Andrew Watts-Curnow, Senior Cloud Architect – Professional Services, APAC, AWS Learn how advances in AI are enabling improvements in customer experience. This is a deep dive using machine learning frameworks for people who are familiar with building their own models. In this session, we will detail a facial recognition solution that can detect known customers and alert customer service staff.

1. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Andrew Watts-Curnow
Senior Cloud Architect, Amazon Web Services
Artificial Intelligence to delight your
customers

2. A Long History of AI at Amazon
Personalized
recommendations
Inventing entirely
new customer
experiences
Fulfillment
automation and
inventory
management
Drones Voice driven
interactions

3. Customer Facial Recognition

4. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.

5. The Amazon AI Stack
Platform Services
Application Services
Frameworks & Interfaces
Caffe2 CNTK
Apache
MXNet
PyTorch TensorFlow Torch Keras Gluon
AWS Deep Learning AMIs
Amazon SageMaker AWS DeepLens
Rekognition Transcribe Translate Polly Comprehend Lex

6. Amazon Rekognition: Face collections
Real-time face search against tens of millions of faces

7. Amazon Rekognition Video
Use case: Customer Facial Recognition
Live Store Camera Amazon Kinesis Video Streams Amazon Rekognition Video Face collection
1. Camera-captured video
streams are processed by
Kinesis Video Streams
2. Amazon Rekognition Video analyses
the video and searches faces on screen
against a collection of millions of faces
Staff AWS Lambda Amazon Kinesis
Streams

8. Build your own AI
Platform Services
Application Services
Frameworks & Interfaces
Caffe2 CNTK
Apache
MXNet
PyTorch TensorFlow Torch Keras Gluon
AWS Deep Learning AMIs
Amazon SageMaker AWS DeepLens
Rekognition Transcribe Translate Polly Comprehend Lex

9. AI & Deep Learning Overview

10. Deep Learning Models

11. AI is still too complicated for everyday developers
Collect and
prepare training
data
Choose and
optimize your
AI 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

12. Amazon SageMaker

13. 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
FRAMEWORKS
Set up and manage
environments for
training
Train and tune
model (trial and
error)
Deploy model
in production
Scale and manage the
production environment
Built-in, high
performance
algorithms
BUILD

14. SageMaker – Hosted notebooks

15. SageMaker – Hosted notebooks

16. Built-in ML Algorithms
Problem Algorithm
Discrete Classification,
Regression
Linear Learner
XGBoost Algorithm
Discrete Recommendations Factorization Machines
Image Classification Image Classification Algorithm
Neural Machine Translation Sequence to Sequence
Time-series Prediction DeepAR
Discrete Groupings K-Means Algorithm
Dimensionality Reduction PCA (Principal Component Analysis)
Topic Determination Latent Dirichlet Allocation (LDA)
Neural Topic Model (NTM)

17. Amazon SageMaker
Pre-built
notebooks for
common
problems
Built-in, high
performance
algorithms
Distributed
training
Hyperparameter
optimization
BUILD TRAIN
Deploy model
in production
Scale and manage
the production
environment

18. SageMaker - Training Jobs

19. SageMaker - Training Jobs

20. Amazon EC2 P3 Instances
• Up to eight NVIDIA Tesla V100 GPUs
• 1 PetaFLOP of computational performance
• 300 GB/s GPU-to-GPU communication (NVLink)
• 16GB 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

21. Cost vs. Time
C
O
S
T
TIME
Single
Machine
Distributed

22. Hyper Parameter Optimization
• Discover a model’s optimal hyper parameters.
• Optimize for accuracy, precision, f1, training time, etc.
• Meta-model regression of learning rate, dropoff,
layers, etc.

23. Amazon SageMaker
Fully managed
hosting with auto-
scaling
One-click
deployment
Pre-built
notebooks for
common
problems
Built-in, high
performance
algorithms
One-click
training
Hyperparameter
optimization
BUILD TRAIN DEPLOY

24. Deployments

25. 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
10
MIN

26. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Get Started with Deep Learning
B u i l d c u s t o m d e e p - l e a r n i n g m o d e l s i n t h e c l o u d u s i n g
A m a z o n S a g e M a k e r , o r u s e t h e c o l l e c t i o n o f p r e t r a i n e d
m o d e l s i n c l u d e d w i t h A W S D e e p L e n s
It takes less than 10 minutes with AWS DeepLens

27. Build It Yourself
Platform Services
Application Services
Frameworks & Interfaces
Caffe2 CNTK
Apache
MXNet
PyTorch TensorFlow Torch Keras Gluon
AWS Deep Learning AMIs
Amazon SageMaker AWS DeepLens
Rekognition Transcribe Translate Polly Comprehend Lex

28. Resources
AWS PARTNER NETWORK
Advisory services
Partner solution architecture
& development
Custom modeling & services
integration
AWS PROSERVE
AI/ML specialists
Data ingestion & processing
Full stack deployment
Team & skill augmentation
Hands-on technical
workshops (Immersion Days)
POCs & pilot delivery
Custom development &
modeling
Application & services
integration
AWS AI SOLUTIONS
LAB & SCIENTISTS
AI/ML specialists
Advisory services
Brainstorming & problem
formulation
AI data preparation
Custom modeling
Application services
integration
Customer education
Technical boot camps

29. Thankyou