DEV Community: Nick Walsh

Add AI Functionality to Your App in Minutes with AWS AI Cognitive Services

Nick Walsh — Tue, 10 Mar 2020 19:52:53 +0000

AWS AI Service Live Demo Site (no code or sign-in required)

Artificial intelligence and machine learning offer truly game changing functionality, but you probably didn't need me to tell you that! Concepts like computer vision and natural language understanding help us to unlock additional value from data we already have in sources like pictures, videos, and text. Additionally, AI can help to convert and generate new media, for scenarios like language translation, text to speech audio synthesis, and transcribing audio to text.

Unfortunately, the process of learning the prerequisite theory, popular machine learning frameworks, and finally integrating this code into existing software, is cumbersome. Don't even get me started on the process of having to gather and clean your own datasets for training a model!

Using Amazon Rekognition to detect objects in an image

To help you on your journey, AWS has a multitude of services to help empower developers without previous machine learning expertise. In this post, I wanted to cover some of the fully managed AI APIs, which I find are the most actionable for developers looking to implement powerful AI functionality quickly. These services offer a cost-effective, highly accurate, easy to use solution, without having to manage more of the ML pipeline in a custom model solution with a tool like Amazon SageMaker. If you can use an SDK, you won't have to worry about any ML theory or ops here.

Some of the biggest benefits of AWS fully managed AI APIs over other self-rolled solutions:

Extremely high availability, with no need to manage scaling
Models are crafted and improved over time by AWS AI Applied Scientists. Integrate once, and the endpoints are automatically updated in waves when new versions of the models are launched
Predictable, value-aligned pricing model (pay per request)
Very easy to get started with - if you can use an SDK, you can use AWS AI APIs

Demos:

To see everything on the live demo site, check it out here.

To power the demos, I used various AWS AI Services to make easily modifiable, standalone React components. Full code samples available here:

nmwalsh / aws-ai-services-site

Simple website to demo functionality for AWS AI services

AWS AI Service Demos

Try out various AI services from AWS, no code or account required.

Demo site hosted at https://ai-service-demos.go-aws.com

Included examples:

All components available in src/components/

Amazon Transcribe
- Transcribe.js: Batch async transcription job for custom audio
Amazon Polly
- Polly.js: Text to speech with standard or neural voice engine across all available languages
Amazon Comprehend
- Comprehend.js: Sentiment, Entity, Key Phrase, and Syntax Token detection
Amazon Rekognition
- Rekognition.js: Object detection
Amazon Translate
- Translate.js: Text to text translate

Installing

To run/test locally:

npm install

npm start

https://localhost:3000

AWS AI Service Free Tiers

The services covered in this demo all have very generous free tiers. At a glance:

Service	Description	Quantity
Amazon Translate	Text-Text Translation	2 million characters/month
Amazon Polly	Text to Speech	5 million characters/month
Amazon Comprehend	Natural Language Understanding	5 million characters/API/month
Amazon Rekognition	Computer Vision	5k images/month
Amazon Transcribe	Audio to Text Transcription	60

…

View on GitHub

The code samples were built using the minimal requirements wherever possible (I swear, most of the trickery is in the CSS), with the structure following a similar format to Translate.js, the most minimal example of the bunch:

// Translate.js 
// boilerplate react code above 

// 1. instantiate Translate client
        var Translate = new AWS.Translate({apiVersion: '2017-07-01'});
        let currentComponent = this;

        // 2. call translateText method
        if (!!TranslateParams.Text) {
          Translate.translateText(TranslateParams, function (err, data){
            if (err) {
                // 3a. catch error
                currentComponent.setState({resultMessage: err.message});
                currentComponent.setState({resultTranslation: 'No translation occurred - check the error!'})
            }
            else {
                // 3b. process successful response
                currentComponent.setState({resultTranslation: data.TranslatedText});
                currentComponent.setState({resultMessage: "Text translation successful!"})
            }
            document.getElementById("chck1").checked = true;
        });
      };

    }


    render() {
        let result, translation;
        // 4. If there is a result message from Translate, generate HTML from JSX
        if(this.state.resultMessage !== ''){
          result = <code>{this.state.resultMessage}</code>
          translation = <code>{this.state.resultTranslation}</code>
        }
/* other JSX code below for displaying info in app */

All components available in src/components/

Amazon Transcribe
- Transcribe.js: Batch async transcription job for custom audio
Amazon Polly
- Polly.js: Text to speech with standard or neural voice engine across all available languages
Amazon Comprehend
- Comprehend.js: Sentiment, Entity, Key Phrase, and Syntax Token detection
Amazon Rekognition
- Rekognition.js: Computer vision - object detection in images
Amazon Translate
- Translate.js: Text to text translate

To clone and install locally:

git clone https://github.com/nmwalsh/aws-ai-services-site

npm install

npm start

https://localhost:3000

Try these services out for free in your own accounts:

The services covered in this demo all have very generous free tiers. At a glance:

Service	Description	Quantity
Amazon Translate	Text-Text Translation	2 million characters/month
Amazon Polly	Text to Speech	5 million characters/month
Amazon Comprehend	Natural Language Understanding	5 million characters/API/month
Amazon Rekognition	Computer Vision	5k images/month
Amazon Transcribe	Audio to Text Transcription	60 minutes/month

For the most up-to-date info on free tier status, check out the live pricing page here.

