講師: Jhen-Wei Huang, Solutions Architect, Amazon Web Services Do your customers love you? Recommendations and Voice of Customer Analytics and AI with AWS

1. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Jhen-Wei Huang (黃振維), Solutions Architect
March 2018
Do your customers love you?
Recommendations and Voice of Customer
Analytics and AI with AWS

2. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Why do we love things?
Fun! Easy Value

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Customer love over time

4. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Customer love over time
Aware
Enroll
Explore
Devour
Peak
Rebalance
Negative
event
Cancel

5. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Customer love over time
Aware
Enroll
Explore
Devour
Peak
Rebalance
Negative
event
Cancel
Increase conversion Reverse conversion

6. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Customer love over time
Aware
Enroll
Explore
Devour
Peak
Rebalance
Negative
event
Cancel
Acquisition
UIandUX
Personalization
Gamification
Monetization
Newservices/content
Exclusivity/reminders
Customercare
Recovery
Increase conversion Reverse conversion

7. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Constant iteration on service/content excellence
Discover
Enroll
Explore
Devour
Peak
Rebalance
Negative experience
Cancel
Tutorial effectiveness
Playback analysis
Custom offers
Social listening
Discovery journeys
Campaign CTR
Priority access
Recovery offers

8. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Audience
ContentOperations
On each iteration, select an experimentation group

9. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Structuring iterations into metrics to experiment for
Audience
• Acquisitions
• Churn
• Where, when, who
• Segmentation
• Cohorts
Content
• Top Content
• Engagement
• Plays per session
• Drop-off
• Referral path
• Recommendations
Operations
• How much
buffering
• Best CDN paths
• Top devices
• Uniques per
platform
Other
• Monetization
• Ad Spend
• Social Media
• Mentions
• Sentiment Analysis
• A/B Feature Testing
• Talent Management

10. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Search
Watch
Listen
Play
Download
Purchase
Rate It
Review It
Sharing
Tagging
Bookmarking
Listening to signals from the Voice of the Customer

11. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
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
...

12. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
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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Start iterations from simple to advanced
1. Search with Boosting
2. Collaborative Filtering
3. Neural Networks
A
B
C

16. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Search With BoostingA
1. Capture Audience Signal Data
2. Record aggregate “Popularity” in the search catalog
 Counts of Views | Downloads
 Trending Sentiment (Positive | Negative)
3. Query Search Engine
4. Use content metadata + “Popularity” to refine
search ranking
5. Enjoy recommendations!

17. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Amazon
Kinesis
Signal Data
(Player, CDN,
Device Logs)
Amazon
Elasticsearc
h
Amazon
S3
vote = count(Event: title, device, time)
VOD Catalog
title: “Toy Dogs and Their Owners”
date: 2014
content: “Cute but surreal look at … ”
votes: 25
title: “The Usual Suspects”
date: 1995
content: “A sole survivor …”
votes: 85
title: “Star Wars 17”
date: 2026
content: “Yoda force ghost returns …
surreal”
votes: 99

18. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
GET /catalog/movies/_search
Search engine adjusts
rankings based on additional contribution of dynamic field “votes”
VOD Catalog
title: “Toy Dogs and Their Owners”
date: 2014
content: “Cute but surreal …”
votes: 25
title: “The Usual Suspects”
date: 1995
content: “A sole survivor …”
votes: 85
title: “Star Wars 17”
date: 2026
content: “Yoda force ghost returns …
surreal”
votes: 99
Rank #1

19. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Speed (Real-time)
Ingest ServingData
sources
Scale (Batch)
Modern data architecture
Insights to enhance business applications, new digital services
Transactions
Web logs /
cookies
ERP
Data analysts
Data scientists
Business users
Engagement platformsConnected
devices
Social media Automation / events
DATA PIPELINES
EVENT PIPELINES

20. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Speed (Real-time)
Ingest ServingData
sources
Scale (Batch)
Modern data architecture
Insights to enhance business applications, new digital services
Transactions
Web logs /
cookies
ERP
Data analysts
Data scientists
Business users
Engagement platformsConnected
devices
Social media Automation / events
DATA PIPELINES
EVENT PIPELINES
Data
Event
Insights
Data
Lake

21. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Speed (Real-time)
Ingest ServingData
sources
Scale (Batch)
Modern data architecture
Insights to enhance business applications, new digital services
Transactions
Web logs /
cookies
ERP
Data analysts
Data scientists
Business users
Engagement platformsConnected
devices
Social media Automation / events
DATA PIPELINES
EVENT PIPELINES
Data
Event
Insights
Data
Lake
ML / Analytics

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A fully managed service that enables data scientists and developers to quickly and easily
build machine-learning based models into production smart applications.
Amazon SageMaker

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© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Highly-optimized
machine learning
algorithms
Amazon SageMaker
BuildPre-built notebook
instances

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© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Highly-optimized
machine learning
algorithms
One-click training
for ML, DL, and
custom algorithms
BuildPre-built notebook
instances
Easier training with
hyperparameter
optimization
Train
Amazon SageMaker

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© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
One-click training
for ML, DL, and
custom algorithms
Easier training with
hyperparameter
optimization
Highly-optimized
machine learning
algorithms
Deployment
without
engineering effort
Fully-managed
hosting at scale
BuildPre-built notebook
instances
Deploy
Train
Amazon SageMaker

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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

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Amazon ECR
Model Training (on EC2)
Amazon SageMaker
Client application
Training code

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Amazon ECR
Model Training (on EC2)
Trainingdata
Training code Helper code
Client application
Training code
Amazon SageMaker

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Amazon ECR
Model Training (on EC2)
Trainingdata
Modelartifacts
Training code Helper code
Client application
Inference code
Training code
Amazon SageMaker

