Artificial Intelligence and Machine Learning on AWS · Mobile Analytics Mobile App Testing Targeted...

© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.

Davide Gallo, AWS Alliances Manager Switzerland

June 2018

Artificial Intelligence and Machine Learning on AWS


At Amazon, we’ve been investing in ML for the last 20 years…and the last 10 years of AI innovation, today are available to our Customers.

Search &

Discovery

Fulfilment

& Logistics

Consumer

Products

New

Initiatives

© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.3

AWS : Most robust, fully featured Cloud Platform

Integrated Networking

Rules Engine

Device Shadows

Device SDKs

Device Gateway

Registry

Local Compute

Machine Learning

Conversational Interface

Virtual Desktops

App Streaming

Schema Conversion

Image RecognitionSharing & Collaboration

Exabyte-ScaleData Migration

Text to Speech Corporate EmailApplication Migration

Database Migration

Regions

Availability Zones

Points of Presence

Data Warehousing

Business Intelligence

Elasticsearch

Hadoop/Spark

Data Pipelines

Streaming Data Collection

ETLStreaming Data Analysis

Interactive SQL Queries

Queuing & Notifications

Workflow

Email

Transcoding

Deep Learning Frameworks

Server Migration

Communications

Business Apps Business Intelligence DevOps Tools Security Networking StorageDatabases

API Gateway

Single Integrated Console

Identity

Sync

Mobile Analytics

Mobile App Testing

Targeted Push Notifications

One-click App Deployment

DevOps Resource Management

Application Lifecycle Management

Containers

Triggers

Resource Templates

Build and Test

Analyze and Debug

ComputeVMs, Auto-scaling, Load Balancing, Containers, Virtual Private Servers, Batch Computing, Cloud Functions, Elastic GPUs, Edge Computing

StorageObject, Blocks, File, Archivals, Import/Export, Exabyte-scale data transfer

CDN

DatabasesRelational, NoSQL, Caching, Migration, PostgreSQL compatible

NetworkingVPC, DX, DNS

Identity Management

Key Management & Storage

Monitoring & Logs

Configuration Compliance

Web Application Firewall

Assessment& Reporting

Resource & Usage Auditing

Access Control

Account Grouping

DDOS Protection

Support ProfessionalServices

Optimization Guidance

PartnerEcosystem

Training & Certification Solutions Management

Account Management

Security & Billing ReportsPersonalized Dashboard

TECHNICAL & BUSINESS SUPPORT

MARKETPLACE

Monitoring

Manage Resources

Data Integration

Integrated Identity & Access

Integrated Resource & Deployment Management

Integrated Devices& Edge Systems

Resource Templates

Configuration Tracking

Server Management

Service Catalogue

Search

HYBRID ARCHITECTUREANALYTICS MOBILE SERVICESDEV/OPS IoT AI ENTERPRISE APPS MIGRATION

APP SERVICES

INFRASTRUCTURE CORE SERVICES SECURITY & COMPLIANCE MANAGEMENT TOOLS


Running AI/ML in Production on AWS Today


AI Technologies Will Be in Almost Every New

Software Product by 2020 – Gartner

By 2020, AI will be a top five investment priority

for more than 30 percent of CIOs – Gartner

The market for cognitive/AI solutions will experience

a compound annual growth rate (CAGR) of 55.1

percent…to more than $47 billion in 2020” – IDC


Gartner’s top 10 Strategic Tech Trends 2018

• AI Foundation

• Intelligent Apps & Analytics

• Intelligent Things

• Blockchain

• Event Driven

• Continuous Adaptive Risk Trust

• Cloud at Edge

• Digital Twins

• Immersive Experience

• Conversational Platforms


1. Why AI/ML is Important for EveryBusiness


What is AI?

https://www.geospatialworld.net/blogs/difference-between-ai%EF%BB%BF-machine-learning-and-deep-learning/


Algorithms

DataProgrammingModels

GPUs &Acceleration

The Advent of Deep Learningimage understanding

natural language

processing

speech recognition

autonomy


Business Benefits of AI/ML

1. Automation

• Optimize existing processes

• Maintain (or improve) quality & reduce cost

2. Enrichment

• Add new features to products and services

• Increase engagement & attract new customers

3. Invention

• Create new products and categories

• Leverage industry experience + AI expertise


NEXT BEST PRODUCT

CUSTOMER ADOPTION MODELS

PERSONALIZED HEALTHCARE

AUTONOMOUS OPERATION

FRAUD DETECTION

DEMAND FORECASTING

PREDICTIVE MAINTENANCE

CLINICAL DECISION SUPPORT

Seemingly endless applications with AI/ML


Autonomous machines - reliability and safety

Mechanized labor increases the ability to scale safely

Transform operations through autonomous systems -- offloading to

machines the undifferentiated or unsafe human tasks and

decision making. Systems can perform more efficiently,

accurately, and safely at scale.

