BDA310 An Introduction to the AI services at AWS

© 2015, Amazon Web Services, Inc. or its Affiliates. All rights reserved.

Keith Steward, Ph.D.

Specialist Solution Architect, AWS

July 26, 2017

An Introduction to the AI Services at AWS

Artificial Intelligence

at Amazon


Apache

Apache

MXNet

Deep learning framework


Apache

Amazon

Polly

Text-to-Speech

Apache

MXNet



Apache

Amazon

Polly

Text-to-Speech

Amazon

Rekognition

Computer Vision

Apache

MXNet



Apache

Amazon

Polly

Text-to-Speech

Amazon

RekognitionAmazon

Lex

Computer Vision ASR & NLU

Apache

MXNet



Apache

MXNet

Apache


Apache MXNet

Programmable Portable High Performance

Near linear scaling

across hundreds of GPUs

Highly efficient

models for mobile

and IoT

Simple syntax,

multiple languages

Why Apache MXNet?

Most Open Best On AWS

Optimized for

deep learning on AWS

Accepted into the

Apache Incubator

(Integration with AWS)

Apache MXNet is the deep learning framework

of choice for AWS

P2 INSTANCES DL CLOUD FORMATION TEMPLATE

DL AMIS


Amazon

Polly

Text-to-Speech

Apache

Amazon Polly: Life-like Text-to-Speech Service

Converts text

to life-like speech

47 voices 24 languages Low latency,

real time

Fully managed

Let’s take a listen…

“Today in Seattle, WA, it’s 11°F”

‘"We live for the music" live from the Madison Square Garden.’

1. Automatic, Accurate Text Processing

Amazon Polly: A Focus On Voice Quality & Pronunciation

2. Intelligible and Easy to Understand




3. Add Semantic Meaning to Text

“Richard’s number is 2122341237“

“Richard’s number is 2122341237“

Telephone Number




3. Add Semantic Meaning to Text

4. Customized Pronunciation

“My daughter’s name is Kaja.”

“My daughter’s name is Kaja.”



Amazon Polly: Common Use Cases

• Internet of Things (smart home, connected devices)

• Education (language learning, training videos)

• Voiced Media (news, blogs, email)

• Voiced Chat Bots (Amazon Lex, Alexa skills)

• Gaming (avatars, Amazon Lumberyard)

#VoiceFirst Movement


Amazon

Rekognition

Computer Vision

Apache

Amazon Rekognition: Computer Vision Service

Object and Scene

Detection

Facial

AnalysisFacial

Comparison

Facial

Recognition

Amazon Rekognition: Computer Vision ServiceState-of-the-art face recognition (bounding box and key features).

Face Attribute Extraction (emotion, gender, race, age, etc.)

Emotion: confused: 4%, calm: 73%

Sunglasses: false (value: 0)

Gender: female (value: 0)

Mouth open wide: 0% (value: 0)

Eye closed: open (value: 0)

Glasses: no glass (value: 0)

Mustache: false (value: 0)

Beard: (value: 0)

Amazon Rekognition: Computer Vision Service

demo

Amazon Rekognition: Object & Scene Detection

Amazon Rekognition: Facial Search

Facial

verification

Face

Search

Visual Similarity

Search

(compare two faces) (compare many faces) (find similar faces)

Amazon Rekognition: A few use cases

Best photo: use the attributes smile and eyesOpen to determine the best photos to post

Demographic detection: collect the age and gender of customers in your store

Sentiment capture: detect the emotions of your customers as they try your product

A/B tuning: identify visually similar alternatives to high-scoring images for A/B testing

Smart filtering: identify images with high visual similarity to ensure only one is displayed

Verify face: compare two faces, receive a confidence score that they are the same person

Protected images: identify visually similar images that are protected by trademarks


Amazon

Lex

ASR & NLU

Apache

The Advent of Conversational Interactions

1st gen: Machine-oriented

interactions



interactions

2nd gen: Control-oriented

& translated



interactions

2nd gen: Control-oriented

& translated

3rd gen:

Intent-oriented

Amazon Lex ... for Conversational Interactions

Powered by the same deep learning technology as Alexa

Enterprise SaaS Connectors

Deployment to chat platforms, like Slack, Facebook

Messenger, Twilio SMS

Build Voice and Text Chatbots

Interactions on mobile, web, and devices

Informational Bot: Example

Amazon Lex Use Cases

Informational BotsChatbots for everyday consumer requests

Application BotsBuild powerful interfaces to mobile applications

• News updates

• Weather information

• Game scores ….

• Book tickets

• Order food

• Manage bank accounts ….

Enterprise Productivity BotsStreamline enterprise work activities and improve efficiencies

• Check sales numbers

• Marketing performance

• Inventory status ….

