8. Aggressive migration
New data created on AWS
Data
Training Prediction
PBs of existing data
The Challenge For Artificial Intelligence: SCALE
9. Tons of GPUs
Elastic capacity
Training
Prediction
Pre-built images
Aggressive migration
New data created on AWS
Data
PBs of existing data
The Challenge For Artificial Intelligence: SCALE
10. Tons of GPUs and CPUs
Serverless
At the Edge, On IoT Devices
Prediction
Tons of GPUs
Elastic capacity
Training
Pre-built images
Aggressive migration
New data created on AWS
Data
PBs of existing data
The Challenge For Artificial Intelligence: SCALE
21. Text In, Life-like Speech Out
Amazon Polly
“Today in Seattle, WA
it’s 11°F”
“Today in Seattle Washington
it’s 11 degrees Fahrenheit”
22. “Today in Seattle, WA, it’s 11°F”
‘"We live for the music" live from the Madison Square Garden.’
1. Automatic, Accurate Text Processing
A Focus On Voice Quality & Pronunciation
23. 2. Intelligible and Easy to Understand
1. Automatic, Accurate Text Processing
A Focus On Voice Quality & Pronunciation
24. 2. Intelligible and Easy to Understand
3. Add Semantic Meaning to Text
“Richard’s number is 2122341237“
“Richard’s number is 2122341237“
Telephone Number
1. Automatic, Accurate Text Processing
A Focus On Voice Quality & Pronunciation
25. 2. Intelligible and Easy to Understand
3. Add Semantic Meaning to Text
4. Customized Pronunciation
“My daughter’s name is Kaja.”
“My daughter’s name is Kaja.”
1. Automatic, Accurate Text Processing
A Focus On Voice Quality & Pronunciation
26. Duolingo voices its language learning service Using Polly
Duolingo is a free language learning service where
users help translate the web and rate translations.
With Amazon Polly our users
benefit from the most lifelike
Text-to-Speech voices
available on the market.
Severin Hacker
CTO, Duolingo
”
“
• Spoken language crucial for language
learning
• Accurate pronunciation matters
• Faster iteration thanks to TTS
• As good as natural human speech
34. Rekognition Customers
Media and Entertainment
Public Safety
Law Enforcement
Digital Asset Management
Influencer Marketing
Digital Advertising
Education
Consumer Storage
35. Media Case Study
Identify who is on camera at what time for each of 8 networks so that
recorded video streams can be indexed and searched
Video frame-sampling facial recognition solution using Amazon
Rekognition:
• Indexed 97,000 people into a face collection in 1 day
• Sample frames every 6 secs and test for image variance
• Upload images to S3 and call Rekognition to find best facial match
• Store time stamp and faceID metadata
36. Influencer Marketing Case Study
Associate influencers with objects and scenes in social media images in
order to create high impact campaigns for clients
Using Rekognition for metadata extraction:
• Create rich media indexes of images from social media feeds, which the
application associates with influencers
• Enable analytics to profile environments where influence is strongest
• Connect client brands with the influencers most likely to have impact
37. Bynder allows you to easily create, find and use
content for branding automation and marketing
solutions.
With our new AI capabilities,
Bynder’s software… now allows
users to save hours of admin
labor when uploading and
organizing their files, adding
exponentially more value.
Chris Hall
CEO, Bynder
”
“
With Rekognition, Bynder revolutionizes marketing admin tasks with AI capabilities
41. Amazon Lex
Utterances
Spoken or typed phrases that invoke
your intent
BookHotel
Intents
An Intent performs an action in
response to natural language user
input
Slots
Slots are input data required to fulfill
the intent
Fulfillment
Fulfillment mechanism for your intent
42. “Book a Hotel”
Book Hotel
NYC
“Book a Hotel in
NYC”
Automatic Speech
Recognition
Hotel Booking
New York City
Natural Language
Understanding
Intent/Slot
Model
UtterancesHotel Booking
City New York City
Check In Nov 30th
Check Out Dec 2nd
“Your hotel is booked for
Nov 30th”
Polly
Confirmation: “Your hotel
is booked for Nov 30th”
“Can I go ahead
with the booking?
