Machine learning (ML) offers innovation for every business. But until recently, developing ML models took time and effort, making it difficult for developers to get started. In this session, we demonstrate how Amazon SageMaker, a fully managed service that enables developers and data scientists to build, train, and deploy ML models at scale, overcomes these barriers. We review its capabilities, including data labeling, model building, model training, tuning, and production hosting.

1. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
Machine learning for developers & data
scientists with Amazon SageMaker
A I M 2 0 3
Charley Frazier
Manager, Machine Learning
Ibotta
Vikrant Kahlir
Enterprise Solution Architect Amazon
Web Services

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Put machine learning in the
hands of every developer
Our mission at AWS
Our mission at AWS
Put machine learning in the
hands of every developer

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More machine learning happens on AWS than anywhere else

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The Amazon Machine Learning stack:
Broadest & deepest set of capabilities
AI Services
Easily add intelligence to applications without machine learning skills
Vision | Documents | Speech | Language | Chatbots | Forecasting | Recommendations
ML Services
Build, train, and deploy machine learning models quickly and easily
Data labeling | Prebuilt algorithms & notebooks | One-click training and deployment
ML frameworks &
infrastructure
Flexibility & choice, highest-performing infrastructure
Support for ML frameworks | Compute options purpose built for ML

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The Amazon ML stack: Broadest & deepest set of capabilities
AI services
Vision
R E K O G N I T I O N
I M A G E
R E K O G N I T I O N
V I D E O
T E X T R A C T
Speech
P O L L Y T R A N S C R I B E
Language Chatbots Forecasting Recommendations
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 X F O R E C A S T P E R S O N A L I Z E
ML services
A M A Z O N
S A G E M A K E R
G R O U N D T R U T H
N O T E B O O K S
A L G O R I T H M S
A W S M A R K E T P L A C E
R E I N F O R C E M E N T
L E A R N I N G
T R A I N I N G
O P T I M I Z A T I O N
( N E O )
D E P L O Y M E N T
H O S T I N G
ML frameworks &
infrastructure
Frameworks Interfaces Infrastructure
E C 2 P 3
& P 3 d n
E C 2 C 5 F P G A s
A W S I o T
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
RL Coach

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Amazon SageMaker
Build, train, and deploy machine learning models at scale

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Prebuilt
notebooks
for common
problems
Amazon SageMaker
Build, train, and deploy machine learning models at scale

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Prebuilt
notebooks
for common
problems
Built-in, high-
performance
algorithms
Amazon SageMaker
Build, train, and deploy machine learning models at scale

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2
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Prebuilt
notebooks
for common
problems
Built-in, high-
performance
algorithms
One-click training
on the highest-
performing
infrastructure
Amazon SageMaker
Build, train, and deploy machine learning models at scale

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2
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Prebuilt
notebooks
for common
problems
Built-in, high-
performance
algorithms
One-click training
on the highest-
performing
infrastructure
Model
optimization
Amazon SageMaker
Build, train, and deploy machine learning models at scale

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2
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Prebuilt
notebooks
for common
problems
Built-in, high-
performance
algorithms
One-click training
on the highest-
performing
infrastructure
Model
optimization
One-click
deployment
Amazon SageMaker
Build, train, and deploy machine learning models at scale

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2
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Prebuilt
notebooks
for common
problems
Built-in, high-
performance
algorithms
One-click training
on the highest-
performing
infrastructure
Model
Optimization
One-click
deployment
Fully managed
with
auto-scaling for
75% less cost
Flexibility & choice with modules for ML developers & data scientists
Amazon SageMaker
Build, train, and deploy machine learning models at scale

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Successful models require high-quality data

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Amazon SageMaker Ground Truth
Label machine learning training data easily and accurately
Easily integrate
human labelers
Get accurate
results
K e y f e a t u r e s
Automatic labeling
via machine learning
Ready-made and
custom workflows
Label
management
Quickly label
training data
Private and public
human workforce

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Subscribe in a
single click
Available in
Amazon SageMaker
Key features
Automatic labeling via machine learning
IP protection
Automated billing and metering
Browse or search
AWS Marketplace
Sellers
Broad selection of paid, free, and
open-source algorithms and models
Data protection
Discoverable on your AWS bill
Buyers
AWS Marketplace for machine learning
ML algorithms and models available instantly

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Over 200 algorithms and models
Natural language
processing
Grammar & parsing Text OCR Computer vision
Named entity
recognition
Video classification
Speech recognition Text-to-speech Speaker identification Text classification 3D images Anomaly detection
Text generation Object detection Regression Text clustering Handwriting recognition Ranking
A v a i l a b l e a l g o r i t h m s & m o d e l s
S e l e c t e d v e n d o r s

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Model optimization is extremely complex

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Amazon SageMaker Neo: Train once, run anywhere
Neo

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Amazon SageMaker Neo
Train once, run anywhere with 2x the performance
K e y f e a t u r e s
Open-source device runtime and compiler
1/10 the size of original frameworks

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So what’s next for
machine learning?
How do you teach machine learning models to make
decisions when thereis no training data?