Autonomous

Machines

Example Use Cases

Predictive maintenance Autonomous vehicles

Shop floor automation Zone access control and event ID

Robotic emergency response Ambient change detection


AI Computer Vision Implementation to identify empty space and detect object

misplacements


Seamless Experience - no more boundaries

Seamless

experience

Remove the hard edges that start and stop an experience

Provide customers with an immersive, natural experience that

starts and stops without artificial boundaries. These

experiences include voice enablement, movement detection,

intelligent visual context, and are responsive to intention.

Example Use Cases

Chatbots and Robo-advisors Service fulfilment via Alexa Skills

Sentiment detection and response Immersive, enriched multimedia

Checkout-free shopping AI-enhanced sales associates

Facial detection for personalization Fraud detection and prevention

Systems see which items have been taken or returned

Machine learning understands in-store and purchase patterns

Combination of computer vision, sensor fusion, and deep learning

Just Walk Out Technology


Scientific breakthrough - golden age of discovery

Solve fundamental needs for humans, animals, and the earth

Scientific discoveries using deep learning require tremendous

compute capabilities to build and validate models -- now available.

Frameworks and development platforms allow for a broadened

community of users from scientists to analysts.

Scientific

Breakthrough

Example Use Cases

Patient treatment & safety Agricultural yield optimization

Urban maintenance Livestock health and production

Quantum / particle advancements Utility grid efficiency


Early Detection of Diabetic Complications

Deep learning can uncover insights in

imaging data that was time-intensive

and error-prone for humans to do


Healthcare &Life Sciences

AI is in Every Industry Vertical


Personalized content- Account access

- Track spending

- Check balances

- Pay bills

- Prevent fraud

Voice recognition

understands your requests

Natural speech responses

create conversation


Object identification and

recognition

Real Time, Per Pixel

Object Segmentation

Centimeter-accurate

positioning

Deep learning creates models of

roads, navigation, relevant

objects, and driving decisions

Edge compute allows for real-time

decisions to be made in-vehicle

Autonomous Driving Systems


illumina instruments responsible for

90% of DNA sequencing globally

In 2000, sequencing cost $1B. In 2014, $1,000.

Moving to AWS, Illumina drove the cost down by 100X

Biological sample converts

to 100GB digital file

Illumina instruments stream

270B data points / yr


2. Relationship between Data Analytics and AI/ML


Applications of AI

"Give me a lever long enough and a fulcrum on which to place it, and I shall move the world."

Archimedes

"Given enough quality data and processing power, AI will eventually do anything you need.”


It’s all about data


A Flywheel for Data

Better Decisions

Object Storage

Databases

Data warehouse

Streaming analytics

BI

Hadoop

Spark/Presto

Elasticsearch

Better Products Machine Learning

Deep Learning/ AI

More Users

More Data

Click stream

User activity

Generated content

Purchases

Clicks

Likes

Sensor data


Deep Learning will enable AI

Eventually everything connectsIoT / Mobile / Cameras / APIs

Large amounts of data will be processed in real-time


Data isIf you don’t leverage it…

it will lose value overtime.

…the fuel of ML

…critical for growth

…a valuable asset


AI/ML Adoption Benefits and Challenges

CONVERTING

THE POWER OF

MACHINE

LEARNING INTO

BUSINESS

VALUE

MAKING THE

BEST USE OF A

DATA

SCIENTISTS

TIME

EMBEDDING

MACHINE

LEARNING INTO

THE FABRIC OF

YOUR BUSINESS

While the power of ML is unrivaled, “data scientists spend around

80% of their time on preparing and managing data for analysis” …

hence only 20% of their time is used to derive insights

The value of data science relies upon operationalizing models

within business applications and processes, yet “50% of the

predictive models [built] don’t get implemented”

While “60% of companies agree that big data will help improve their

decision making and competitiveness … only 28% indicate that

they are currently generating strategic value from their data”

1

2

3


Artificial Intelligence requires a rich set of technologies and services to support the growing demands for innovation.

