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© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
AWS re:INVENT
Amazon SageMaker Algorithms:
Infinitely Scalable Machine Learning
E d o L i b e r t y – S e n i o r M a n a g e r o f R e s e a r c h , A m a z o n S a g e M a k e r
N o v e m b e r 3 0 , 2 0 1 7
M C L 3 4 1
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Speaker
Dr. Edo Liberty is a Principal Scientist at AWS and the manager of the Algorithms Group at
Amazon AI. His group includes highly technical scientists and math loving engineers; it focuses
on building machine learning and data mining algorithms for massive datasets.
His personal research focuses on online algorithms, streaming, sketching, numerical linear
algebra, fast dimensionality reduction, and clustering. He is the author of more than thirty
academic papers on these topics and received several awards for his breakthrough research.
He is a frequent invited speaker, tutorial presenter, and committee member for leading
international conferences and journals.
Agenda
• What is Amazon SageMaker?
• Challenges in Machine Learning
• Architecture and Design Choices
• Infinitely Scalable ML Algorithms
• More Great Algorithms
• Using Amazon SageMaker Algorithms
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
What is Amazon SageMaker?
Exploration Training
Hosting
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Challenges in Machine Learning
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Machine Learning
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Large Scale Machine Learning
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Large Scale Machine Learning
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Our Customers use ML at a massive scale!
“We collect 160M events
daily in the ML pipeline and
run training over the last
15 days and need it to
complete in one hour.
Effectively there's 100M
features in the model”
Valentino Volonghi, CTO
“We process 3 million ad
requests a second, 100,000
features per request. That’s
250 trillion per day. Not
your run of the mill Data
science problem!”
Bill Simmons, CTO
“Our data warehouse is
100TB and we are
processing 2TB daily. We're
running mostly gradient
boosting (trees), LDA and
K-Means clustering and
collaborative filtering.“
Shahar Cizer Kobrinsky, VP
Architecture
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Cost vs. Time
$$$$
$$$
$$
$
Minutes Hours Days Weeks Months
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Cost vs. Time
$$$$
$$$
$$
$
Minutes Hours Days Weeks Months
Single
Machine
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Cost vs. Time
$$$$
$$$
$$
$
Minutes Hours Days Weeks Months
Single
Machine
Distributed, with
Strong Machines
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Cost vs. Time
$$$$
$$$
$$
$
Minutes Hours Days Weeks Months
Single
Machine
Distributed, with
Strong Machines
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Model Selection
1
1
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Incremental Training
2
3
1
2
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Production Readiness
Data/Model Size
Investment
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Production Readiness
Data/Model Size
Investment Reasonable
Investment Level
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Production Readiness
Data/Model Size
Investment Reasonable
Investment Level
Unusable Data /
Wasted opportunity
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Architecture and Design Choices
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Streaming
State
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Streaming
Data Size
Memory
Data Size
Time/Cost
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Incremental Training
2
3
1
2
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Incremental Training
3
1
2
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
GPU/CPU
GPU State
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Distributed
GPU State
GPU State
GPU State
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Shared State
GPU
GPU
GPU Local
State
Shared
State
Local
State
Local
State
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Cost vs. Time
$$$$
$$$
$$
$
Minutes Hours Days Weeks Months
Best Alternative
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Cost vs. Time
$$$$
$$$
$$
$
Minutes Hours Days Weeks Months
Best Alternative
Amazon SageMaker
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
State Model
GPU State
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Model Selection
1
1
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Model Selection
1
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Abstraction and Containerization
def initialize(...)
def update(...)
def finalize(...)
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Production Readiness
Data/Model Size
Investment Reasonable
Investment Level
Unusable Data
Wasted opportunity
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Production Readiness
Data/Model Size
Investment Reasonable
Investment Level
No unusable Data
No wasted opportunity
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Infinitely Scalable ML Algorithms
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Linear Learner
Regression:
Estimate a real valued function
Binary Classification:
Predict a 0/1 class
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Linear Learner
Train
Fit thresholds
and select
Select model with best validation performance
>8x speedup over naïve parallel training!
© 2017, 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
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Factorization Machines
Log_loss F1 Score Seconds
SageMaker 0.494 0.277 820
Other (10 Iter) 0.516 0.190 650
Other (20 Iter) 0.507 0.254 1300
Other (50 Iter) 0.481 0.313 3250
Click Prediction 1 TB advertising dataset,
m4.4xlarge machines, perfect scaling.