a
in
49. And a few more examples…
Fraud detection Detecting fraudulent transactions, filtering spam emails,
flagging suspicious reviews, …
Personalization Recommending content, predictive content loading,
improving user experience, …
Targeted marketing Matching customers and offers, choosing marketing
campaigns, cross-selling and up-selling, …
Content classification Categorizing documents, matching hiring managers and
resumes, …
Churn prediction Finding customers who are likely to stop using the service,
free-tier upgrade targeting, …
Customer support Predictive routing of customer emails, social media
listening, …
58. One-Click
Deep Learning
AWS Deep Learning AMIs
Amazon Linux & Ubuntu
Up to~40k CUDA cores
Apache MXNet
TensorFlow
Theano
Keras
Caffe
CNTK
Torch
Pre-configured CUDA drivers
Anaconda, Python3
Out-of-the-box Tutorials
+ CloudFormation template
+ Container Image
Available in the AWS Marketplace
59. Programmable Portable High Performance
Near linear scaling
across hundreds of GPUs
Highly efficient
models for mobile
and IoT
Simple syntax,
multiple languages
Most Open Best On AWS
Optimized for
deep learning on AWS
Accepted into the
Apache Incubator
Apache MXNet
60. Integrate with
AWS Services
Bring Scalable Deep
Learning to AWS
Services such as
Amazon EMR, AWS
Lambda and
Amazon ECS.
Foundation for
AI Services
AmazonAI API
Services, Internal AI
Research, Amazon
Core AI
Development
Leverage the
Community
Community brings
velocity and
innovation with no
single project owner
or controller
Amazon Strategy | Apache MXNet
66. Create your own Basquiat with Deep Learning
https://becominghuman.ai/create-your-own-basquiat-with-deep-learning-for-much-less-than-110-million-314aa07c9ba8
72. Elastic GPUs On EC2
P2M4 D2 X1 G2T2 R4 I3 C5
General Purpose
GPUGeneral Purpose
Dense storage Large memory
Graphics
intensive
Memory intensive High I/O
Compute intensiveBurstable
Lightsail
Simple VPS
F1
FPGAs
Instance Families
73. 1GiB
GPU Memory
2 GiB
4 GiB
8 GiB
C u r r e n t
G e n e r a t i o n
E C 2
I n s t a n c e
Elastic GPUs For EC2:
GPU Acceleration For Graphics Workloads
74. FPGA Images Available In AWS Marketplace
F 1 I n s t a nc e
W i t h y o u r c u s t o m l o g i c
r u n n i n g o n a n F P G A
D e v e l o p , s i m u l a t e , d e b u g
& c o m p i l e y o u r c o d e
P a c k a g e a s F P G A
I m a g e s
F1 Instances:
Bringing Hardware Acceleration To All
75. “Our results indicate that FPGAs may
become the platform of choice for
accelerating next-generation DNNs.”
76. Up to
40 thousand parallel processing cores
70 teraflops (single precision)
over 23 teraflops (double precision)
Instance Size GPUs GPU Peer
to Peer
vCPUs Memory
(GiB)
Network
Bandwidth*
p2.xlarge 1 - 4 61 1.25Gbps
p2.8xlarge 8 Y 32 488 10Gbps
p2.16xlarge 16 Y 64 732 20Gbps
*In a placement group
Amazon EC2 P2 Instances
Also feel free to also contact me on twitter or my email and I will do my best to connect you with the right folks.
as a system or service which can perform tasks that usually require human intelligence, such as visual perception, speech recognition, decision-making or translation
as a system or service which can perform tasks that usually require human intelligence, such as visual perception, speech recognition, decision-making or translation
Training requires a ton of compute power
Specifically GPUs, which are super expensive
And which can sit idle when you’re not training them
Scale of prediction
Netflix and cost of implementation
Prediction for all models
Prediction at the edge/on device
Today Ai is everywhere is Amazon
From recommendation pages, to fulfillment centers, Prime Air Drones, Amazon Go and Alexa
Today Ai is everywhere is Amazon
From recommendation pages, to fulfillment centers, Prime Air Drones, Amazon Go and Alexa
Amazon Web Services provides a rich ecosystem to help you build smarter applications. In this context, it is worth highlighting the higher level AI services based on deep learning algorithms, like Amazon Rekognition, an image recognition service, Amazon Polly, a text to speech synthesizer, and Amazon Lex, a voice and text chatbot service.
We also provide the infrastructure including GPU EC2 instances for fast parallel processing which you can use in combination with any of the popular deep learning libraries like Apache mxnet, Tensorflow, Theano, etc, all of which are available on the AWS deep learning AMI.
For your general machine learning purposes, you can also use EC2, Amazon Elastic MapReduce and Spark with SparkML to run any machine learning algorithm. Another popular library is the python scikit-learn, which you can deploy on AWS Lambda or containers, or EC2.
