2018/4/18 に開催された，AWS Machine Learning Tech Night - April 2018 - の Sunil Mallya による Amazon SageMaker についての発表資料です． https://awsj-ml.connpass.com/event/83623/

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Amazon SageMaker
Sunil Mallya
Sr. AI Solutions Architect/ ML Solutions Lab / Amazon Web Services

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One-click training
for ML, DL, and
custom algorithms
Easier training with
hyperparameter
optimization
Highly-optimized
machine learning
algorithms
Deployment
without
engineering effort
Fully-managed
hosting at scale
Build
Pre-built
notebook
instances
Deploy
Train
Amazon SageMaker

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Training
code
• Matrix Factorization
• Regression
• Principal Component Analysis
• K-Means Clustering
• Gradient Boosted Trees
• And More!
Amazon provided Algorithms
Bring Your Own Container
Bring Your Own Script
IM Estimators in
Apache Spark
Amazon SageMaker: model options

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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
Amazon SageMaker: 10x better algorithms

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Infinitely scalable algorithms

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GPU State
Streaming

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Data Size
Memory
Data Size
Time/Cost
Streaming

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GPU State
GPU State
GPU State
Distributed

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Shared State
GPU
GPU
GPU Local
State
Shared
State
Local
State
Local
State

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Cost vs. Time
$$$$
$$$
$$
$
Minutes Hours Days Weeks Months
Best Alternative
Amazon SageMaker

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Linear Learner
Regression (mean squared error)
SageMaker Other
1.02 1.06
1.09 1.02
0.332 0.183
0.086 0.129
83.3 84.5
Classification (F1 Score)
SageMaker Other
0.980 0.981
0.870 0.930
0.997 0.997
0.978 0.964
0.914 0.859
0.470 0.472
0.903 0.908
0.508 0.508
30 GB datasets for web-spam and web-url classification
0
0.2
0.4
0.6
0.8
1
1.2
0 5 10 15 20 25 30
CostinDollars
Billable time in Minutes
sagemaker-url sagemaker-spam other-url other-spam

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0
1
2
3
4
5
6
7
8
10 100 500
BillableTimeinMinutes Number of Clusters
sagemaker other
K-Means Clustering
k SageMaker Other
Text
1.2GB
10 1.18E3 1.18E3
100 1.00E3 9.77E2
500 9.18.E2 9.03E2
Images
9GB
10 3.29E2 3.28E2
100 2.72E2 2.71E2
500 2.17E2 Failed
Videos
27GB
10 2.19E2 2.18E2
100 2.03E2 2.02E2
500 1.86E2 1.85E2
Advertising
127GB
10 1.72E7 Failed
100 1.30E7 Failed
500 1.03E7 Failed
Synthetic
1100GB
10 3.81E7 Failed
100 3.51E7 Failed
500 2.81E7 Failed
Running Time vs. Number of Clusters
~10x Faster!

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DeepAR: Time Series Forecasting
Mean absolute
percentage error
P90 Loss
DeepAR R DeepAR R
traffic
Hourly occupancy rate of 963
Bay Area freeways
0.14 0.27 0.13 0.24
electricity
Electricity use of 370
homes over time
0.07 0.11 0.08 0.09
pageviews
Page view hits
of websites
10k 0.32 0.32 0.44 0.31
180k 0.32 0.34 0.29 NA
One hour on p2.xlarge, $1
Input
Network

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Built in Algorithm Demo

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Image Classification
• ResNet implementation
with Apache MXNet.
• More networks to come.
• Transfer learning: begin
with a model already
trained on ImageNet!
0
0.5
1
1.5
2
2.5
3
3.5
0 1 2 3 4 5
Speedup
Number of Machines (P2)
Linear Speedup with Horizontal Scaling

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Customer Stories

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Amazon SageMaker Customers

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Amazon SageMaker to
automatically map buildings in
Vietnam

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End-to-End
Machine Learning
Platform
Zero setup Flexible Model
Training
Pay by the second
$
Amazon SageMaker
Build, train, and deploy machine learning models at scale

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AWS DeepLens
• Intel Atom Processor
• Gen9 graphics
• Ubuntu OS- 16.04 LTS
• 100 GFLOPS performance
• Dual band Wi-Fi
• 8 GB RAM
• 16 GB Storage (eMMC)
• 32 GB SD card
• 4 MP camera with MJPEG
• H.264 encoding at 1080p resolution
• 2 USB ports
• Micro HDMI
• Audio out
• AWS Greengrass preconfigured
• clDNN Optimized for MXNet

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Resources
https://aws.amazon.com/machine-learning
https://aws.amazon.com/blogs/ai
https://aws.amazon.com/sagemaker (free tier available)
https://github.com/awslabs/amazon-sagemaker-examples
An overview of Amazon SageMaker https://www.youtube.com/watch?v=ym7NEYEx9x4

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Thank You