$-
$20.00
$40.00
$60.00
$80.00
$100.00
$120.00
$140.00
$160.00
$180.00
$200.00
1 2 3 4 5 6 7 8
CostinDollars Billable Time in Hours
10
machines
20
machines
30
machines
4050
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
K-Means Clustering
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
K-Means Clustering
Method Accurate? Passes Efficient
Tuning
Comments
Lloyds [1] Yes* 5-10 No
K-Means ++ [2] Yes k+5 to k+10 No scikit-learn
K-Means|| [3] Yes 7-12 No spark.ml
Online [4] No 1 No
Streaming [5,6] No 1 No Impractical
Webscale [7] No 1 No spark streaming
Coresets [8] No 1 Yes Impractical
SageMaker Yes 1 Yes
[1] Lloyd, IEEE TIT, 1982
[2] Arthur et. al. ACM-SIAM, 2007
[3] Bahmani et. al., VLDB, 2012
[4] Liberty et. al., 2015
[5] Shindler et. al, NIPS, 2011
[6] Guha et. al, IEEE Trans. Knowl. Data Eng. 2003
[7] Sculley, WWW, 2010
[8] Feldman et. al.
© 2017, 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!
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Principal Component Analysis (PCA)
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Principal Component Analysis (PCA)
More than 10x faster
at a fraction the cost!
0.00
20.00
40.00
60.00
80.00
100.00
120.00
8 10 20
Mb/Sec/Machine
Number of Machines
other sagemaker-deterministic sagemaker-randomized
Cost vs. Time Throughput and Scalability
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
0 10 20 30 40 50
CostinDollars
Billable time in Minutes
other sagemaker-deterministic sagemaker-randomized
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Neural Topic Modeling
Perplexity vs. Number of Topic
Encoder: feedforward net
Input term counts vector
Document
Posterior
Sampled Document
Representation
Decoder:
Softmax
Output term counts vector
0
2000
4000
6000
8000
10000
12000
0 50 100 150 200
Perplexity
Number of Topics
NTM Other
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Time Series Forecasting (coming soon...)
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
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
More Great Algorithms
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Spectral LDA
Training Time vs. Number of Topics
0
50
100
150
200
250
0 20 40 60 80 100TrainingTimeinMinutes
Number of Topics
lda-data-a lda-data-b other-data-a other-data-b
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Boosted Decision Trees
Throughput vs. Number of MachinesXGBoost is one of the most
commonly used
implementations of boosted
decision trees in the world.
It is now available in Amazon
SageMaker!
0
200
400
600
800
1000
1200
1400
0 10 20 30 40 50 60 70
ThroughputinMB/Sec
Number of Machines (C4.8xLarge)
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Sequence to Sequence
English-German Translation
0
5
10
15
20
25
0 5 10 15 20 25
BLEUScore
Billable Time in Hours
P2.16x P2.8x P2.x
Best known result!
Based on Sockeye
and Apache incubated
MxNet, Multi-GPU, and can
be used for Neural Machine
Translation.
Supports both RNN/CNN
as encoder/decoder
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Image Classification
Implementation in MxNet
of ResNet.
Other networks such as
DenseNet and Inception will
be added in the future.
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 Machine (P2)
Speedup with Horizontal Scaling
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Amazon SageMaker – More Algorithms
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Using Amazon SageMaker Algorithms
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Input Data
profile=<your_profile>
arn_role=<your_arn_role>
training_image=382416733822.dkr.ecr.us-east-1.amazonaws.com/kmeans:1
training_job_name=clutering_text_documents_`date '+%Y_%m_%d_%H_%M_%S'`
aws --profile $profile 
--region us-east-1 
sagemaker create-training-job 
--training-job-name $training_job_name 
--algorithm-specification TrainingImage=$training_image,TrainingInputMode=File 
--hyper-parameters k=10,feature_dim=1024,mini_batch_size=1000 
--role-arn $arn_role 
--input-data-config '{"ChannelName": "train", "DataSource": {"S3DataSource":{"S3DataType":
"S3Prefix", "S3Uri": "s3://kmeans_demo/train", "S3DataDistributionType": "ShardedByS3Key"}},
"CompressionType": "None", "RecordWrapperType": "None"}' 
--output-data-config S3OutputPath=s3://training_output/$training_job_name
--resource-config InstanceCount=2,InstanceType=ml.c4.8xlarge,VolumeSizeInGB=50 
--stopping-condition MaxRuntimeInSeconds=3600
From Command Line
Hardware
Algorithm
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
From Amazon EMR
Start Training
Parameters
Hardware
Apply Model
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
From Amazon SageMaker Notebooks
Parameters
Hardware
Start Training
Host model
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Amazon SageMaker - Go try it out
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
THANK YOU!

