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. You'll also hear how and why Intuit is using Amazon SaeMaker on AWS for real-time fraud detection.

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AWS re:INVENT
INTRODUCTION TO AMAZON SAGEMAKER
K U M A R V E N K A T E S W A R , S R . P R O D U C T M A N A G E R , A W S
M O N I C A H S U , G R O U P M A N A G E R , D A T A S C I E N C E , I N T U I T
MCL 3 6 5
November 29, 2017

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Agenda
• Why did we build Amazon SageMaker?
• What is Amazon SageMaker?
• How do I get started using Amazon SageMaker?
• Intuit’s experience 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
Frontend and UX Mobile Backend
and operations
Data and
analytics

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Three types of data-driven development
Retrospective
analysis and
reporting
Here-and-now
real-time processing
and dashboards
Inferences
to enable smart
applications
Amazon Kinesis
Amazon EC2
AWS Lambda
Amazon Redshift
Amazon RDS
Amazon S3
Amazon EMR
Amazon Deep Learning AMI
Amazon Machine Learning
Amazon SageMaker

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Machine Learning Process is Hard…
Fetch data
Clean &
format data
Prepare &
transform
data
Train model
Evaluate
model
Integrate
with prod
Monitor /
debug /
refresh
Data wrangling
• Set up and manage
Notebook environments
• Get data to notebooks
securely
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.
Machine Learning Process is Hard…
Fetch data
Clean &
format data
Prepare &
transform
data
Train model
Evaluate
model
Integrate
with prod
Monitor /
debug /
refresh
Data wrangling
• Set up and manage
Notebook environments
• Get data to notebooks
securely
Experimentation
• Set up and manage
clusters
• Scale/distribute ML
algorithms
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.
Machine Learning Process is Hard…
Fetch data
Clean &
format data
Prepare &
transform
data
Train model
Evaluate
model
Integrate
with prod
Monitor /
debug /
refresh
Data wrangling
• Set up and manage
Notebook environments
• Get data to notebooks
securely
Experimentation
• Set up and manage
clusters
• Scale/distribute ML
algorithms
Deployment
• Set up and
manage inference
clusters
• Manage and auto
scale inference
APIs
• Testing,
versioning, and
monitoring

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… and time consuming ...
Fetch data
Clean &
format data
Prepare &
transform
data
Train model
Evaluate
model
Integrate
with prod
Monitor /
debug /
refresh
6-18
months

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… 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 SageMaker
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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Mo du lar arch it e ct u re so y o u can u se w h at y o u n e e d
Past
Data
Training
algorithm
Model
artifacts
Inference
code
Client
application
Model
Data
Inference
Ground
truth
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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How do I get started using
Amazon SageMaker?

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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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Demo Part 1: Start Training and
Deployment

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Intuit’s Near Real-time
Fraud Detection in AWS
Using Amazon SageMaker
Monica Hsu
Group Manager, Data Science, Intuit

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About Intuit

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POWERING PROSPERITY AROUND THE WORLD
Through technology and data

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History of self-disruption & re-imagination
2010s1980s 1990s 2000s
Intuit Founded
Quicken
DOS
QuickBooks
DOS
TurboTax Online
QuickBooks
Windows
TurboTax Windows
QuickBooks
Mobile
ERA OF DOS ERA OF WINDOWS
QuickBooks
Online Global
SnapTax
QuickBooks & TurboTax
Self-Employed
QuickBooks Online
ERA OF MOBILE & CLOUDERA OF WEB
Quicken
Windows
ERA OF AI, CHAT & VR
2020s
Live Community
(1st AI in an Intuit product)
CUSTOMER-OBSESSED • DESIGN-INSPIRED • TECHNOLOGY-POWERED
2020s
Tax Knowledge Engine

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AI and ML at Intuit: Our three areas of
focus
Customer Care and
Expert Advice
Fraud Detection and
Prevention
Smart Products

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Designed to keep fraudsters out of our
systems and data
Strive to stay several moves ahead of them by leveraging machine
learning-generated insights from data
Near real-time fraud detection in TurboTax:
• Account take-over detection
• Identity theft detection

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Key benefits of Amazon SageMaker @ Intuit
Ad hoc setup and management
of notebook environments
Limited choices for model
deployment
Competing compute resources
across teams
Easy data exploration in
Amazon SageMaker notebooks
Building around virtualization
for flexibility
Auto-scalable model hosting
environment
From To

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Model Hosting
(Amazon SageMaker)
Near real-time fraud detection in AWS
using Amazon SageMaker
Calculate
Features
Reader
Cleanser
Processor
Data
Lookup
Training
Feature Store Model Training
(Amazon SageMaker)
Model
Client Service

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Testimonials from Intuit
“In Amazon SageMaker Notebooks, I like the availability of pre-configured
environments with support for Python 2 and Python 3, TensorFlow with GPU,
etc.”
“My issues were always resolved within a day through Amazon SageMaker
support.”
“One of the key benefits we see in Amazon SageMaker is that by building
around virtualization it can support any machine learning language, package,
or algorithm.”

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Demo Part 2: Evaluate deployed
model

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Questions?

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THANK YOU!