So what I am trying to convey is that there is a lot of choice, which basically boils down to picking the right tool for the right job, where you can make trade-offs between ‘do your own’ with all the flexibility, or picking a managed solution which allows you to get results fast without having to do the heavy lifting.
Amazon Web Services provides a rich ecosystem to help you build smarter applications. In this context, it is worth highlighting the higher level AI services based on deep learning algorithms, like Amazon Rekognition, an image recognition service, Amazon Polly, a text to speech synthesizer, and Amazon Lex, a voice and text chatbot service.
We also provide the infrastructure including GPU EC2 instances for fast parallel processing which you can use in combination with any of the popular deep learning libraries like Apache mxnet, Tensorflow, Theano, etc, all of which are available on the AWS deep learning AMI.
For your general machine learning purposes, you can also use EC2, Amazon Elastic MapReduce and Spark with SparkML to run any machine learning algorithm. Another popular library is the python scikit-learn, which you can deploy on AWS Lambda or containers, or EC2.
So what I am trying to convey is that there is a lot of choice, which basically boils down to picking the right tool for the right job, where you can make trade-offs between ‘do your own’ with all the flexibility, or picking a managed solution which allows you to get results fast without having to do the heavy lifting.
Today AWS AI offering is broadening – lets have a look at what we have got
The basics are pretty simple, but the service has deep functionality.
You can send the service a simple string of text, and it will generate the life like voice in your choice of 47 different voices.
But it’s not naive of the context of the text. For example, the text here - ‘WA’ and ‘degree F’, that would sound strange if it were spoken out loud.
Instead, Polly will automatically expand the text strings ‘WA’ and ‘degree F’, to ‘Washington’ and ‘degrees fahrenheit’, to create more life like speech. The developer doesn’t have to do anything - just send the text, and get life like voice back.
Today AWS AI offering is broadening – lets have a look at what we have got
Image moderation
Rekognition automatically detects explicit or suggestive adult content in your images, and provides confidence scores.
Clients can request influencers in a key demographic. Rekognition partners use their metadata to identify high quality influencers for targeted campaigns, which may involve paying influencers for product use and social media posts featuring the product.
An influencer’s strength is measured by who is following them on social media
Future use for Reko: identify brands, measure impact, rate influencers
Today AWS AI offering is broadening – lets have a look at what we have got
Amazon Web Services provides a rich ecosystem to help you build smarter applications. In this context, it is worth highlighting the higher level AI services based on deep learning algorithms, like Amazon Rekognition, an image recognition service, Amazon Polly, a text to speech synthesizer, and Amazon Lex, a voice and text chatbot service.
We also provide the infrastructure including GPU EC2 instances for fast parallel processing which you can use in combination with any of the popular deep learning libraries like Apache mxnet, Tensorflow, Theano, etc, all of which are available on the AWS deep learning AMI.
For your general machine learning purposes, you can also use EC2, Amazon Elastic MapReduce and Spark with SparkML to run any machine learning algorithm. Another popular library is the python scikit-learn, which you can deploy on AWS Lambda or containers, or EC2.
So what I am trying to convey is that there is a lot of choice, which basically boils down to picking the right tool for the right job, where you can make trade-offs between ‘do your own’ with all the flexibility, or picking a managed solution which allows you to get results fast without having to do the heavy lifting.
Binary Classification Model
"Is this email spam or not spam?"
"Will the customer buy this product?"
"Is this product a book or a farm animal?"
"Is this review written by a customer or a robot?"
Multiclass Classification Model
"Is this product a book, movie, or clothing?"
"Is this movie a romantic comedy, documentary, or thriller?"
"Which category of products is most interesting to this customer?"
Regression Model
"What will the temperature be in Seattle tomorrow?"
"For this product, how many units will sell?"
"What price will this house sell for?"
Bookstore
Home page 1996 (20 years) when we celebrated 1million titles
Bookstore
Home page 1996 (20 years) when we celebrated 1million titles
There are lots of other examples. Machine learning is being used to filter spam emails, flag inappropriate content, personalize user experience, targeted marketing campaigns, call routing in support centers, social network monitoring, and many more.
The platform protects more than 2 percent of all U.S. e-commerce, and its client base and data requirements are growing at a pace of more than 1,000 percent per year.
to predict propensity to purchase high-end real estate. Some pretty impressive numbers – 400% increase in the number of identified qualified leads in their pipeline and more than 10x reduction is lead acquisition cost.
to predict propensity to purchase high-end real estate. Some pretty impressive numbers – 400% increase in the number of identified qualified leads in their pipeline and more than 10x reduction is lead acquisition cost.