Developers using our technologies may want to build at different levels with fully managed application services, with

development platforms, with coding frameworks, and directly leveraging specialized infrastructure



The Cloud Was Built for Big Data and ML

Agility: Try more, fail fast,

go big or start small, and

process data at any scale

Scalability: Run jobs any

time, without guessing

capacity or limiting

functionality

Get to Insights Faster:

Focus on data science not

the heavy undifferentiated lift

of managing raw data

Broadest and Deepest

Capabilities: Access 70+

managed Big Data/AI

services to address any

workload

Low Cost: Pay only for the

IT you use, when you use it

Data Migrations Made

Easy: Move exabyte-scale

data to the cloud quickly and

cost-effectively


Amazon AIIntelligent Services Powered By Deep Learning


NEW NEW NEW

NEW

Engines: Deep Learning

Amazon

SageMakerPlatforms: AI Model Creation

Services: AI Applications


The Amazon machine learning stack

PLATFORM SERVICES

APPLICATION SERVICES

FRAMEWORKS & INTERFACES

Caffe2 CNTKApache

MXNetPyTorch

TensorFlo

wTorch Keras Gluon

AWS Deep Learning AMIs

Amazon SageMaker AWS DeepLens

Rekognition Transcribe Translate Polly Comprehend Lex

Amazon Mechanical Turk Amazon ML


The Amazon machine learning stack – APIs

PLATFORM SERVICES



Caffe2 CNTKApache

MXNetPyTorch

TensorFlo

wTorch Keras Gluon


Amazon SageMaker AWS DeepLensAmazon Mechanical Turk Amazon ML


Amazon Rekognition

Object, Scene &

Activity Recognition

Facial

Recognition

Facial Analysis Person Tracking

Unsafe Content

Detection

Celebrity

Recognition

Text in Images

D e e p l e a r n i n g - b a s e d i m a g e a n d v i d e o a n a l y s i s


Demographic Data

Facial Landmarks

Sentiment Expressed

Image Quality

Brightness: 25.84

Sharpness: 160

General Attributes

Rekognition: Facial Detection


Rekognition: Object & Scene detection


Rekognition Video Analysis Service


Voice Services : Amazon Transcribe

A u t o m a t i c c o n v e r s i o n o f s p e e c h i n t o a c c u r a t e , g r a m m a t i c a l l y c o r r e c t t e x t

Support for

telephony audio

Timestamp

generation

Intelligent punctuation

and formatting

Recognize multiple

speakers

Custom

vocabulary

Multiple

languages


Amazon Translate

N a t u r a l a n d f l u e n t l a n g u a g e t r a n s l a t i o n

Real-time

translation

Batch analysis Automatic language

recognition

Low cost


Amazon Polly

Tu r n t e x t i n t o l i f e l i k e s p e e c h u s i n g d e e p l e a r n i n g

Wide Selection of

Voices and

Languages

Synchronize

Speech

Fine-grained

ControlUnlimited Replay


Amazon Comprehend

D i s c o v e r i n s i g h t s a n d r e l a t i o n s h i p s i n t e x t

Entities

Key Phrases

Language

Sentiment

Amazon

Comprehend


Amazon Comprehend

STORM

WORLD SERIES

STOCK MARKET

WASHINGTON

LIBRARY OF

NEW S ARTICLES

Amazon

Comprehend

D i s c o v e r i n s i g h t s a n d r e l a t i o n s h i p s i n t e x t


Amazon Lex

C o n v e r s a t i o n a l i n t e r f a c e f o r a p p l i c a t i o n s u s i n g n a t u r a l l a n g u a g e

Integrated

development in the

AWS console

Trigger

Lambda

functions

Multi-step

conversations

One-click

deployment

Enterprise

connectors

Fully

managed


Lex Bots

Salesforce

Microsoft Dynamics

Marketo

Zendesk

Web

Devices

AppsFacebook Messenger, Slack,

Amazon Connect

MobileMobile Hub integration

Quickbooks

Amazon Lex: Conversational Chatbots


The Amazon machine learning stack – platform


PLATFORM SERVICES


Caffe2 CNTKApache

MXNetPyTorch

TensorFlo

wTorch Keras Gluon



Can we do more to put ML in

the hands of all developers

(literally)?


