Artificial Intelligence (Machine Learning) on AWS: How to Start

© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Vladimír Šimek, Senior Solutions Architect, AWS
14th of November, 2018
Artificial Intelligence on AWS:
How to Start

Agenda
•Definitions
•Brief history of AI/ML
•AI/ML in Amazon
•AWS Services for AI/ML
•Demo + Q&A

A system or service which can perform tasks
that usually require human intelligence
Artificial Intelligence

is a field of computer science that gives
computers the ability to learn without being
explicitly programmed.
Machine Learning
Supervised Unsupervised

History of AI

AI (1993 – 2017)
1997 2005
2017
2011

Fulfilment
& Logistics
At Amazon, we’ve been making investments in
ML for the last 20 years…

Fulfilment
& Logistics
Search &
Discovery

Fulfilment
& Logistics
Existing
Products
Search &
Discovery

Fulfilment
& Logistics
Existing
Products
New
Products
Search &
Discovery

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

Frameworks &
Infrastructure
AWS Deep Learning AMI
GPU
(P3 Instances)
MobileCPU
IoT
(Greengrass)
Vision:
Rekognition Image
Rekognition Video
Speech:
Amazon Polly
Transcribe
Language:
Lex Translate
Comprehend
Apache
MXNet
PyTorch
Cognitive
Toolkit
Keras
Caffe2
& Caffe
TensorFlow Gluon
AWS ML Stack
Application
Services
Platform
Services
Amazon Machine
Learning
Mechanical
Turk
Spark &
EMR
Amazon
SageMaker
AWS
DeepLens

CUSTOMERS RUNNING MACHINE
LEARNING ON AWS TODAY

Frameworks &
Infrastructure
GPU
(P3 instances)
Mobile
CPU
(C5 instances)
IoT
(Greengrass)
Vision:
Rekognition Image
Rekognition Video
Speech:
Amazon Polly
Transcribe
Language:
Lex Translate
Comprehend
Apache
MXNet
PyTorch
Cognitive
Toolkit
Keras
Caffe2
& Caffe
TensorFlow Gluon
AWS ML Stack
Application
Services
Platform
Services
Amazon Machine
Learning
Mechanical
Turk
Spark &
EMR
Amazon
SageMaker
AWS
DeepLens

Amazon EC2 P3 Instances ( Oc tober 2017)
• Up to eight NVIDIA Tesla V100 GPUs
• 1 PetaFLOP of computational performance
– 14x better than P2
• 300 GB/s GPU-to-GPU communication
(NVLink) – 9X better than P2
• 16 GB GPU memory with 900 GB/sec peak
GPU memory bandwidth
T h e f a s t e s t , m o s t p o w e r f u l G P U i n s t a n c e s i n t h e c l o u d

• Get started quickly with easy-to-launch tutorials
• Hassle-free setup and configuration
• Pay only for what you use – no additional charge for
the AMI
• Accelerate your model training and deployment
• Support for popular deep learning frameworks
https://d1.awsstatic.com/white
papers/nucleus-tensorflow.pdf

How do we put ML in the hands of
developers (literally)?

AWS DeepLens: A Deep Learning-
Enabled Video Camera for Developers
• Fully programmable video camera
• Optimized for deep-learning on the
device with Apache MXNet, Caffe,
TensorFlow
• Tutorials, sample code, examples,
and pre-built models

AWS DeepLens: How Does It Work?
Deep learning models
run in real time on the device
Deploy pre-trained educational models
Deploy custom models from Amazon SageMaker

AWS DeepLens: How Does It Work?
Deep learning models
run in real time on the device
Stream live video back to AWS
Integrate with Lambda, SNS, SES, etc.
Video analytics with Amazon Rekognition Video

AWS DeepLens: Project Templates
Artistic Style Transfer Hot Dog/Not Hot
Dog Recognition
Object Recognition

Amazon Machine Learning
• Managed service for building ML models and generating
predictions, enabling the development of robust, scalable
smart applications
• No need for extensive background in ML algorithms and
techniques
• Integrated with AWS (S3, Redshift, RDS)
• APIs for Batch and Real-Time Predictions
• Completely serverless

A (kind of) Demo – Titanic Survival

Amazon Mechanical Turk
• The first and foremost challenge in
build AI systems is to build ground
truth data
• Need human intelligence to
annotate speech, vision, or
language data sets
• https://www.mturk.com/

Machine learning process is hard …
Data wrangling
• Setup and manage
notebook environments
• Get data to
notebooks securely
Experimentation
clusters
• Scale/distribute ML
algorithms
Deployment
inference clusters
• Manage and auto scale
inference APIs
• Testing, versioning,
and monitoring
Fetch data
Clean &
format data
Prepare &
transform
data
Train model
Evaluate
model
Integrate with
prod
Monitor/
debug/refresh

…and time consuming
Fetch data
Clean &
format data
Prepare &
transform
data
Train model
Evaluate
model
Integrate with
prod
Monitor/
debug/refresh
6–18
months

Solution: Amazon SageMaker
A managed service
that provides the quickest and easiest way for
your data scientists and developers to get
ML models from idea to production.