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NEW LAUNCH! Infinitely Scalable Machine Learning Algorithms with Amazon AI - MCL341 - re:Invent 2017

  • 1. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. AWS re:INVENT Amazon SageMaker Algorithms: Infinitely Scalable Machine Learning E d o L i b e r t y – S e n i o r M a n a g e r o f R e s e a r c h , A m a z o n S a g e M a k e r N o v e m b e r 3 0 , 2 0 1 7 M C L 3 4 1
  • 2. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Speaker Dr. Edo Liberty is a Principal Scientist at AWS and the manager of the Algorithms Group at Amazon AI. His group includes highly technical scientists and math loving engineers; it focuses on building machine learning and data mining algorithms for massive datasets. His personal research focuses on online algorithms, streaming, sketching, numerical linear algebra, fast dimensionality reduction, and clustering. He is the author of more than thirty academic papers on these topics and received several awards for his breakthrough research. He is a frequent invited speaker, tutorial presenter, and committee member for leading international conferences and journals. Agenda • What is Amazon SageMaker? • Challenges in Machine Learning • Architecture and Design Choices • Infinitely Scalable ML Algorithms • More Great Algorithms • Using Amazon SageMaker Algorithms
  • 3. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. What is Amazon SageMaker? Exploration Training Hosting
  • 4. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Challenges in Machine Learning
  • 5. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Machine Learning
  • 6. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Large Scale Machine Learning
  • 7. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Large Scale Machine Learning
  • 8. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Our Customers use ML at a massive scale! “We collect 160M events daily in the ML pipeline and run training over the last 15 days and need it to complete in one hour. Effectively there's 100M features in the model” Valentino Volonghi, CTO “We process 3 million ad requests a second, 100,000 features per request. That’s 250 trillion per day. Not your run of the mill Data science problem!” Bill Simmons, CTO “Our data warehouse is 100TB and we are processing 2TB daily. We're running mostly gradient boosting (trees), LDA and K-Means clustering and collaborative filtering.“ Shahar Cizer Kobrinsky, VP Architecture
  • 9. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Cost vs. Time $$$$ $$$ $$ $ Minutes Hours Days Weeks Months
  • 10. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Cost vs. Time $$$$ $$$ $$ $ Minutes Hours Days Weeks Months Single Machine
  • 11. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Cost vs. Time $$$$ $$$ $$ $ Minutes Hours Days Weeks Months Single Machine Distributed, with Strong Machines
  • 12. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Cost vs. Time $$$$ $$$ $$ $ Minutes Hours Days Weeks Months Single Machine Distributed, with Strong Machines
  • 13. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Model Selection 1 1
  • 14. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Incremental Training 2 3 1 2
  • 15. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Production Readiness Data/Model Size Investment
  • 16. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Production Readiness Data/Model Size Investment Reasonable Investment Level
  • 17. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Production Readiness Data/Model Size Investment Reasonable Investment Level Unusable Data / Wasted opportunity
  • 18. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Architecture and Design Choices
  • 19. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Streaming State
  • 20. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Streaming Data Size Memory Data Size Time/Cost
  • 21. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Incremental Training 2 3 1 2
  • 22. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Incremental Training 3 1 2
  • 23. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. GPU/CPU GPU State
  • 24. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Distributed GPU State GPU State GPU State
  • 25. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Shared State GPU GPU GPU Local State Shared State Local State Local State
  • 26. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Cost vs. Time $$$$ $$$ $$ $ Minutes Hours Days Weeks Months Best Alternative
  • 27. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Cost vs. Time $$$$ $$$ $$ $ Minutes Hours Days Weeks Months Best Alternative Amazon SageMaker
  • 28. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. State Model GPU State
  • 29. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Model Selection 1 1
  • 30. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Model Selection 1
  • 31. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Abstraction and Containerization def initialize(...) def update(...) def finalize(...)
  • 32. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Production Readiness Data/Model Size Investment Reasonable Investment Level Unusable Data Wasted opportunity
  • 33. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Production Readiness Data/Model Size Investment Reasonable Investment Level No unusable Data No wasted opportunity
  • 34. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Infinitely Scalable ML Algorithms
  • 35. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Linear Learner Regression: Estimate a real valued function Binary Classification: Predict a 0/1 class
  • 36. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Linear Learner Train Fit thresholds and select Select model with best validation performance >8x speedup over naïve parallel training!
