In this session, learn how data scientists in the retail industry, from companies like Tapestry, Coach, and Kate Spade, are finding new, counterintuitive consumer insights using AWS artificial intelligence services in a data lake. By leveraging data from various retail systems, including CRM, marketing, e-commerce, point of sale, order management, merchandising, and customer care, we show you how these consumer insights might influence new and interesting retail use cases while establishing a data-driven culture within the organization. Services referenced include Amazon S3, Amazon Machine Learning, Amazon QuickSight, Amazon SageMaker, among others.

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AI/ML with Data Lakes:
Counterintuitive Consumer Insights in Retail
Paul Fryer
Solutions Architect
AWS Enterprise
R E T 2 0 6
Fabio Luzzi
Head of Datalabs and
Customer Analytics
Tapestry Global
Shilpa Mehta
Solutions Architect
AWS Enterprise

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Agenda
• Building a retail data platform
• Machine learning data readiness
• Tapestry’s journey of innovation: Enabling a data and prediction
driven company
• Continuous model training with SageMaker Demo

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Today's conversation
Iterative design for business outcomes
Building for scale and for speed

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Data analyzed for benefit
Available data
Should we collect "all the data" and see what's in it?
COST
VALUE
Investment value of analytics
2010 2015 2020 2025
Datavolume

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Three big indicators of individual behavior
Purchases Movement Influence

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A platform to build business outcomes from data
Purchases
Movement
Influence
Ingest/
Collect
Consume/
visualize
Store
Process/
analyze
1 4
0 9
5
Revenue Lift
Market
acquisition
Customer delight
Brand advocacy
Inventory
optimization
Supply chain
efficiency
...

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Design an on-demand experimentation sandbox
... and only pay for what you use

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Experimentation is fast and cost-efficient
Design once, automatically deploy many times
AWS
CloudFormation

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Quickly find the right outcomes, and turn off the
rest -- Win fast, fail cheap

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AWS offers full capabilities for a retail data platform
Ingest Store ConsumeAnalyze
Amazon Kinesis
streaming data
AWS Database
Migration Service
import from Oracle,
Netezza, and others
AWS Direct Connect
link to data center
AWS Snowball
mail-in bulk data
Internal apps
Customer-facing appsInteroperate with
everything
and many more…
Amazon Redshift
data warehouse
Amazon EMR
unstructured data
Amazon Athena
interactive query
service
Amazon Machine
Learning
ML as-a-service
Amazon QuickSight
visualization
and many more…
Amazon S3
secure, cost-
effective
storage
AWS Glue
managed
ETL

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The Amazon Machine Learning stack
TOOLS
&
SOLUTIONS
APPLICATION SERVICES
A M A Z O N
R E K O G N I T I O N
R E K O G N I T I O N
V I D E O
A M A Z O N
P O L L Y
A M A Z O N
T R A N S C R I B E
A M A Z O N
T R A N S L A T E
A M A Z O N
C O M P R E H E N D
A M A Z O N
L E X
Amazon SageMaker Amazon Mechanical Turk
FRAMEWORKS KERAS
P3
NVIDIA Tesla V100 GPU
accelerated for AI/ML training
Machine learning
AMIs
INFRASTRUCTURE
&
AWS Greengrass
ML
Amazon Deep Learning AMIs
Compute intensive instances for
AI/ML Inference
C5

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AI/ML workflow
Performance monitoring
& adaptation
8
Data acquisition & storage1
Model & framework selection3
Model training4
Hyperparameter tuning5
Model testing and simulation6
Model deployment (inference)7
Data labeling2

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Outcome 1 : Modernize and consolidate
• Insights to enhance business applications and create new digital services
Outcome 2 : Innovate for new revenues
• Personalization, demand forecasting, risk analysis
Outcome 3 : Real-time engagement
• Interactive customer experience, event-driven automation, fraud
detection
Outcome 4 : Automate for expansive reach
• Automation of business processes and physical infrastructure
Business outcomes on a modern data architecture

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Tapestry and AWS:
Enabling a Data & Predictive-driven Company
Fabio Luzzi
Global Head of Datalabs and Customer Analytics
Tapestry

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Diagnostic
Why it
happened?
Descriptive
What is happening
now?
Predictive
What will
happen inthe
future?
Prescriptive
What should I
do about it?
Cognitive &AI
What haven’t I already
considered?
Increasing Analytic Maturity
How to take the company to an increased level of analytic
maturity
Now The future

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Predictive
What will happen inthe
future?
Prescriptive
What should I do about it?
ML & AI framework
Vision Stack: Technical framework
Hindsight + Insights + Foresights
Data Lakes
Cognitive &AI
What haven’t I already considered?
How to take the company to an increased level of analytic
maturity
Self-Service Platforms

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Idea to completion 3 months
We laid the foundation to enable the future:
Tapestry DataLabs Cloud
Secure & AI Ready
Web ServersUser Applications
Tapestry
Decision
Makers
Tapestry
Developers &
Data Scientists
VPN
Tapestry
Hadoop Cluster
(customer data)
Tapestry DataLabs Virtual Private
Cloud
Router
3rd Party Data
Cloud-powered analytics
applications
Tapestry
Corporate
Network
Internet
Machine Learning & AI cloud
lab
Private subnet
Public subnet
AWS Cloud
Data Lake

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Use case one: self service data tools. Project “Falcon”
VIDEO

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Use case two: Machine Learning & AI
Project “Leonardo”
We built, trained and deployed a predictive model using ML&AI to
optimize inventory by sku across ~200 stores
Tapestry
Allocation System
Model Training Model Deployment
+ + +

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Continuous Training with Amazon
SageMaker
Paul Fryer
Solutions Architect
AWS Enterprise

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Continuous training architecture
Amazon API
Gateway
AWS Step
Function
Amazon
SageMaker
Amazon
S3AWS Lambda

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Trigger training with S3 event

37. Thank you!
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