Analyst Webcast: JumpStart Guide to Cloud-Based Firewalls in AWS – July 31, 2019 10:30am US/Eastern

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Speakers: Brian Russell, Anthony Tanzi and David Aiken

Cloud-based firewalls, along with threat prevention capabilities (including intrusion detection and prevention systems) are key to securing ever-changing organization perimeters. This pre-recorded webcast provides guidance on the key issues to consider when choosing next-generation firewall/threat prevention solutions for integration on the AWS platform and suggests a process for making that important decision.

Attendees will learn about:

  • Cloud design considerations affecting the selection and use of next-generation firewalls and threat protection capabilities
  • Needs and capabilities associated with firewalls and threat prevention capabilities, including intrusion prevention, antivirus, logging and alerting, event correlation, continuous dynamic updating of threat databases and malware protection
  • Key business, technical and operational considerations for cloud-based firewall protection
  • Key AWS-specific considerations for perimeter security that address firewalls and threat prevention
  • Process for making an informed decision about products to integrate

Be among the first to receive the associated whitepaper written by SANS Analyst and cybersecurity specialist Brian Russell.

In conjunction with Optiv www.optiv.com

Amplify Framework Update – Quickly Add Machine Learning Capabilities to Your Web and Mobile Apps

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At AWS, we want to put machine learning in the hands of every developer. For example, we have pre-trained AI services for areas such as computer vision and language that you can use without any expertise in machine learning. Today we are making another step in that direction with the addition of a new Predictions category to the Amplify Framework. In this way, you can add and configure AI/ML uses cases for your web or mobile application with few lines of code!

AWS Amplify consists of a development framework and developer services that make super easy to build mobile and web applications on AWS. The open-source Amplify Framework provides an opinionated set of libraries, user interface (UI) components, and a command line interface (CLI) to build a cloud backend and integrate it with your web or mobile apps. Amplify leverages a core set of AWS services organized into categories, including storage, authentication & authorization, APIs (GraphQL and REST), analytics, push notifications, chat bots, and AR/VR.

Using the Amplify Framework CLI, you can interactively initialize your project with amplify init. Then, you can go through your storage (amplify add storage) and user authentication & authorization (amplify add auth) options.

Now, you can also use amplify add predictions to configure your app to:

  • Identify text, entities, and labels in images using Amazon Rekognition, or identify text in scanned documents to get the contents of fields in forms and information stored in tables using Amazon Textract.
  • Convert text into a different language using Amazon Translate, text to speech using Amazon Polly, and speech to text using Amazon Transcribe.
  • Interpret text to find the dominant language, the entities, the key phrases, the sentiment, or the syntax of unstructured text using Amazon Comprehend.

You can select to have each of the above actions available only to authenticated users of your app, or also for guest, unauthenticated users. Based on your inputs, Amplify configures the necessary permissions using AWS Identity and Access Management (IAM) roles and Amazon Cognito.

Let’s see how Predictions works for a web application. For example, to identify text in an image using Amazon Rekognition directly from the browser, you can use the following JavaScript syntax and pass a file object:

Predictions.identify({
  text: {
    source: file
    format: "PLAIN" # "PLAIN" uses Amazon Rekognition
  }
}).then((result) => {...})

If the image is stored on Amazon S3, you can change the source to link to the S3 bucket selected when adding storage to this project. You can also change the format to analyze a scanned document using Amazon Textract. Here’s how to extract text from a form in a document stored on S3:

Predictions.identify({
  text: {
    source: { key: "my/image" }
    format: "FORM" # "FORM" or "TABLE" use Amazon Textract
  }
}).then((result) => {...})

Here’s an example of how to interpret text using all the pre-trained capabilities of Amazon Comprehend:

Predictions.interpret({
  text: {
    source: {
      text: "text to interpret",
    },
    type: "ALL"
  }
}).then((result) => {...})

To convert text to speech using Amazon Polly, using the language and the voice selected when adding the prediction, and play it back in the browser, you can use the following code:

Predictions.convert({
  textToSpeech: {
    source: {
      text: "text to generate speech"
    }
  }
}).then(result => {
  var audio = new Audio();
  audio.src = result.speech.url;
  audio.play();
})

Available Now
You can start building you next web or mobile app using Amplify today by following the get-started tutorial here and give us your feedback in the Amplify Framework Github repository.

There are lots of other options and features available in the Predictions category of the Amplify Framework. Please see this walkthrough on the AWS Mobile Blog for an in-depth example of building a machine-learning powered app.

