AI - State of the Union

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AI: State of the Union
Steven Bryen | AWS Technical Evangelist
@steven_bryen
sbryen@amazon.com

Today’s Agenda
10:00-11:00 – AI: State of the Union
11:00-11:30 – Break
11:30-12:30 – re:Invent Deep dive on Amazon Sagemaker, Forecast & Personalize
12:30-13:30 – Lunch
13:30 – 14:30 – Machine Learning: from notebook to production in Amazon Sagemaker
14:30-15:30 – Fraud Detection with Amazon Sagemaker
15:30-15:45. – Break
15:45-16:45 – An introduction to reinforcement learning with Amazon Sagemaker RL

If I asked you…
“How important is it for your company to invest in
Machine Learning and AI?”
What we often hear:
“We’re not a Machine Learning company.”
”They’re not central to our business.”
“We have more important topics to address.”

Now, if I asked you…
“How important is it for your organization to build better
products and deliver a better customer experience?”
We always hear:
“This is our number one priority.”
But isn’t this the same question, really?

Artificial
Intelligence
Object Storage
Databases
Data warehouse
Streaming analytics
BI
Hadoop
Spark/Presto
Elasticsearch
A Flywheel For Data
Click stream
User activity
Generated content
Purchases
Clicks
Likes
Sensor data
More Data Better Analytics
Better ProductsMore Users

Myth - AI is the flavour of the month

John McCarthy (1927-2011)
1956 - Coined the term “Artificial
Intelligence”
1958 - Invented LISP
1971 - Received the Turing Award
Fact - AI is 60 years old
Marvin Minsky (1927-2016)
1959 - Co-founded the MIT AI Lab
1968 - Advised Kubrick on “2001: A Space Odyssey”
1969 - Received the Turing Award

The (60+ years) rise of Artificial Intelligence

The Advent of
Deep Learning
Algorithms

The Advent of
Deep Learning
Data
Algorithms

The Advent of
Deep Learning
Data
GPUs
& Acceleration
Algorithms

The Advent of
Deep Learning
Data
GPUs
& Acceleration
Programming
models
Algorithms

Amazon AI

Put machine learning in the hands of every developer and
data scientist
ML @ AWS: Our mission

Application
Services
Platform
Services
Frameworks
&
Infrastructure
API-driven services: Vision & Language Services, Conversational Chatbots
AWS ML Stack
Deploy machine learning models with high-performance machine learning
algorithms, broad framework support, and one-click training, tuning, and
inference.
Develop sophisticated models with any framework, create managed, auto-
scaling clusters of GPUs for large scale training, or run inference on trained
models.

Customer Running ML on AWS Today

Application
Services API-driven services: Vision & Language Services, Conversational Chatbots
AWS ML Stack

Amazon Rekognition
Deep learning-based visual analysis service

Amazon
Rekognition
Object and scene detection
Facial analysis
Face comparison
Celebrity recognition
Image moderation

Object & Scene Detection

Facial Search

Marinus Analytics uses facial recognition to stop
human trafficking
“Now with Traffic Jam’s
FaceSearch, powered by
Amazon Rekognition,
investigators are able to
take effective action by
searching through millions
of records in seconds to
find victims.”
http://www.marinusanalytics.com/articles/2017/10/17/amazon-rekognition-helps-marinus-analytics-fight-human-trafficking

Amazon Rekognition Video

Video in. People, activities, and details out.
Objects, scenes, and activities
Person detection and recognition
Person tracking
Celebrity recognition
Inappropriate content detection
Amazon Rekognition Video

Rekognition Video Analysis Service

http://timescapes.org/trailers/

City of Orlando
Real-time video analysis
”The City of Orlando is excited to work with Amazon to
pilot the latest in public safety software through a unique,
first-of-it's-kind public-private partnership. Through the
pilot, Orlando will utilize Amazon Rekognition Video and
Amazon Kinesis Video Streams technology in a way that
will use existing City resources to provide real-time
detection and notification of persons-of-interests, further
increasing public safety, and operational efficiency
opportunities for the City of Orlando and other cities
across the nation”.
John Mina, Police Chief, City of Orlando

Amazon Polly
Deep learning-based Text-to-Speech service

Amazon Polly
“Hejsan! Jag
heter Astrid och
läser upp det som
skrivs här.”
Amazon Polly: Text In, Life-like Speech Out

Amazon Translate
Neural Machine Translation Service

“Hello, what’s up? Do you
want to go see a movie
tonight?”
Amazon Translate
Natural and fluent language translation
"Bonjour, quoi de neuf ? Tu
veux aller voir un film ce
soir ?"
Amazon
Translate

