re:Invent 2018 Recap Digital Advertising (Japanese)

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re:Invent 2018 Recap
Digital Advertising
Tatsuya, Yagi
Solutions Architect
18/12/2018

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* [AWS Black Belt Online Seminar] AWS re:Invent 2018 QA
https://aws.amazon.com/jp/blogs/news/webinar-bb-reinvent-2018/
* AWS Slideshare
: https://www.slideshare.net/AmazonWebServices
JP: https://www.slideshare.net/AmazonWebServicesJapan
* AWS Youtube re:Invent 2018 Slideshare
https://www.youtube.com/user/AmazonWebServices

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I n d e x : D i g i t a l M a r k e t i n g s e s s i o n s
Breakout Sessions
ADT201-L – Leadership Session: Digital Advertising – Customer Learning & The Road Ahead
ADT202 – Use Amazon Rekognition to Power Video Creative Asset Production
Chalk Talks
ADT205 – Go Global with Cloud-Native Architecture: Deploy AdTech Services Across Four Continents
CTD406 – Monetizing Live OTT Streams Made Easier
CTD403 – Personalized Ad Targeting Using Real-Time, Content – Aware Machine Learning
Workshops
ADT401 – Real-Time Web Analytics with Amazon Kinesis Data Analytics
Builders Sessions
ADT301 – Create a Serverless Web Event Pipeline
ADT302 – Democratize Data Preparation for Analytics & Machine Learning: A Hands-On Lab
ADT303 – ML for Real-Time Self-Service Trend Detection & Root Cause Analysis: A Hands-On Lab

4. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
:
Breakout Sessions
ADT201-L – Leadership Session: Digital Advertising – Customer Learning & The Road Ahead
ADT202 – Use Amazon Rekognition to Power Video Creative Asset Production
Chalk Talks
ADT205 – Go Global with Cloud-Native Architecture: Deploy AdTech Services Across Four Continents
CTD406 – Monetizing Live OTT Streams Made Easier
CTD403 – Personalized Ad Targeting Using Real-Time, Content – Aware Machine Learning
Workshops
ADT401 – Real-Time Web Analytics with Amazon Kinesis Data Analytics
Builders Sessions
ADT301 – Create a Serverless Web Event Pipeline
ADT302 – Democratize Data Preparation for Analytics & Machine Learning: A Hands-On Lab
ADT303 – ML for Real-Time Self-Service Trend Detection & Root Cause Analysis: A Hands-On Lab

5. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
:
Breakout Sessions
ADT201-L – Leadership Session: Digital Advertising – Customer Learning & The Road Ahead
ADT202 – Use Amazon Rekognition to Power Video Creative Asset Production
Chalk Talks
ADT205 – Go Global with Cloud-Native Architecture: Deploy AdTech Services Across Four Continents
CTD406 – Monetizing Live OTT Streams Made Easier
CTD403 – Personalized Ad Targeting Using Real-Time, Content – Aware Machine Learning
Workshops
ADT401 – Real-Time Web Analytics with Amazon Kinesis Data Analytics
Builders Sessions
ADT301 – Create a Serverless Web Event Pipeline
ADT302 – Democratize Data Preparation for Analytics & Machine Learning: A Hands-On Lab
ADT303 – ML for Real-Time Self-Service Trend Detection & Root Cause Analysis: A Hands-On Lab

6. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Leadership Session: Digital Advertising
Customer Learnings & the Road Ahead
Karl Bunch
Worldwide Technical Leader,
Digital Advertising, AWS
A D T 2 0 1 - L
Alex Collmer
Founder & CEO
VidMob
Dave Pickles
CTO
The Trade Desk

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Agenda
• Intro
• The evolution of AI in advertising – The Trade Desk
• Future of creative - VidMob
• AI in advertising media
• Discussion
• Questions & answers

8. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Agenda
• Intro
• The evolution of AI in advertising – The Trade Desk
• Future of creative - VidMob
• AI in advertising media
• Discussion
• Questions & answers

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AI

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AI: …
= State of the art is AI/ML powered.

