really fun and exciting thing to do at the AWS Summit,” Rahul said.

The third-place finisher was Adrian Sarno from San Mateo, California. Adrian is a data scientist and has been actively involved with machine learning for most of his career. Attending the workshop and participating in the league was his first experience with reinforcement learning and he was curious to learn this advanced ML technique. Adrian’s first attempt at building his model was not as successful as he wanted it to be. When he realized what was at stake, he took to his keyboard and retrained his model for 2 hours. Then he returned with a model that scored him a podium finish.

Adrian says, “It’s straightforward to work with the applications that have been put together.”

All of our participants are excited to experiment more and use the coming months to get more advanced models ready to compete at re:Invent 2019. There, they can use their new found skills to help them win the AWS DeepRacer Championship Cup. 

Heading to Paris to reach developers globally

And it doesn’t end there. The AWS DeepRacer League made its first international stop at the AWS Summit in Paris, France yesterday. Paris is fast becoming a hub for learning and research on artificial intelligence. The French government has plans to invest in Paris to help enable the AI ecosystem in France and the rest of Europe. Such an investment can encourage a large community of developers to learn with easy access to the tools they need to become machine learning developers just like Chris, Rahul, and Adrian.

Today, at the AWS Summit in Paris, the AWS DeepRacer League welcomed more developers to learn, build, and train models to compete. The podium was filled with developers who came to the Summit to participate in the league and each of them had spent time on their models at home before arriving. Positions changed throughout the afternoon as they learned more. In a tense final 60 minutes of racing, Arthur Pace from Paris, took home the Paris Summit Champion cup with a lap time of 13.87 seconds. Second place went to “JO” (Wajdi Fathallah), who attended a DeepRacer meet up before the AWS Summit and secured a 15.5 second lap. The third place finisher was Matthieu Rousseau (16.00 seconds). Matthieu worked on his model with fellow engineering student (and Paris Champion) Arthur Pace for the last 2 weeks in order to land on the podium!

The 2019 developer journey continues

On April 10, the AWS DeepRacer League will be at the AWS Summit in Singapore. The Summit there offers an opportunity to get hands-on with AWS DeepRacer. There will be multiple workshops and hours of live racing. You can follow the action live on at www.deepracerleague.com. Coming soon is the AWS DeepRacer Virtual League. Get ready today by taking the digital training course for reinforcement learning and AWS DeepRacer.

Developers, start your engines! Your journey to becoming a machine learning developer begins with the AWS DeepRacer League.


About the Author

Alexandra Bush is a Senior Product Marketing Manager for AWS AI. She is passionate about how technology impacts the world around us and enjoys being able to help make it accessible to all. Out of the office she loves to run, travel and stay active in the outdoors with family and friends.

 

 

 

 

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