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
$

Highly-optimized
machine learning
algorithms
Amazon SageMaker
BuildPre-built notebook
instances

Highly-optimized
machine learning
algorithms
Amazon SageMaker
One-click training
for ML, DL, and
custom algorithms
instances
Easier training with
hyperparameter
optimization
Train

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

Streaming
datasets, for
cheaper training
Train faster, in a
single pass
Greater reliability
on extremely
large datasets
Choice of several
ML algorithms
Algorithms designed for huge datasets

Object and Scene
Detection
Facial
Analysis
Face
Comparison
Facial
Recognition
Celebrity
Recognition
Image
Moderation
Amazon Rekognition Image: Deep
learning-based image analysis service

Real-time face recognition against tens of millions of faces
<0.5 second response time
Up to 10M faces
Enable Immediate response
Rekognition: Real-Time Face Search

How can we apply these powerful
capabilities to video?

Amazon Rekognition Video
Video Analysis

One solution for all
Stored Video
Amazon S3
Video Live Stream
Amazon Kinesis Video Stream
Media Search Index
Unsafe Video Detection
Investigative Analysis
Public Safety Immediate Response
Home Monitoring

Media and Entertainment Public Safety Smart Home
- Search & Filter
- Immediate Response
- Investigative Analysis
- Monitoring
Primary Use Cases

Demo
Traffic Cam - Video
Analysis

Amazon confidential
Rekognition Video: Demo

Amazon Polly
Use Cases
Content Creation
Mobile & Desktop Applications
Internet of Things (IoT)
Education & E-Learning
Telephony
Game Development
Key Features
50+ Voices across 27 languages
Lip-Syncing & Text Highlighting
Fine-grained Voice Control
Custom Vocabularies

Amazon Polly Customers

Amazon Transcribe: Automatic Speech
Recognition
Time Stamps
Support for both
regular &
telephony audio
Punctuation
& formatting
§
S3 Integration
Recognize
Multiple
Speakers
Custom
Vocabulary

Call
Centers
Subtitles for
VOD
Transcribe
meetings
Broadcast closed
captions
Amazon Transcribe: Use Cases

Amazon Lex

Amazon Lex – Use Cases
Informational Bots
Chatbots for everyday consumer requests
Application Bots
Build powerful interfaces to mobile applications
• News updates
• Weather information
• Game scores ….
• Book tickets
• Order food
• Manage bank accounts ….
Enterprise Productivity Bots
Streamline enterprise work activities and improve efficiencies
• Check sales numbers
• Marketing performance
• Inventory status ….
Internet of Things (IoT) Bots
Enable conversational interfaces for device interactions
• Wearables
• Appliances
• Auto ….
Contact Center Bots
Chatbots for customer service IVR
• Account inquiries
• Bill payment
• Service update ….

Amazon Lex Customers

REAL-TIME
TRANSLATION
POWERED BY DEEP
LEARNING
12 LANGUAGE PAIRS
(more to come)
LANGUAGE DETECTION
Amazon Translate: Neural Machine
Translation

Amazon Comprehend: Natural Language
Processing
Sentiment Entities LanguagesKey phrases Topic modeling
Powered By Deep Learning

A m a z o n . c o m , I n c . i s l o c a t e d i n
S e a t t l e , W A a n d w a s f o u n d e d J u l y
5 t h , 1 9 9 4 b y J e f f B e z o s . O u r
c u s t o m e r s l o v e b u y i n g e v e r y t h i n g
f r o m b o o k s t o b l e n d e r s a t g r e a t
p r i c e s
N a m e d E n t i t i e s
• A m a z o n . c o m : O r g a n i z a t i o n
• S e a t t l e , W A : L o c a t i o n
• J u l y 5 t h , 1 9 9 4 : D a t e
• J e f f B e z o s : P e r s o n
K e y p h r a s e s
• O u r c u s t o m e r s
• b o o k s
• b l e n d e r s
• g r e a t p r i c e s
S e n t i m e n t
• P o s i t i v e
L a n g u a g e
• E n g l i s h
Amazon Comprehend: Extract Insight
from Text

Amazon Comprehend: Use Cases
Voice of Customer Analytics
Semantic Search
Knowledge Management/Discovery
Analyzing what customer are saying about your brand, products, and services
Making search smarter by searching on keyphrase, sentiment, and topic
Organizing documents, categorizing by topic and personalizing experiences

Multilingual Social Analytics
Twitter
Stream API
Kinesis
Lambda
S3 Athena
Translate Comprehend
Transcribe

Multi-lingual Social Analytics: Poll
How long did this take to build?
How much does it cost to run per day?
1 day
$17/day
(to analyze tweets for AWS-size
customer)

Starting point for AI/ML on AWS:
h t t p s : / / a w s . a m a z o n . c o m / m a c h i n e - l e a r n i n g /

AWS Free Tier
h t t p s : / / a w s . a m a z o n . c o m / f r e e /

Q & A

Thank you!
vladsim@amazon.com

Artificial Intelligence (Machine Learning) on AWS: How to Start

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