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= State of the art is AI/ML powered.
AI: …

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= State of the art is AI/ML powered.
AI: …

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= State of the art is AI/ML powered.
AI: …

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AI as a new data source …
AI

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Amazon Rekognition
•
• API

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Amazon Transcribe: Speech to text
Amazon Transcribe is an automatic speech recognition (ASR) service that makes it easy for
developers to add speech to text capability to their applications
SUPPORT FOR BOTH REGULAR &
TELEPHONY AUDIO S3 INTEGRATION
TIMESTAMPS FOR
TEMPORAL DATA PUNCTUATION AND FORMATTING RECOGNIZE MULTIPLE SPEAKERS
CUSTOM
VOCABULARY
§

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STORM
WORLD SERIES
STOCK MARKET
WASHINGTON
LIBRARY O F
NEW S ARTICLES
Amazon
Comprehend
Amazon Comprehend: Understanding textual data
Natural language processing to discover insights from text

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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
Amazon SageMaker: AI for the masses

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

21. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
:
Breakout Sessions
ADT201-L – Leadership Session: Digital Advertising – Customer Learning & The Road Ahead
ADT202 – Use Amazon Rekognition to Power Video Creative Asset Production
Chalk Talks
ADT205 – Go Global with Cloud-Native Architecture: Deploy AdTech Services Across Four Continents
CTD406 – Monetizing Live OTT Streams Made Easier
CTD403 – Personalized Ad Targeting Using Real-Time, Content – Aware Machine Learning
Workshops
ADT401 – Real-Time Web Analytics with Amazon Kinesis Data Analytics
Builders Sessions
ADT301 – Create a Serverless Web Event Pipeline
ADT302 – Democratize Data Preparation for Analytics & Machine Learning: A Hands-On Lab
ADT303 – ML for Real-Time Self-Service Trend Detection & Root Cause Analysis: A Hands-On Lab

22. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Using Amazon Rekognition to
Power Video Creative Asset
Production
James Kupernik
CTO, VidMob
Joline McGoldrick
SVP, Data and Insights, VidMob

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Today’s Agenda
Creative data
Amazon Rekognition
What we’ve built

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We Are VidMob
VidMob

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28. Data is everywhere
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2

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Creative needs to be:

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creative data ?

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AI

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Amazon Rekognition Image
•

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Amazon Rekognition Video
•

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Amazon Rekognition
/

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:
/
/

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&
Storage Compute/Application
Integration
Machine Learning

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Step Functions
Start
Recognition
Update Status
Process
Response

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Start recognition
data = json.loads(message['Body'])
start_step_function(data['media_id'],
data.get('api_type’))
if api_type is None or len(api_types) == 0:
rekognition.start_label_detection()
elif api_type == ‘Person’
rekognition.start_face_detection()
rekognition.start_person_tracking()

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{
"Label": {
"Confidence": 66.68119812011719,
"Name": "Snow"
},
"Timestamp": 200
},
{
"Label": {
"Confidence": 60.01740264892578,
"Name": "Snowboarding"
},
"Timestamp": 200
},
{
"Label": {
"Confidence": 60.01740264892578,
"Name": "Sport"
},
"Timestamp": 200
},
{
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.

47. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
[ 0, 200, 400, 600, 800, 1000, 1200, 1400, 1600, 2000 ]

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[400] [800] [1000] [ 2000]

49. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
{
"Timestamp": 400,
"Label": {
"Name": "Snowboard",
"Confidence": 61.29149627685547
}
},
{
"Timestamp": 1200,
"Label": {
"Name": "Snowboard",
"Confidence": 56.50600051879883
}
} [400] [800] [1000] [1200]
Processing response data

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, ,
Face: {
BoundingBox: {
Height: 0.036666665226221085,
Left: 0.19312499463558197,
Top: 0.2522222101688385,
Width: 0.020625000819563866
},
Pose: {
Roll: -20.857219696044922,
Pitch: 3.1029155254364014,
Yaw: 13.885883331298828
},
...
}

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Not just for flying
if Yaw < -20:
if Pitch < -20:
return "down-left"
elif Pitch > 20:
return "up-left"
else:
return "left"
elif Yaw > 20:
...

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vid
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vid
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https://www.youtube.com/watch?v=S0KI9YRvusM
AWS re:Invent 2018: Use Amazon Rekognition to Power Video Creative Asset Production
(ADT202)

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Watch Dashboard
Amazon Rekognition
Amazon Rekognition Amazon Transcribe

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x

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22.5 K
Hours
21
Days
+80%
ROI

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61. aws
https://aws.amazon.com/jp/careers/teams/solutionsarchitect/

62. Thank you!
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