Amazon SageMaker is a fully-managed service that enables data scientists and developers to quickly and easily build, train, and deploy machine learning models, at scale. This session will introduce you the features of Amazon SageMaker, including a one-click training environment, highly-optimized machine learning algorithms with built-in model tuning, and deployment without engineering effort. With zero-setup required, Amazon SageMaker significantly decreases your training time and overall cost of building production machine learning systems.

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Using Amazon SageMaker to build, train, and deploy your ML
Models
Imran Kashif, Sr. Solutions Architect
Nick Brandaleone, Solutions Architect
Anjana Kandalam, Solutions Architect
Ro Mullier, Solutions Architect

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Agenda
• Why did we build Amazon SageMaker?
• What is Amazon SageMaker?
• How do I get started using Amazon SageMaker?
• Q&A

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Why Did We Build Amazon SageMaker?

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Data is part of the fabric of the applications

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Three types of data-driven development
Amazon SageMaker

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Fetch data
Clean &
format data
Prepare &
transform
data
Train model
Evaluate
model
Integrate
with prod
Monitor /
debug /
refresh
Experimentation
• Setup and manage
clusters
• Scale/distribute ML
algorithms
Deployment
• Setup and manage
inference clusters
• Manage and auto
scale inference APIs
• Testing, versioning,
and monitoring

7. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved
Fetch data
Clean &
format data
Prepare &
transform
data
Train model
Evaluate
model
Integrate
with prod
Monitor /
debug /
refresh
Deployment
• Setup and manage
inference clusters
• Manage and auto
scale inference APIs
• Testing, versioning,
and monitoring

8. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved
Fetch data
Clean &
format data
Prepare &
transform
data
Train model
Evaluate
model
Integrate
with prod
Monitor /
debug /
refresh

9. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved
Fetch data
Clean &
format data
Prepare &
transform
data
Train model
Evaluate
model
Integrate
with prod
Monitor /
debug /
refresh

10. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved
… but full of potential
”Machine learning and AI is a horizontal enabling layer. It will empower
and improve every business, every government organization, every
philanthropy — basically there’s no institution in the world that cannot be
improved with machine learning…
We’re in a great position, because of the success of Amazon Web
Services, to be able to put energy into making those techniques easy and
accessible. ”
--Jeff Bezos

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What is Amazon Sagemaker?

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A managed service
that provides the quickest and easiest way for
your data scientists and developers to get
ML models from idea to production.
Amazon SageMaker

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End-to-end
Machine Learning
Platform
Zero setup Flexible model
training
Pay by the second
Introducing Amazon SageMaker

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Amazon-optimized
algorithms using
the AWS SDK…
… or Apache Spark
IM Estimators
Bring your own
deep learning
script…
… or your custom
algorithm Docker
image
Distributed training that works with you

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Streaming
datasets, for
cheaper training
Train faster, in a
single pass
Greater reliability
on extremely large
datasets
Choice of several
ML algorithms
Algorithms designed for huge datasets

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XGBoost, FM, and
Linear for
classification and
regression
Kmeans and PCA
for clustering and
dimensionality
reduction
Image
classification with
convolutional
neural networks
LDA and NTM for
topic modeling,
seq2seq for
translation
More than just general purpose algorithms

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One step
deployment
Low latency, high
throughput, and
high reliability
A/B testing Use your own
model
Quickly deploy in production

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Resizable as you
need
Common tools pre-
installed
Easy access to
your data sources
No servers to
manage
Zero setup for data exploration

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Modular architecture so you can use what you need
Past
Data
Model
artifacts
Model
Amazon SageMaker

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ML compute by the
second starting
at $0.0464/hr
ML storage by the
second
at $0.14
per GB-month
Data processed in
notebooks and
hosting
at $0.016 per GB
Free trial to get
started quickly
Pay as you go and inexpensive

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Start with notebook samples

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Modify to access your data sources

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Train your model

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Deploy your model

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Perform inferences

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