Amazon Transcribe
Automatic speech recognition service

“Hello, this is Allan
speaking”
Amazon
Transcribe

A vailable in
pre vie w
to d ay
Support for
telephony audio
Timestamp
generation
Intelligent
punctuation and
formatting
Recognize
multiple
speakers
Custom
vocabulary
Multiple
languages

Amazon Comprehend
Natural Language Processing

Fully managed natural language processing
Discover valuable insights from text
Entities
Key Phrases
Language
Sentiment
Amazon
Comprehend

Support for large data sets and topic modeling
STORM
WORLD SERIES
STOCK MARKET
WASHINGTON
LIBRARY OF
NEWS ARTICLES *
Amazon
Comprehend
* Integrated with Amazon S3 and AWS Glue

Amazon Lex
Conversational Interfaces

Intents
A particular goal that the
user wants to achieve
Utterances
Spoken or typed phrases
that invoke your intent
Slots
Data the user must provide to fulfill the
intent
Prompts
Questions that ask the user to input
data
Fulfillment
The business logic required to fulfill the
user’s intent
BookHotel

Lex Bots
Salesforce
Microsoft Dynamics
Marketo
Zendesk
Web
Devices
Apps
Facebook Messenger,
Slack,
Amazon
Connect
Mobile
Mobile Hub
integration
Quickbooks
Amazon Lex: Conversational Chatbots

Presley, Elvis AaronN A M E
Graceland, Memphis, TNA D D R E S S
12-12-1234I D
TCB LimitedC O M P A N Y
Graceland, Memphis
Presley
TCB Limited
12-12-1234
TN
901 987-6543
3765 Elvis Presley Blvd.
38116
Elvis
Elvis.Presley@yahoo.com
Amazon Textract: automatic document processing
without data entry, or writing rules

Amazon Textract
Extract text and data from virtually any document
Eliminate
manual effort
Lower document
processing costs
K E Y F E AT U R E S
Extract data quickly and
accurately
Optical Character
Recognition
(OCR)
Key-value pair
detection
Adjustable
confidence
thresholds
Table
detection
Bounding box
coordinates
No ML experience
required

Platform
Services
AWS ML Stack
Deploy machine learning models with high-performance machine learning
algorithms, broad framework support, and one-click training, tuning, and
inference.

Amazon SageMaker
A fully managed service to quickly and easily
build machine-learning based models

End-to-End
Machine Learning
Platform
Zero setup Flexible Model
Training
Pay by the second
$
Amazon SageMaker
Build, train, and deploy machine learning models at scale

One-click training
for ML, DL, and
custom algorithms
Easier training with
hyperparameter
optimization
Highly-optimized
machine learning
algorithms
Deployment
without
engineering effort
Fully-managed
hosting at scale
BuildPre-built
notebook
instances
Deploy
Train
Amazon SageMaker

Launch Customers

Frameworks
&
Infrastructure
AWS ML Stack
Develop sophisticated models with any framework, create managed, auto-
scaling clusters of GPUs for large scale training, or run inference on trained
models.

Amazon EC2 P3 Instances (October 2017)
• Up to eight NVIDIA Tesla V100 GPUs
• 1 PetaFLOPs of computational
performance – 14x better than P2
• 300 GB/s GPU-to-GPU communication
(NVLink) – 9X better than P2
• 16GB GPU memory with 900 GB/sec peak
GPU memory bandwidth
The fastest, most powerful GPU instances in the cloud

AWS Deep Learning AMI
• Easy-to-launch tutorials
• Hassle-free setup and configuration
• Pay only for what you use
• Accelerate your model training and deployment
• Support for popular deep learning frameworks

AWS DeepLens
Deep learning enabled video camera for
developers

A new way to learn
Custom built for deep learning
Broad Framework Support
Deploy models from Amazon SageMaker
Integrated with AWS
Full programmable with AWS Lambda
AWS DeepLens
World’s first deep learning enabled video camera for developers

10 minutes to your first deep learning project
1
Choose your deep learning
model from the AWS
DeepLens pre-trained
model library, or your own
models trained with
Amazon SageMaker.
2
Deploy your
model to the
device with a
single click.
3
Watch the
results in real
time in the AWS
Management
Console .

Amazon ML Lab
Provides the missing ML expertise

Amazon ML Lab
Lots of companies
doing Machine
Learning
Unable to unlock
business potential
Brainstorming Modeling Teaching
Lack ML
expertise
Leverage Amazon experts with decades of ML
experience with technologies like Amazon Echo,
Amazon Alexa, Prime Air and Amazon Go
Amazon ML Lab
provides the missing
ML expertise

Thank You
Steven Bryen | AWS Technical Evangelist
@steven_bryen
sbryen@amazon.com

AI - State of the Union

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