  • 37. © 2017, 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
  • 38. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Factorization Machines Log_loss F1 Score Seconds SageMaker 0.494 0.277 820 Other (10 Iter) 0.516 0.190 650 Other (20 Iter) 0.507 0.254 1300 Other (50 Iter) 0.481 0.313 3250 Click Prediction 1 TB advertising dataset, m4.4xlarge machines, perfect scaling. $- $20.00 $40.00 $60.00 $80.00 $100.00 $120.00 $140.00 $160.00 $180.00 $200.00 1 2 3 4 5 6 7 8 CostinDollars Billable Time in Hours 10 machines 20 machines 30 machines 4050
  • 39. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. K-Means Clustering
  • 40. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. K-Means Clustering Method Accurate? Passes Efficient Tuning Comments Lloyds [1] Yes* 5-10 No K-Means ++ [2] Yes k+5 to k+10 No scikit-learn K-Means|| [3] Yes 7-12 No spark.ml Online [4] No 1 No Streaming [5,6] No 1 No Impractical Webscale [7] No 1 No spark streaming Coresets [8] No 1 Yes Impractical SageMaker Yes 1 Yes [1] Lloyd, IEEE TIT, 1982 [2] Arthur et. al. ACM-SIAM, 2007 [3] Bahmani et. al., VLDB, 2012 [4] Liberty et. al., 2015 [5] Shindler et. al, NIPS, 2011 [6] Guha et. al, IEEE Trans. Knowl. Data Eng. 2003 [7] Sculley, WWW, 2010 [8] Feldman et. al.
  • 41. © 2017, 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!
  • 42. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Principal Component Analysis (PCA)
  • 43. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Principal Component Analysis (PCA) More than 10x faster at a fraction the cost! 0.00 20.00 40.00 60.00 80.00 100.00 120.00 8 10 20 Mb/Sec/Machine Number of Machines other sagemaker-deterministic sagemaker-randomized Cost vs. Time Throughput and Scalability 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 0 10 20 30 40 50 CostinDollars Billable time in Minutes other sagemaker-deterministic sagemaker-randomized
  • 44. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Neural Topic Modeling Perplexity vs. Number of Topic Encoder: feedforward net Input term counts vector Document Posterior Sampled Document Representation Decoder: Softmax Output term counts vector 0 2000 4000 6000 8000 10000 12000 0 50 100 150 200 Perplexity Number of Topics NTM Other
  • 45. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Time Series Forecasting (coming soon...) 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
  • 46. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. More Great Algorithms
  • 47. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Spectral LDA Training Time vs. Number of Topics 0 50 100 150 200 250 0 20 40 60 80 100TrainingTimeinMinutes Number of Topics lda-data-a lda-data-b other-data-a other-data-b
  • 48. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Boosted Decision Trees Throughput vs. Number of MachinesXGBoost is one of the most commonly used implementations of boosted decision trees in the world. It is now available in Amazon SageMaker! 0 200 400 600 800 1000 1200 1400 0 10 20 30 40 50 60 70 ThroughputinMB/Sec Number of Machines (C4.8xLarge)
  • 49. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Sequence to Sequence English-German Translation 0 5 10 15 20 25 0 5 10 15 20 25 BLEUScore Billable Time in Hours P2.16x P2.8x P2.x Best known result! Based on Sockeye and Apache incubated MxNet, Multi-GPU, and can be used for Neural Machine Translation. Supports both RNN/CNN as encoder/decoder
  • 50. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Image Classification Implementation in MxNet of ResNet. Other networks such as DenseNet and Inception will be added in the future. 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 Machine (P2) Speedup with Horizontal Scaling
  • 51. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon SageMaker – More Algorithms
  • 52. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Using Amazon SageMaker Algorithms
  • 53. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Input Data profile=<your_profile> arn_role=<your_arn_role> training_image=382416733822.dkr.ecr.us-east-1.amazonaws.com/kmeans:1 training_job_name=clutering_text_documents_`date '+%Y_%m_%d_%H_%M_%S'` aws --profile $profile --region us-east-1 sagemaker create-training-job --training-job-name $training_job_name --algorithm-specification TrainingImage=$training_image,TrainingInputMode=File --hyper-parameters k=10,feature_dim=1024,mini_batch_size=1000 --role-arn $arn_role --input-data-config '{"ChannelName": "train", "DataSource": {"S3DataSource":{"S3DataType": "S3Prefix", "S3Uri": "s3://kmeans_demo/train", "S3DataDistributionType": "ShardedByS3Key"}}, "CompressionType": "None", "RecordWrapperType": "None"}' --output-data-config S3OutputPath=s3://training_output/$training_job_name --resource-config InstanceCount=2,InstanceType=ml.c4.8xlarge,VolumeSizeInGB=50 --stopping-condition MaxRuntimeInSeconds=3600 From Command Line Hardware Algorithm
  • 54. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. From Amazon EMR Start Training Parameters Hardware Apply Model
  • 55. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. From Amazon SageMaker Notebooks Parameters Hardware Start Training Host model
  • 56. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon SageMaker - Go try it out
  • 57. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. THANK YOU!