1. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved.
Sunil Mallya (수닐 말야)
Sr. AI Solutions Architect/ ML Solutions Lab / Amazon Web Services
Amazon SageMaker

2. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved.
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

3. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved.
Training
code
• Matrix Factorization
• Regression
• Principal Component Analysis
• K-Means Clustering
• Gradient Boosted Trees
• And More!
Amazon provided Algorithms
Bring Your Own Container
Amazon SageMaker: model options
Bring Your Own Script
IM Estimators in
Apache Spark

4. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved.
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

5. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved.
Infinitely scalable algorithms

6. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved.
Streaming
GPU State

7. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved.
Streaming
Data Size
Memory
Data Size
Time/Cost

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Distributed
GPU State
GPU State
GPU State

9. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved.
Shared State
GPU
GPU
GPU
Local
State
Shared
State
Local
State
Local
State

10. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved.
Cost vs. Time
$$$$
$$$
$$
$
Minutes Hours Days Weeks Months
Best Alternative
Amazon SageMaker

11. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved.
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
Spam or not?

12. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved.
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!

13. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved.
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
zi,t 2, xi,t 1 zi,t 1, xi,t zi,t, xi,t+1
hi,t 1 hi,t hi,t+1
`(zi,t 1|✓i,t 1) `(zi,t|✓i,t) `(zi,t+1|✓i,t+1)
zi,t 1 zi,t zi,t+1
Input
Network

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Built in Algorithm Demo

15. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved.
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

16. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved.
Tensorflow on SageMaker Demo

17. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved.
Amazon SageMaker Customers (recent additions)

18. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
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

19. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved.
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

20. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved.
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
https://medium.com/@julsimon

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