Internet of Things (IoT) BotsEnable conversational interfaces for device interactions

• Wearables

• Appliances

• Auto ….

AI Solutions for Every Developer

https://aws.amazon.com/amazon-ai/

Amazon AI: Getting Started

Thank you!

aws.amazon.com/amazon-ai

• 10+ year partnership

• Joint development

• Shared customer passion

• High performance + low costs

• World class supply chain

CLOUD &

DATA CENTER

THINGS &

DEVICES

AWS IOT Alexa Voice

Services

Amazon EC2 Amazon S3

Amazon & Intel

Amazon & Intel

40@IntelAI

Hardware for DL Workloads

Up to 2X better peak performance

on compute-intensive analytics

100x improvement in inference

performance on EC2 C5 instance*

NEW C5 more computational

power, lower costs – customers do

more with less

Blazingly Fast Data Access

New microarchitecture, hardware

acceleration, Intel® AVX-512

50% more memory than previous

generation

Novartis conducted 39 years of

computational chemistry in 9 hours*

High Speed Scalability

Up to 1.73x faster completion of

massively parallel research

simulations than the previous

generation

Seamless data transfer via

interconnects

Training AI: Intel® xeon® scalable processorBest-in-Class Deep Learning Training Performance

Accelerator for training compute density in deep learning centric environments

+

41@IntelAI

Inference in the cloud: amazon & Intel®Math Kernel Library for Deep Neural Networks

For developers of deep learning frameworks featuring optimized performance on Intel hardware

6.1 2.4 1.2 0.8

679.4

262.5

79.7 73.9

0

200

400

600

800

AlexNet GoogLeNet v1 ResNet-50 Inception v3

Imag

es/S

ec

c4.8xlarge MXNet Inference

No MKL MKL

Up to 2X better peak performance on compute-intensive analytics

100x improvement in inference performance on EC2 C5 instance*

Intel-optimized Caffe, Intel® MKL for high performance distributed training and inference

CloudFormation template with AWS services and EC2, CfnCluster, DynamoDB, EBS and Spot Instance support

Classify text, train a Convolutional neural network, visualize the training using Tensorboard using BigDL on AWS

Intel Confidential

INTEL® IOT GATEWAY REAL TIME ANALYTICSAWS IOT PLATFORM

Amazon EC2

X1

Inference at the edge: AWS & Intel®

cost savings with scalability

End-to-end interoperability

to scale applications and services

streamlined manageability and

analytics

Seamless data management

and analytics from thing

to network to cloud

multilayered, end-to-end

security

A chain of trust rooted

in the hardware and linked throughout

the software

43@IntelAI

Libraries, frameworks & toolsIntel® Math Kernel

Library

Intel® MLSL

Intel® Data

Analytics

Acceleration

Library

(DAAL)

Intel®

Distributio

n

Open

Source

Frameworks

Intel Deep

Learning SDK

Intel® Computer

Vision SDKIntel® MKL MKL-DNN

High

Level

Overview

Computation

primitives; high

performance math

primitives granting

low level of control

Computation

primitives; free

open source DNN

functions for high-

velocity integration

with deep learning

frameworks

Communication

primitives; building

blocks to scale deep

learning framework

performance over a

cluster

Broad data analytics

acceleration object

oriented library

supporting distributed

ML at the algorithm

level

Most popular and

fastest growing

language for

machine learning

Toolkits driven by

academia and

industry for training

machine learning

algorithms

Accelerate deep

learning model

design, training and

deployment

Toolkit to develop &

deploying vision-

oriented solutions

that harness the full

performance of Intel

CPUs and SOC

accelerators

Primary

Audience

Consumed by

developers of

higher level

libraries and

Applications

Consumed by

developers of the

next generation of

deep learning

frameworks

Deep learning

framework

developers and

optimizers

Wider Data Analytics

and ML audience,

Algorithm level

development for all

stages of data

analytics

Application

Developers and

Data Scientists

Machine Learning

App Developers,

Researchers and

Data Scientists.

Application

Developers and Data

Scientists

Developers who

create vision-oriented

solutions

Example

Usage

Framework

developers call

matrix

multiplication,

convolution

functions

New framework

with functions

developers call for

max CPU

performance

Framework

developer calls

functions to distribute

Caffe training

compute across an

Intel® Xeon Phi™

cluster

Call distributed

alternating least

squares algorithm for

a recommendation

system

Call scikit-learn

k-means function

for credit card

fraud detection

Script and train a

convolution neural

network for image

recognition

Deep Learning

training and model

creation, with

optimization for

deployment on

constrained end

device

Use deep learning to

do pedestrian

detection

…

Find out more at software.intel.com/ai

Don’t Forget Your Evaluations!

BDA310 An Introduction to the AI services at AWS

Technology

Transcript of BDA310 An Introduction to the AI services at AWS