It has never been easier to add machine learning functionalities to a web or mobile app, please let me know what you’re going to build next.

Danilo

AWS CloudFormation Update – Public Coverage Roadmap & CDK Goodies

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I launched AWS CloudFormation in early 2011 with a pair of posts: AWS CloudFormation – Create Your AWS Stack From a Recipe and AWS CloudFormation in the AWS Management Console. Since that launch, we have added support for many AWS resource types, launched many new features, and worked behind the scenes to ensure that CloudFormation is efficient, scalable, and highly available.

Public Coverage Roadmap
CloudFormation use is growing even faster than AWS itself, and the team has prioritized scalability over complete resource coverage. While our goal of providing 100% coverage remains, the reality is that it will take us some time to get there. In order to be more transparent about our priorities and to give you an opportunity to manage them, I am pleased to announce the much-anticipated CloudFormation Coverage Roadmap:

Styled after the popular AWS Containers Roadmap, the CloudFormation Coverage Roadmap contains four columns:

Shipped – Available for use in production form in all public AWS regions.

Coming Soon – Generally a few months out.

We’re working on It – Work in progress, but further out.

Researching – We’re thinking about the right way to implement the coverage.

Please feel free to create your own issues, and to give a thumbs-up to those that you need to have in order to make better use of CloudFormation:

Before I close out, I would like to address one common comment – that AWS is part of a big company, and that we should simply throw more resources at it. While the team is growing, implementing robust, secure coverage is still resource-intensive. Please consider the following quote, courtesy of the must-read Mythical Man-Month:

Good cooking takes time. If you are made to wait, it is to serve you better, and to please you.

Cloud Development Kit Goodies
The Cloud Development Kit (CDK) lets you model and provision your AWS resources using a programming language that you are already familiar with. You use a set of CDK Constructs (VPCs, subnets, and so forth) to define your application, and then use the CDK CLI to synthesize a CloudFormation template, deploy it to AWS, and create a stack.

Here are some resources to help you to get started with the CDK:

Stay Tuned
The CloudFormation Coverage Roadmap is an important waypoint on a journey toward open source that started out with cfn-lint, with some more stops in the works. Stay tuned and I’ll tell you more just as soon as I can!

Jeff;

Amazon Polly Introduces Neural Text-To-Speech and Newscaster Style

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From Robbie the Robot to Jarvis, science fiction writers have long understood how important it was for an artificial being to sound as lifelike as possible. Speech is central to human interaction, and beyond words, it helps us express feelings and emotions: who can forget HAL 9000’s haunting final scene in 2001: A Space Odyssey?

In the real world, things are more complicated of course. Decades before the term ‘artificial intelligence’ had even been coined, scientists were designing systems that tried to mimic the human voice. In 1937, almost 20 years before the seminal Dartmouth workshop, Homer Dudley invented the Voder, the first attempt to synthesize human speech with electronic components: this video has sound samples and extra information on this incredible device.

We’ve come a long way since then! At AWS re:Invent 2016, we announced Polly, a managed service that turns text into lifelike speech, allowing customers to create applications that talk, and build entirely new categories of speech-enabled products. Zero machine learning expertise required: just call an API and get the job done! Since then, the team has regularly added new voices, for a current total of 29 languages and 59 voices.

Today, we’re happy to announce two major new features for Polly: Neural Text-To-Speech, and a groundbreaking newscaster style.

Introducing Neural Text-To-Speech (NTTS)
Through a new machine learning approach, NTTS delivers significant improvements in speech quality. It increases naturalness and expressiveness, two key factors in synthesizing lifelike speech that is getting closer than ever from human voices. Here’s an example of the quality you can expect.

As of today, NTTS is available for 11 voices, both in real-time and in batch mode:

  • All 3 UK English voices: Amy, Emma and Brian.
  • All 8 US English voices: Ivy, Joanna, Kendra, Kimberly, Salli, Joey, Justin and Matthew.

Why not head out to the AWS console for a quick test?

Introducing the newscaster style

Speech quality is certainly important, but more can be done to make a synthetic voice sound even more realistic and engaging. What about style? For sure, human ears can tell the difference between a newscast, a sportscast, a university class and so on; indeed, most humans adopt the right style of speech for the right context, and this certainly helps in getting their message across.

Thanks to NTTS, it’s possible to apply styles to synthesized speech, and you can now use a newscaster style with Polly. Here’s an example.