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Amazon ECR
Model Training (on EC2)
Model Hosting (on EC2)
Trainingdata
Modelartifacts
Training code Helper code
Helper codeInference code
Client application
Inference code
Training code
Amazon SageMaker

31. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Amazon ECR
Model Training (on EC2)
Model Hosting (on EC2)
Trainingdata
Modelartifacts
Training code Helper code
Helper codeInference code
Client application
Inference code
Training code
Inference requestInference
response
Inference Endpoint
Amazon SageMaker

32. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Amazon ECR
Model Training (on EC2)
Model Hosting (on EC2)
Trainingdata
Modelartifacts
Training code Helper code
Helper codeInference code
GroundTruth
Client application
Inference code
Training code
Inference requestInference
response
Inference Endpoint
Amazon SageMaker

33. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Amazon SageMaker
1 2 3 4
I I I I
Notebook Instances Algorithms ML Training Service ML Hosting Service

34. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
1
I
Notebook Instances
Zero Setup For Exploratory Data Analysis
Authoring &
Notebooks
ETL Access to AWS
Database services
Access to S3 Data
Lake
• Recommendations/Personalization
• Fraud Detection
• Forecasting
• Image Classification
• Churn Prediction
• Marketing Email/Campaign Targeting
• Log processing and anomaly detection
• Speech to Text
• More…
“Just add data”

35. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
2
I
Algorithms
Training code
• Matrix Factorization
• Regression
• Principal Component Analysis
• K-Means Clustering
• Gradient Boosted Trees
• And More!
Amazon provided Algorithms
Bring Your Own Script (IM builds the Container)
IM Estimators in
Apache Spark Bring Your Own Algorithm (You build the Container)
Amazon SageMaker: 10x better algorithms

36. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Built-in ML Algorithm
h t t p s : / / d o c s . a w s . a m a z o n . c o m / s a g e m a k e r / l a t e s t / d g / a l g o s . h t m l
Problem Algorithm Learning Typ
Discrete Classification,
Regression
Linear Learner Supervised
XGBoost Algorithm Supervised
Discrete Recommendations Factorization Machines Supervised
Image Classification Image Classification Algorithm Supervised, CNN
Neural Machine Translation Sequence to Sequence Supervised, seq2seq
Time-series Prediction DeepAR Supervised, RNN
Discrete Groupings K-Means Algorithm Unsupervised
Dimensionality Reduction PCA (Principal Component Analysis) Unsupervised
Topic Determination Latent Dirichlet Allocation (LDA) Unsupervised
Neural Topic Model (NTM) Unsupervised,
Neural Network Based

37. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Managed Distributed Training with Flexibility
Training code
• Matrix Factorization
• Regression
• Principal Component Analysis
• K-Means Clustering
• Gradient Boosted Trees
• And More!
Amazon provided Algorithms
Bring Your Own Script (IM builds the Container)
Bring Your Own Algorithm (You build the Container)
3
I
ML Training Service
Fetch Training data
Save Model Artifacts
Fully
managed –
Secured–
Amazon ECR
Save Inference Image
IM Estimators in
Apache Spark
CPU GPU HPO

38. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
4
I
ML Hosting Service
Amazon ECR
30 50
10 10
ProductionVariant
Model Artifacts
Inference Image
Model versions
Versions of the same
inference code saved in
inference containers.
Prod is the primary one,
50% of the traffic must
be served there!
One-Click!
EndpointConfiguration
Inference Endpoint
Amazon Provided Algorithms
Amazon SageMaker
Easy Model Deployment to Amazon SageMaker
InstanceType: c3.4xlarge
InitialInstanceCount: 3
ModelName: prod
VariantName: primary
InitialVariantWeight: 50

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Demo

40. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Speed (Real-time)
Ingest ServingData
sources
Scale (Batch)
Modern data architecture
Insights to enhance business applications, new digital services
Transactions
Web logs /
cookies
ERP
Data analysts
Data scientists
Business users
Engagement platformsConnected
devices
Social media Automation / events
DATA PIPELINES
EVENT PIPELINES
Data
Event Engagement
Insights
Data
Lake
ML / Analytics
Predict /
Recommend
AI

41. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Speed (Real-time)
Ingest ServingData
sources
Scale (Batch)
Modern data architecture
Insights to enhance business applications, new digital services
Transactions
Web logs /
cookies
AWS Database
Migration
AWS Direct
Connect
Internet
Interfaces Amazon S3
Raw Data
Amazon S3
Staged Data
(Data Lake)
Amazon EMR
ETL
AWS
Cloud Trail
AWS
IAM
Amazon
CloudWatch
AWS
KMS
Data analysts
Data scientists
Business users
Engagement platforms
Amazon
Kinesis
Connected
devices
Social media
Advanced
Analytics
MLlib
Event Capture
Amazon Kinesis
Stream Analysis
Amazon EMR Event Scoring
Amazon AI
Event Handler
AWS Lambda Response Handler
AWS Lambda
Automation / events
Schemaless
Amazon ElasticSearch
Direct Query
Amazon Athena
Near-Zero Latency
Amazon DynamoDB
Semi/Unstructured
Amazon EMR
ERP
Enterprise Apps
Oracle, Teradata,
SQL Server, MySQL, et
Data Warehouse
Amazon Redshift

42. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Earn your customer love - iterate and improve
Aware
Enroll
Explore
Devour
Peak
Rebalance
Negative
event
Cancel
Acquisition
UIandUX
Personalization
Gamification
Monetization
Newservices/content
Exclusivity/reminders
Customercare
Recovery
Increase conversion Reverse conversion

43. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Thank you!