Learn to identify use cases for machine learning (ML), acquire best practices to frame problems in a way that key stakeholders and senior management can understand and support, and help create the right conditions for delivering successful ML-based solutions to your business.

1. S U M M I T
L ON DON

2. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
Drive Digital Transformation
using AI
A I M 1
STEVEN BRYEN
AWS TECHNICAL & DEVELOPER EVANGELISM
@steven_bryen
BJOERN REINKE
DIRECTOR – SMART METERING, DRAX RETAIL
linkedin.com/in/bjoernreinke

3. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
If I asked you…
“How important is it for your company to invest in Machine Learning
and AI?”
What we often hear:
“We’re not a Machine Learning company.”
”They’re not central to our business.”
“We have more important topics to address.”

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Now, if I asked you…
“How important is it for your organization to build better products
and deliver a better customer experience?”
We always hear:
“This is our number one priority.”
But isn’t this the same question, really?

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More applicants Better outcomes
More data Better analytics
A Flywheel for Data
More data than ever, but how do you use it?

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Myth - AI is the flavour of the month

7. One of the "Founding Fathers" of AI
John McCarthy
1955

8. The (60+ years) rise of Artificial Intelligence

9. The Advent of AI
Algorithms

10. The Advent of AI
Data
Algorithms

11. The Advent of AI
Data
GPUs
& Acceleration
Algorithms

12. The Advent of AI
Data
GPUs
& Acceleration
Cloud
Computing
Algorithms

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AI is the New Normal

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AI is the New Normal – Why?
2018
WW GDP
$ 80 Tn

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2030
WW GDP
*projected
$ 112 Tn
-Sizing the prize
PwC’s Global Artificial
Intelligence Study: Exploiting
the AI Revolution
27 June 2017
2018
WW GDP
$ 80 Tn
AI is the New Normal – Why?

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AI is the New Normal – Why?
2030
WW GDP
*projected
$ 112 Tn
$ 15.7 Tn
AI
-Sizing the prize
PwC’s Global Artificial
Intelligence Study: Exploiting
the AI Revolution
27 June 2017
2018
WW GDP
$ 80 Tn
-Sizing the prize
PwC’s Global Artificial
Intelligence Study: Exploiting
the AI Revolution
27 June 2017

17. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
AI is the New Normal – Why?
2030
WW GDP
*projected
$ 112 Tn
$ 15.7 Tn
AI
-Sizing the prize
PwC’s Global Artificial
Intelligence Study: Exploiting
the AI Revolution
27 June 2017
2018
WW GDP
$ 80 Tn
-Sizing the prize
PwC’s Global Artificial
Intelligence Study: Exploiting
the AI Revolution
27 June 2017
14% OF
Human
Productivity
in 2030
The BUILDERS who embrace AI will be the engines of human acceleration.

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Defining AI - Popularly
When a machine mimics
"cognitive" functions
that we associate with
other humans,
such as "learning"
and "problem solving".

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Defining AI –
Computer Science
The study of
"intelligent agents":
i.e. any human-made agent that
perceives its environment and takes
actions that maximize its chances of
success at some defined goal.

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Data Science vs AI vs Machine Learning vs Deep Learning
Deep Learning
Machine Learning
Data Science
Artificial Intelligence

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AI / ML is not just Predictions
Hint: … it’s a lot more than that

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Supervised Learning
Get better at
finding the
TARGET

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Un-supervised Learning
ASSUME NOTHING,
QUESTION EVERYTHING
Uncover Hidden Patterns
& Relationships

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REWARDPENALTY GOOD ACTIONBAD ACTION
Reinforcing Good actions with REWARDS to
maximize high performing behavior.
Reinforcement Learning

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Reinforcement Learning
Reinforcing Good actions with REWARDS to
maximize high performing behavior.
REWARDPENALTY GOOD ACTIONBAD ACTION

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Amazon AI

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Few Years Ago

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2 Years Ago

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Every day we apply new AI/ML based
improvements to the Amazon business,
at a global scale through AWS

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Amazon Scout, a Machine Learning powered visual shopping tool for personalized
Recommendations …. www.amazon.com/scout

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Challenges that have so-far prevented organizations from adopting Machine
Learning quickly?
ML expertise is
rare
Building and scaling ML
technology is hard
Deploying and operating models
in production is time-
consuming and expensive
A lack of cost-effective,
easy-to-use, and scalable
ML services

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Typical ML Workflow – Customer Pain Points
Data Acquisition & Storage1
Model & Framework selection3
Model Training4
Hyper parameter tuning5
Model testing and simulation6
Model Deployment (inference)7
Data Labelling2
• Iterative and time consuming.
• Image recognition/ object detection
require training with over a million
images with each image containing over
65,000 pixels.
• Speech recognition and synthesis
require training with 100s of hours of
human speech.
Training

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Our Mission in AWS is to put
Machine Learning in the hands
of every Developer

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We set out to build a
Machine Learning platform that is
accessible to every Developer,
Data Scientist & IT Professional