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Introducing reinforcement learning (RL)
Reinforcement learning
(RL)
Supervised learning
(ASR, computer vision)
Unsupervised learning
(anomaly detection,
identifying text topics)
Amount of labeled training data required
Complexityofdecisions

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Amazon SageMaker RL
Reinforcement learning for every developer and data scientist
2D & 3D physics
environments and
OpenGym support
Support Amazon Sumerian, AWS
RoboMaker, and the open source
Robotics Operating System (ROS)
project
Fully
managed
Example notebooks
and tutorials
K E Y F E A T U R E S

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Choose and
optimize your ML
algorithm
Train and
tune models
Scale and manage
the production
environment
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2
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Amazon SageMaker:
Build, train, and deploy ML models at scale
Prebuilt notebooks
for common problems
Built-in,
high-performance
algorithms
Amazon SageMaker
Ground Truth
Collect and prepare
training data
One-click training on
the highest-
performing
infrastructure
Set up and manage
environments for
training
AWS Marketplace
for machine
learning
Amazon EC2 P3
instances
Model
optimization
Amazon SageMaker
RL
Amazon SageMaker
Neo
One-click deployment
Deploy models in
production
Fully managed with
auto-scaling for 75%
less
Amazon Elastic
Inference
Deploy everywhere
Flexibility & choice with modules for ML developers & data scientists

24. S U M M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Machine learning at Ibotta
Charley Frazier
Manager, Machine Learning
Ibotta

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View
content1 Engage
&unlock2 Verify
purchase3 Cash
out4
Ibotta: How it works

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M I S S I O N
Become the most efficient way to generate
incremental purchases
Make every purchase rewarding
Discoverthemostrelevantcontent
• Recommendation engines
• Search relevancy
• Personalized communications
Maximizemarketingreturnoninvestment
• Optimize rebate values
• Run & analyze A/B tests
• Provide real-time performance data
Ibotta: The big picture

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Decrease time from
ideation to production
Make it easier to consume
ML services
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110
0
0
+
ML Model
Repository
Months Minutes
Development Production
Machine learning
everywhere
MODEL 2
MODEL 3
MODEL 1
IbottaML
FrameworksPatternsStandards
Personalization Search Optimization
Amazon SageMaker
Amazon SageMaker Amazon SageMaker
Amazon SageMaker Amazon SageMaker Amazon SageMaker Amazon SageMaker
Amazon SageMaker
Amazon DynamoDB
Amazon ElastiCache
Amazon SageMaker: Ibotta strategic focus areas

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Amazon SageMaker: Ibotta ML architecture
Keybenefits
• Consistent workflows for
real time and batch
• Online & offline feature
banks
• Framework agnostic model
development
• Fully managed ML
endpoints

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Amazon SageMaker: Low-latency feature embeddings
TensorFlow Autoencoder

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Amazon SageMaker: Real-time search relevancy
ExampleAmazonSageMaker
endpoint

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Amazon SageMaker: Key learnings & future direction
• Maintain a repo with internal
examples
• Define and standardize your
integration patterns
• Pair Amazon SageMaker with
open-source libraries
• Create initial endpoints with
mock data
• Continued investment in
managed ML infrastructure
• Hundreds of endpoints
• Integration with other ML
services
• Rapid prototyping to rapid
productionization
Key learnings Future direction

32. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
Additional resources
- Amazon SageMaker product page:
• https://aws.amazon.com/sagemaker
- Amazon SageMaker on the AWS Management Console:
• https://console.aws.amazon.com/sagemaker
- Amazon SageMaker blogs:
• https://aws.amazon.com/blogs/machine-learning/category/artificial-intelligence/sagemaker
- Amazon SageMaker 10-minute tutorial:
• https://aws.amazon.com/getting-started/tutorials/build-train-deploy-machine-learning-model-
sagemaker

33. Thank you!
S U M M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Vikrant Kahlir
Enterprise Solutions Architect
Amazon Web Services