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AI @ AWS: A look into AI Innovations by Amazon and
AWS Customers
Swami Sivasubramanian
VP, Amazon AI
Thousands of Amazon Engineers Focused on AI
Fulfilment & Logistics
Thousands of Amazon Engineers Focused on AI
Fulfilment & Logistics
Search & Discovery
Thousands of Amazon Engineers Focused on AI
Fulfilment & Logistics
Search & Discovery
Existing Products
Fulfilment & Logistics
Search & Discovery
Existing Products
New Products
Thousands of Amazon Engineers Focused on AI
Data Training Prediction
How to build these artificial intelligence systems?
Lots of GPUs and CPUs
Serverless
At the Edge, On IoT Devices
Prediction
The Challenge For Artificial Intelligence: SCALE
Tons of GPUs
Elastic capacity
Training
Pre-built images
Aggressive migration
New data created on AWS
Data
PBs of existing data
“The future is here,
it’s just not evenly distributed yet”
William Gibson
Amazon AIIntelligent Services Powered By Deep Learning!
Amazon AI: Artificial Intelligence In The Hands Of Every Developer
Infrastructure!
Engines!
Services!
Platforms!
Infrastructure! CPU!
Engines! Apache!MXNet! TensorFlow! Caffe2 &!
Caffe! Theano! Keras! CNTK!
Services!Rekognition! Polly!
Chat!
Platforms!
IoT!
Speech!Vision!
Lex!
Mobile!
Amazon AI: Artificial Intelligence In The Hands Of Every Developer
Amazon !ML!
Spark &!EMR! Kinesis! Batch! ECS!
GPU!
Torch!
Rekognition: Object & Scene Detection
Rekognition: Facial Detection
Rekognition: Facial Search
Automating Footage Tagging with Amazon Rekognition
Built in 3 weeks Index against 99,000 people Saving ~9,000 hours a year in labor
Quickly Identifying Persons of Interest with Amazon Rekognition
More than 300,000 photo leads indexed within 1-2 days Identification process went from 2-3 days, to minutes – greatly increasing the ability for law enforcement to act quickly Within 1 week of going live, the solution identified a suspect for a cold case that lead to an arrest through due process
Polly: Life-like Speech Service
Converts text to life-like
speech
48 voices 24 languages Low latency, real time
Fully managed
High quality language education using Amazon Polly Text-to-Speech (TTS)
>170M users: Most popular language-learning platform Each lesson targets different linguistic skills and concepts. Amazon Polly has been the A/B winner against their previous TTS providers in all six voice tests they use to evaluate impact to their business.
• Media and Entertainment!
• Education!
• Accessibility!
• Content Creation!
• Telephony/Contact Center!
• Internet of Things!
• Gaming!
Amazon Polly: Use Cases & Customers
Amazon Lex: Conversational Chatbots
I’d like to book a hotel.
Sure! What city would you like to book it in?
Amazon Lex: Conversational Chatbots
I’d like to book a hotel.
Sure! What city would you like to book it in?
New York City Destination: New York City
Amazon Lex: Conversational Chatbots
I’d like to book a hotel.
Sure! What city would you like to book it in?
New York City
When would you like to check in?
Next Friday
Destination: New York City
Check In: 5/26/2017
Amazon Lex: Conversational Chatbots
Lex Bots
Salesforce
Microsoft Dynamics
Marketo
Zendesk
Web
Devices
Apps Facebook Messenger,
Slack,
Amazon Connect
Mobile Mobile Hub integration
Quickbooks
Customer support made refreshingly easy
Freshbots combine Automatic Speech Recognition (ASR) and Natural Language Understanding (NLU) capabilities of Amazon Lex with in-house workflows to automate customer service Users transact using natural language leading to significant productivity improvements Customer support agents can make intelligent decisions to enhance customer experience
An Amazon Lex voice chatbot to register Heart Walk participants
Nearly 300 Heart Walk fundraising events annually in the U.S., totaling nearly 1 million participants Amazon Lex enables Heart Walk participants to use natural language to register for the event Voice interaction eliminates the need to read text, and accelerates the registration process
Amazon Machine Learning: Predictive models in minutes
Integrated with S3 and Redshift for data access
Efficient common ML
transformations
Simple regression
model generation
Real-time and batch
predictions at scale
Model evaluation and interpretation
tools
Amazon AI: Democratized Artificial Intelligence
Infrastructure! CPU!
Engines! Apache!MXNet! TensorFlow! Caffe2 &!
Caffe! Theano! Keras! CNTK!
Services!Rekognition! Polly!
Chat!
Platforms!
IoT!
Speech!Vision!
Lex!
Mobile!
Amazon !ML!
Spark &!EMR! Kinesis! Batch! ECS!
GPU!
Torch!
One-Click GPU or CPU Deep Learning
AWS Deep Learning AMI
Up to~40k CUDA cores
Apache MXNet
TensorFlow
Theano
Caffe2 & Caffe
Torch
CNTK
Keras
Pre-configured CUDA drivers,
MKL
Anaconda, Python3
Ubuntu and Amazon Linux
+ CloudFormation template
+ Container Image
Computer vision for crowd-sourced maps using deep learning on Amazon
Gained insight from large volumes of unstructured public data to improve global mobility and transportation Accelerated training and inference of deep neural networks for graphic-intensive workloads using AWS EC2 P2 instances Applied their fine grain computer vision algorithms to 142M images and nearly 2M miles of mapped roads
Deep Learning Model Optimization on AWS
Improved accuracy and lower training costs using MXNet and other frameworks using AWS EC2 P2 and SigOpt HPO Dramatically reduces computation costs by 8X or more compared to traditional optimization methods
0.2-0.1...0.7
Input Output
1 1 11 0 10 0 0
3
mx.sym.Pooling(data, pool_type="max", kernel=(2,2), stride=(2,2)
lstm.lstm_unroll(num_lstm_layer, seq_len, len, num_hidden, num_embed)
4 2 2 0 4=Max
13...4
0.2-0.1...0.7
mx.sym.FullyConnected(data, num_hidden=128)
2
mx.symbol.Embedding(data, input_dim, output_dim = k)
Queen
4 2 2 0 2=Avg
Input Weights
cos(w, queen) = cos(w, king) - cos(w, man) + cos(w, woman)
mx.sym.Activation(data, act_type="xxxx")
"relu"
"tanh"
"sigmoid"
"softrelu"
Neural Art
Face Search
Image Segmentation
Image Caption
“People Riding Bikes”
Bicycle, People, Road, SportImage Labels
Image
Video
Speech
Text
“People Riding Bikes”
Machine Translation
“Οι άνθρωποι ιππασίας ποδήλατα”
Events
mx.model.FeedForward model.fit
mx.sym.SoftmaxOutput
Anatomy of a Deep Learning Model
mx.sym.Convolution(data, kernel=(5,5), num_filter=20)
Deep Learning Models
Ex. MXNet User | TuSimple|Autonomous Driving
Ex. MXNet User |TwoSense| Biometrics for Mobile Devices
Deep Learning @Amazon
• Applied Research
• Core Research
• Alexa
• Demand Forecasting
• Risk Analytics
• Search
• Recommendations
• AI Services | Rek, Lex, Polly
• Q&A Systems
• Supply Chain Optimization
• Advertising
• Machine Translation
• Video Content Analysis
• Robotics
• Lots of Computer Vision..
• Lots of NLP/U..
*Teams are either actively evaluating, in development, or transitioning to scale production
Running AI In Production on AWS Today