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M L F R A M E W O R K S &
I N F R A S T R U C T U R E
A I S E R V I C E S
R E K O G N I T I O N
I M A G E
P O L L Y T R A N S C R I B E T R A N S L A T E C O M P R E H E N D
C O M P R E H E N D
M E D I C A L
L E XR E K O G N I T I O N
V I D E O
Vision Speech Chatbots
A M A Z O N S A G E M A K E R
B U I L D T R A I N
F O R E C A S TT E X T R A C T P E R S O N A L I Z E
D E P L O Y
Pre-built algorithms & notebooks
Data labeling (G R O U N D T R U T H )
One-click model training & tuning
Optimization ( N E O )
One-click deployment & hosting
M L S E R V I C E S
F r a m e w o r k s I n t e r f a c e s I n f r a s t r u c t u r e
E C 2 P 3
& P 3 d n
E C 2 C 5 F P G A s G R E E N G R A S S E L A S T I C
I N F E R E N C E
Models without training data (REINFORCEMENT LEARNING)
Algorithms & models ( A W S M A R K E T P L A C E )
Language Forecasting Recommendations
NEW NEWNEW
NEW
NEW
NEWNEW
NEW
NEW
RL Coach
Customers need a complete end-to-end Machine Learning stack

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B U I L D T R A I N D E P L O Y
Pre-built algorithms & notebooks
Data labeling (G R O U N D T R U T H )
One-click model training & tuning
Optimization ( N E O )
One-click deployment & hosting
M L S E R V I C E S
Models without training data (REINFORCEMENT LEARNING)
Algorithms & models ( A W S M A R K E T P L A C E )
NEW
NEWNEW
NEW
Build, Train and Deploy Machine Learning Models at
Scale
Pre Built Developer Notebooks, Popular Machine Learning
algorithms, Automatic Model Tuning
10,000+ Customers using SageMaker today
A M A Z O N S A G E M A K E R

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k-means Clustering
k-Nearest Neighbour (k-NN)
PCA
Neural Topic Modelling
Factorisation Machines
Linear Learner – Regression
XGBoost
LDA
Image Classification
Object Detection
Seq2Seq
Linear Learner
Binary Classification
DeepAR Forecasting
ML Prediction now becomes a RESTful API call
Amazon Sagemaker: Built-In Algorithms

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Our Customers are actively Building innovative
ML based Intelligent systems to empower their
businesses, at a global scale through AWS

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© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Deployment time down
by 90% with Amazon
SageMaker
< 1 W E E K6 M O N T H S
BUILDERS can BUILD Intelligent Systems much faster with AWS….

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Delivering powerful insights and
predictions to fans in real time.

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Tensorflow
AWS
Rekognition
Amazon
Polly
Safety Helmet
Recognition
Facial
Recognition
Voice
Message
+
“David,
Please
wear a
helmet!”
Recognition Alerts
Side view Front view

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More ML happens on AWS than anywhere else

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LET’S HEAR FROM…
BJOERN REINKE
DIRECTOR – SMART METERING, DRAX RETAIL

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46. Connecting the System of Record and System of Engagement
System of Intelligence in Drax
Trusted Data
- Effective Controls and Governance
- Data Structure and Models
- Foundation for Reporting, Operations
and Analytics
Enterprise Automation
- AI and Automation Technologies
- Streamlining of Operations
- New Customer Journey
Propositions
31
System of Intelligence
System of Record
System of Engagement
Sources: 02/2018, https://news.greylock.com/the-new-moats-53f61aeac2d9,
Trusted data Insight & Reporting Data Science Automation
Billing &
Settlement
Finance and
Procurement
Sales and
Service
Industry and
Meter Data
Web Mobile Chat
Insight Everywhere
- Analytics at Scale
- Insight Embedded into Ops
- Enabler and Driver for Fact-
based Change
2

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November 2018System of Intelligence49
A System of Intelligence for…
Our Customers
“How do I compare to
my competitors?”
“Other coffee shops
open 17min earlier
but spend 23% more
on refrigeration.”

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A System of Intelligence for…
Our Team Leaders
“Which customers
benefit from
Solar Panels?”
“Here are the
customers with suitable
consumption pattern
and roof space.”

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A System of Intelligence for…
Customer Service Agents
“Welcome to
Drax Tariff Support,
how can we help you?”
“The next customer
has just been online,
searching for
a better tariff.”

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A System of Intelligence for…
Our Leaders
““We should have
delivered £2m
in benefits by now?”
“Here is your
real time
benefits
tracker.”

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“Would you like me
to switch you to an
ethical energy provider,
saving you £200
per year?”
“Yes, please?”

52. Data Science
Data
Warehouse
Our System structure
Analytics
Platform
Reporting &
Visualistion
Fragmented and
Conflicting Data
Sources
ETL
Layer
Data Lake
Amazon s3
AWS Athena
AWS Sagemaker
Extract, Transform and Load
AWS Glue
and Matillion
AWS Redshift

53. Example 1: Customer tailoring?
Easter
Holiday
Electric
Car
Family
Appliances
Family
Fridge
Summer
Holiday

54. How to help new and current customers through their changing world
Example 2: Anomaly Detection with Inawisdom
?!

55. “Just
checking
everything
is ok?”Confidence in anomaly
Anomalies
per month
HighLow
High
Human Input
Self-learning
Automation

56. Thank you!
S U M M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
STEVEN BRYEN
AWS TECHNICAL & DEVELOPER EVANGELISM
@steven_bryen
BJOERN REINKE
DIRECTOR – SMART METERING, DRAX RETAIL
linkedin.com/in/bjoernreinke

57. S U M M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.