From news to blog posts, this makes narration sound even more realistic, and customers like The Globe and Mail already use it today. Thanks to Polly and the newscaster style, their readers (or should we say listeners now?) can enjoy articles read in a high-quality voice that sounds like what they might expect to hear on the TV or radio. Adding Amazon Translate, they can also listen to articles that are automatically translated to a language they understand.

As of today, the newscaster style is available for two US English voices (Joanna and Matthew), both in real-time and in batch mode. Again, you can head out to the AWS console for a quick test, and here’s the same clip as above with the newscaster style.

Using Polly APIs with the NTTS voices and the newscaster style is extremely easy. Please let me show you how to get started with both.

Using NTTS Voices and the Newscaster Style
Let’s grab a bit of text for Polly to read: how about this paragraph from Amazon Simple Storage Service (S3)‘s announcement in 2006?

“Earlier today we rolled out Amazon S3, our reliable, highly scalable, and low-latency data storage service. Using SOAP and REST interfaces, developers can easily store any number of blocks of data in S3. Each block can be up to 5 GB in length, and is associated with a user-defined key and additional key-value metadata pairs. Further, each block is protected by an ACL (Access Control List) allowing the developer to keep the data private, share it for reading, or share it for reading and writing, as desired. The system was designed to provide a data availability factor of 99.99%; all data is transparently stored in multiple locations”.

I will use batch mode in order to save sound files in S3 and let you grab them: I explicitly changed permissions to make them public, but don’t worry, your own files are completely private by default.

Let’s first try the standard Matthew voice.

$ aws polly start-speech-synthesis-task
      --voice-id Matthew --text file://s3.txt
      --output-s3-bucket-name "jsimon-polly" --output-format mp3
      --query "SynthesisTask.TaskId"
"e3db409c-419d-4a31-a3a7-72c1e712fe23"
$ wget https://jsimon-polly.s3.amazonaws.com/e3db409c-419d-4a31-a3a7-72c1e712fe23.mp3 -O matthew-standard.mp3

Tell us a bit about S3, Matthew.

Now, let’s use the NTTS version of the same voice: all we have to do is set the ‘engine‘ parameter to ‘neural‘.

$ aws polly start-speech-synthesis-task
      --voice-id Matthew --engine neural --text file://s3.txt
      --output-s3-bucket-name "jsimon-polly" --output-format mp3
      --query "SynthesisTask.TaskId"
"e3902335-c1e6-450b-b6e9-f913d6d52055"
$ wget https://jsimon-polly.s3.amazonaws.com/e3902335-c1e6-450b-b6e9-f913d6d52055.mp3 -O matthew-neural.mp3

You should immediately notice the quality improvement that NTTS brings. Of course, Polly has correctly picked up technical abbreviations, numbers, etc.

Now let’s spice things up and apply the newscaster style. This requires that we use the SSML markup language. All we need to do is to enclose the text like so:

<speak>
<amazon:domain name="news">
Earlier today we rolled out Amazon S3, our reliable, highly scalable, and low-latency data storage service. Using SOAP and REST interfaces, developers can easily store any number of blocks of data in S3. Each block can be up to 5 GB in length, and is associated with a user-defined key and additional key-value metadata pairs. Further, each block is protected by an ACL (Access Control List) allowing the developer to keep the data private, share it for reading, or share it for reading and writing, as desired. The system was designed to provide a data availability factor of 99.99%; all data is transparently stored in multiple locations.
</amazon:domain>
</speak>

Let’s synthesize this text again, making sure to set text type to SSML.

$ aws polly start-speech-synthesis-task
       --voice-id Matthew --engine neural
       --text file://s3.ssml --text-type ssml
       --output-s3-bucket-name "jsimon-polly" --output-format mp3
       --query "SynthesisTask.TaskId"
"25c18bda-b32b-4485-a45f-eb9b757a513b"
$ wget https://jsimon-polly.s3.amazonaws.com/25c18bda-b32b-4485-a45f-eb9b757a513b.mp3 -O matthew-neural-newscaster.mp3

I’m sure you can immediately tell the difference! Doesn’t this sound like a news reporter reading our text?

If you’re curious about the Joanna voice, here are the equivalent clips: standard, neural, and neural with newscaster style.

Available Now!
As you can see, it’s extremely easy to use these new features, and they are available today in US East (N. Virginia), US West (Oregon) and Europe (Ireland). The free tier offers 1 million characters for NTTS voices per month for the first 12 months, starting from your first request for speech (standard or NTTS).

We’re looking forward to your feedback! Please post it to the AWS Forum for Polly, or send it to your usual AWS support contacts.

Julien;

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