Built With

AWS AI Services - Fully managed AI services, on a pay-per-use model.
AWS Amplify - Development toolchain for building and deploying webapps

Another awesome callout here is Amplify Predictions, a class of functionality for the Amplify Framework that enables you to easily generate code that achieves similar functionality to what I created, all with a few simple CLI commands! I would highly recommend this, as the autogenerated code will save you significant time for some of the services that would otherwise require writing code to act as connective tissue (storing data to S3 before processing, for example).

Thanks for reading!

I hope this article and code sample were helpful for you! My goal with this was to offer a way to try AWS AI services for yourselves, with your own data. This way, you can see if these services would be a good fit for your use case - all before writing any of your own code. I'm working on some more demos in this space and would love to hear your thoughts!

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For the latest updates on new demos, or to vote on the next one I'll create, follow along on twitter (@TheNickWalsh). Cheers!

AWS Databases Deep Dive: Season 1 Recap

Nick Walsh — Wed, 12 Feb 2020 22:05:18 +0000

AWS Databases Deep Dive: Season 1 Recap

Databases are ubiquitous, powering the data storage, access, and retrieval functions needed by most applications we use on the internet today. With a constantly expanding landscape of implementation options and acronyms, it’s often difficult to understand where to begin (nonetheless figuring out how to choose one option over another).

Last year, some teammates and I came together to attempt to ameliorate this issue for database users on AWS. While there’s a vast body of “getting started” content out there, we felt there was a gap in highly technical content that extended beyond the format of one-to-many style re:Invent talks.

Knowing this, our show started to take shape: my co-host Randall Hunt and I would bring on a special guest each week from the teams that build these database services, diving deep on each offering from AWS. While there would be some slides, the goal would be for the format to be conversational in nature: expounding on explanations, performance questions, hypotheticals, and everything in between — all integrating questions from twitch chat, no matter how difficult.

While many nuanced, technical questions are answered initially with “it depends”, we aimed to go a step further so that developers could develop an understanding of the questions to ask themselves in order to arrive at the best tool for the job.

The unifying value propositions we aimed to return to week after week were:

Deepen the understanding of features and tools available for database services you may already already using
Broaden your horizons by exposing you to database offerings that may be better suited for a particular task you face
Offer a glimpse of how these services operate under the hood to achieve performance at scale

So, over the course of 3.5 months, we went live on *twitch.tv/aws* weekly, rain or shine, every Tuesday at Noon pacific. In total, Season 1 resulted in a 13 episode series (with countless topics left over for us to cover in season 2).

To view any of the episodes on demand (with chatlogs replaying as they occurred during the broadcast) check out the full playlist available on Twitch here.

Season 1 Episode List

Overview

Ep1 — How to Choose The Right Database for the Job with Joe Idziorek

NoSQL Databases

SQL/Relational Databases

Ep3 — Amazon Aurora: Architecting the Relational Database Built for the Cloud with Yoav Eilat
Ep5 — Aurora Storage: Scalability and Reliability for Relational Data with Yuri Volobuev
Ep6 — Aurora Serverless: What’s Up with Serverless Databases? with Anoop Gupta
Ep10 —Amazon Aurora: Why are so many people migrating to PostgreSQL? with Denis Lussier
Ep13 — What’s New with Serverless Relational Databases? *(ft. Amazon RDS Data API) *with Nitesh Mehta and Lawrence Webley

Graph Databases

Ep7 —* Amazon Neptune:* Graph Databases Demystified with Brad Bebee

Caching

Ep8 — Redis & Amazon ElastiCache: Building Real Time Applications with Mohan Varthakavi

Database Migrations

Ep9 — Migrating Relational Databases to the Cloud with *Amazon DMS *with John Winford
Ep11 — Migrating to the Cloud with NoSQL Databases using *Amazon DMS *with John Winford and Rick Houlihan

Performance Analysis

Ep12 — Analyzing and fine tuning the relational database using *RDS Performance Insights *with Kyle Hailey and Mackenzie Kosut

Looking back

With Season 1 is in the books, I just wanted to say it was a blast streaming for all of those many hours, and that’s in no small part due to the excellent questions, comments, and feedback we received in the chat from you, the viewers. Whether you asked a deeply technical question about applying a database to one of your production workloads, or simply wanted to comment on our laptop stickers, the audience contributions made it all worthwhile for us.

tasteful banter is always appreciated :)

And they don’t stop comin’

In the meantime, we’re hard at work preparing for Season 2, which is right around the corner! As always, your feedback is greatly appreciated, and helps us craft an even better experience for you all. Want more demos? Customer use cases/case studies? Live coding? More elaborate guest introductions? Silly recurring rituals? Any particular guests you demand to see back? We’re all ears. Reach out over twitter, or email us at databasesdeepdive (at) amazon .com to let us know. If you’re interested in related content in the meantime, our channel also hosts an entire show dedicated to building on DynamoDB, check out past episodes here.

To follow along with what’s developing in the next few weeks, follow me on twitter for announcements, and our AWS twitch channel to get notifications when we go live. Hope to see you in chat!