ML is still too complicated for everyday developers

Collect and prepare

training data

Choose and

optimize your ML

algorithm

Set up and manage

environments for

training

Train and tune model

(trial and error)

Deploy model

in production

Scale and manage

the production

environment


Amazon SageMaker

Collect and prepare

training data

Choose and

optimize your ML

algorithm

Set up and manage

environments for

training

Train and tune model

(trial and error)

Deploy model

in production

Scale and manage

the production

environment

E a s i l y b u i l d , t r a i n , a n d d e p l o y m a c h i n e l e a r n i n g m o d e l s


Amazon SageMaker

Pre-built

notebooks for

common

problems

K-Means Clustering

Principal Component Analysis

Neural Topic Modelling

Factorization Machines

Linear Learner - Regression

XGBoost

Latent Dirichlet Allocation

Image Classification

Seq2Seq

Linear Learner - Classification

ALGORITHMS

Apache MXNet

TensorFlow

Caffe2, CNTK,

PyTorch, Torch

FRAMEWORKSSet up and m anage

env i r onment s f o r

t r a i n ing

Tr a i n and t une

m odel ( t r i a l and

er r o r )

Dep l oy m odel

i n p r oduct ion

Sca l e and m anage t he

p r oduc t ion env i r onment

Built-in, high

performance

algorithms

BUILD


Amazon SageMaker

Pre-built

notebooks for

common

problems

Built-in, high

performance

algorithms

One-click

training

Hyperparameter

optimization

BUILD TRAIN

Deploy model

in production

Scale and manage

the production

environment


Amazon SageMaker

Fully managed

hosting with auto-

scaling

One-click

deploymentPre-built

notebooks for

common

problems

Built-in, high

performance

algorithms

One-click

training

Hyperparameter

optimization

BUILD TRAIN DEPLOY


The Amazon machine learning stack – foundation


PLATFORM SERVICES



Bottom Layer: Frameworks & interfaces

NVIDIA

Tesla V100 GPUs

P3

AWS Deep Learning AMI

5,120 Tensor cores

128GB of memory

1 Petaflop of compute

NVLink 2.0

~14X faster than P2


AWS Deep Learning AMIs ready for you

Quickly launch Amazon EC2 instances, pre-installed with popular deep learning frameworks to train sophisticated, custom AI models, experiment with new algorithms, or to learn new skills and techniques.

KERAS

More TensorFlow

applications are run on

AWS than any other

platform

Open source framework,

including convolutional

neural networks (CNNs)

and long short-term

memory (LSTMs)

High level API that is

user-friendly for a

broader base, to

increase ease of use for

TensorFlow and others


Support for all major frameworks

AWS Deep Learning AMI

Apache MXNet

Torch

Cognitive

ToolkitKeras

TheanoCaffe2

& Caffe

TensorFlow

Amazon EC2

AnacondaIntel MKL

Nvidia CUDA &

cuDNN

Python2 &

Python3


Open source deep learning interface which allows developersto easily and quickly build machine learning models, withoutcompromising performance

Provides a clear, concise API for defining machine learning models using a collection of pre-built, optimized neural networks

Developers will find this interface familiar, since machine learning models can be defined and manipulated just like any other data structure

Data scientists and researchers will value the ability to build prototypes quickly and utilize dynamic neural network graphs for entirely new model architectures

AWS and Microsoft announced Gluon


Train model in the cloud Run model at the edge

AWS

Greengrass

AWS IoT

Tesla V100

120 TFLOPS

Amazon

EC2


AWS DeepLens

HD video camera

Custom-designed

deep learning

inference engine

Micro-SD

Mini-HDMI

USB

USB

Reset

Audio out

Power

HD video camera

with on-board

compute optimized

for deep learning

Tutorials, examples,

demos, and pre-built

models

From unboxing to

first inference in

<10 minutes

Integrates with Amazon

SageMaker and AWS

Lambda

10MIN


PrimeAir

Edge compute allows for real-time

decisions to be made in-vehicle, driven

by computer vision and deep learning

Sense and Avoid Technology


Fully integrate AI services into your applications, leveraging your

existing business data assets, your streaming data, your data lakes,

and your operational systems

Use blueprints for what has been done to achieve what is possible...


Thank you !

Artificial Intelligence and Machine Learning on AWS · Mobile Analytics Mobile App Testing Targeted...

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