Apache Spark and Spark ML overview
Running Spark ML on Amazon EMR
Interactive notebook options
Building recommendation engines at Zillow Group
Apache Spark and Spark ML overview
Running Spark ML on Amazon EMR
Interactive notebook options
Building recommendation engines at Zillow Group
Amazon Web Services provides a rich ecosystem to help you build smarter applications. In this context, it is worth highlighting the higher level AI services based on deep learning algorithms, like Amazon Rekognition, an image recognition service, Amazon Polly, a text to speech synthesizer, and Amazon Lex, a voice and text chatbot service.
We also provide the infrastructure including GPU EC2 instances for fast parallel processing which you can use in combination with any of the popular deep learning libraries like Apache mxnet, Tensorflow, Theano, etc, all of which are available on the AWS deep learning AMI.
For your general machine learning purposes, you can also use EC2, Amazon Elastic MapReduce and Spark with SparkML to run any machine learning algorithm. Another popular library is the python scikit-learn, which you can deploy on AWS Lambda or containers, or EC2.
So what I am trying to convey is that there is a lot of choice, which basically boils down to picking the right tool for the right job, where you can make trade-offs between ‘do your own’ with all the flexibility, or picking a managed solution which allows you to get results fast without having to do the heavy lifting.
35 Lets have a look at the first offering – which gives you directly access to the lowest level of customization, our Deep Learning Image which let you use the most popular AI frameworks at a single click of the mouse.
On click – Marketplace
AMI supported and maintained by Amazon Web Services for use on EC2.
Designed to provide a stable, secure, and high performance execution environment for deep learning applications running on Amazon EC2.
Popular deep learning frameworks, including MXNet, Caffe, Tensorflow, Theano, CNTK and Torch
as Packages that enable easy integration with AWS, including launch configuration tools and many popular AWS libraries and tools.
It also includes the Anaconda Data Science Platform for Python2 and Python3.
Just after this session, Julien will have a deep dive on MXNET, which is the Deep Learning platform of choice here at Amazon.
$110 million
Early detection
97% early to 14% late
Every year there are about 5.4 million new cases of skin cancer in the United States, and while the five-year survival rate for melanoma detected in its earliest states is around 97 percent, that drops to approximately 14 percent if it’s detected in its latest stages
Amazon Web Services provides a rich ecosystem to help you build smarter applications. In this context, it is worth highlighting the higher level AI services based on deep learning algorithms, like Amazon Rekognition, an image recognition service, Amazon Polly, a text to speech synthesizer, and Amazon Lex, a voice and text chatbot service.
We also provide the infrastructure including GPU EC2 instances for fast parallel processing which you can use in combination with any of the popular deep learning libraries like Apache mxnet, Tensorflow, Theano, etc, all of which are available on the AWS deep learning AMI.
For your general machine learning purposes, you can also use EC2, Amazon Elastic MapReduce and Spark with SparkML to run any machine learning algorithm. Another popular library is the python scikit-learn, which you can deploy on AWS Lambda or containers, or EC2.
So what I am trying to convey is that there is a lot of choice, which basically boils down to picking the right tool for the right job, where you can make trade-offs between ‘do your own’ with all the flexibility, or picking a managed solution which allows you to get results fast without having to do the heavy lifting.
It’s clear we love us some compute
Workloads are not vanilla - they are of different sizes and constraints
Like building a house: you don’t use just one tool, you use lots of tools in your toolbox.
It’s also true with any of these building block services; the right tool for the job you need to get done.
You can also add graphics processing which looks and operates just like a GPU. You can use the same OpenGL code that your application or game already uses, and have them rendered on a GPU. This is perfect if you only need a small part of a GPU more cost effectively (with the smallest option starting at just 1/8th of a GPU), or would like to be able to add graphics processing capabilities to instances which are optimized for I/O, storage, or memory workloads (scaling all the way up to connecting one or more full GPUs).
1/Develop, simulate, debug & compile your code
2/HW development kit and FPGA image
3/Create your own FPGA acceleration that you package into FPGA image
4/ Upload FPGA image
5/ MP
You can also add graphics processing which looks and operates just like a GPU. You can use the same OpenGL code that your application or game already uses, and have them rendered on a GPU. This is perfect if you only need a small part of a GPU more cost effectively (with the smallest option starting at just 1/8th of a GPU), or would like to be able to add graphics processing capabilities to instances which are optimized for I/O, storage, or memory workloads (scaling all the way up to connecting one or more full GPUs).