Machine Learning for Developers

Machine Learning for Developers

Devoxx Belgium, Antwerp, November 10th, 2016

Have you always wanted to add predictive capabilities or voice recognition to your application, but haven’t been able to find the time or the right technology to get started? Everybody wants to build smart apps, but only a few are Data Scientists. We had the same issue inside Amazon, so we created a Machine Learning engine that Developers can easily use. The same approach is now available in the AWS cloud. And we introduced Amazon Alexa to build engaging voice experiences for your services and devices: if you are a device maker, and your connected product has a microphone and a speaker, the Alexa Voice Service (AVS) enables you to add voice-powered experiences to your connected devices. And you can also use the Alexa Skills Kit (ASK) to teach new skills to Alexa!

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Danilo Poccia

November 10, 2016
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Transcript

  1. 1.

    Machine Learning for Developers Danilo Poccia Technical Evangelist, AWS @danilop

    Sébastien Stormacq Solution Architect, Alexa @sebsto
  2. 2.

    What to Expect from this Session Understand Machine Learning Terminology

    & Challenges Implement a Machine Learning Model Add Predictive Capabilities to your App Provide Your Customer with Voice UX
  3. 11.
  4. 13.

    Supervised Learning Machine Learning Unsupervised Learning The task of inferring

    a model from labeled training data The task of inferring a model to describe hidden structure from unlabeled data
  5. 35.

    Binary Classification Multiclass Classification Regression Logistic Regression
 (Logistic Loss Function

    + SGD) Multinomial Logistic Regression
 (Multinomial Logistic Loss + SGD) Linear Regression
 (Squared Loss Function + SGD) The optimization technique used in Amazon ML is
 online Stochastic Gradient Descent (SGD)
  6. 38.
  7. 39.
  8. 40.
  9. 47.

    Deep Learning AMI 5 Deep Learning Frameworks MXNet, Caffe, Tensorflow,

    Theano, and Torch Pre-installed components to speed productivity, such as Nvidia drivers, cuDNN, Anaconda, Python 2 & 3 AWS Integration R eady to use on A m azon EC 2
  10. 48.

    Amazon EC2 P2 Instances Up to: • 16 NVIDIA K80

    GPUs • 64 vCPUs 732 GiB of host memory • combined 192 GB of GPU memory • 40 thousand parallel processing cores • 70 teraflops (single precision) • over 23 teraflops (double precision). • GPUDirect™ for up to 16 GPUs G PU Instances
  11. 50.
  12. 52.

    Create Great Content: ASK is how you connect
 to your

    consumer THE ALEXA ECOSYSTEM Supported by two powerful frameworks A L E X A 
 V O I C E 
 S E R V I C E Unparalleled Distribution: AVS allows your content
 to be everywhere Lives In The Cloud Automated Speech Recognition (ASR) Natural Language Understanding (NLU) Always Learning A L E X A 
 S K I L L S 
 K I T
  13. 53.

    UNDER THE HOOD OF ASK A closer look at how

    the Alexa Skills Kit process a request and returns an appropriate response You Pass Back a Textual or Audio Response You Pass Back a Graphical Response Alexa Converts Text-to-Speech (TTS) & Renders Graphical Component Respond to Intent through Text & Visual Alexa sends Customer Intent to Your Service User Makes a Request Alexa Identifies Skill & Recognizes Intent Through ASR & NLU Your Service processes Request Audio Stream is sent up to Alexa
  14. 56.

    ALEXA SKILL KIT High Level Overview Availability Zone 1 Web

    tier App tier RDS (Master) Availability Zone 2 RDS (Standby)
  15. 57.

    ALEXA SKILL KIT High Level Overview Elastic Beanstalk environment Auto

    Scaling group Elastic Beanstalk container Auto Scaling group Elastic Beanstalk container Prod 1 Prod 2 Route 53
  16. 66.

    Your Skill (Lambda function) Amazon Machine Learning get real-time predictions

    invoke Weather Forecast Historical Data get forecast build & train model B ike Sharing D em o A rchitecture