The document discusses Amazon SageMaker, a fully managed service that enables developers and data scientists to build, train, and deploy machine learning models at scale. It provides an overview of how SageMaker simplifies and automates many complex ML workflow tasks like setting up environments, training models, and deploying models into production. Key features highlighted include built-in algorithms, frameworks and SDK support, hyperparameter tuning, and one-click deployment. Examples are given of using the SageMaker APIs from the command line and Python.

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Steve Shirkey
Solutions Architect, Amazon Web Services ASEAN
Predicting The Future
With Amazon SageMaker

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Long History Of Machine Learning (ML) At Amazon
Personalised
recommendations
Inventing
entirely new
customer
experiences
Fulfillment
automation and
inventory
management
Drones Voice-driven
interactions

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Customers Running Machine Learning On AWS Today

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Common Platform Use Cases For ML
• Retail: Supply chain and demand forecasting
• Financial services: Credit default prediction for customer behavior
• Manufacturing: Real-time predictions for industrial IoT
• Advertising: Predict click-through rate for targeted ads
• Media and branding: Prediction of language content quality
• Automotive innovation: Self-driving vehicles and simulation
• Health and wellness: Track disease progression

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Our mission:
Put machine learning in the hands of every
developer and data scientist
Machine Learning At AWS

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The Amazon Machine Learning Stack
FRAMEWORKS & INTERFACES
PLATFORM SERVICES
APPLICATION SERVICES

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Image Classification:
Differentiating Two Logos

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The Amazon Machine Learning Stack
PLATFORM SERVICES
APPLICATION SERVICES
FRAMEWORKS & INTERFACES
Caffe2 CNTK
Apache
MXNet
PyTorch
TensorFlo
w
Torch Keras Gluon
AWS Deep Learning AMIs
Amazon SageMaker AWS DeepLens
Rekognition Transcribe Translate Polly Comprehend Lex
FRAMEWORKS & INTERFACES
PLATFORM SERVICES

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The Amazon Machine Learning Stack
PLATFORM SERVICES
APPLICATION SERVICES
FRAMEWORKS & INTERFACES
Caffe2 CNTK
Apache
MXNet
PyTorch
TensorFlo
w
Torch Keras Gluon
AWS Deep Learning AMIs
Amazon SageMaker AWS DeepLens
Rekognition Transcribe Translate Polly Comprehend Lex
FRAMEWORKS & INTERFACES
PLATFORM SERVICES

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Amazon Rekognition

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Old Logo Via Amazon Rekognition

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Old Logo Via Amazon Rekognition

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New Logo Via Amazon Rekognition

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New Logo Via Amazon Rekognition

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Logo Detected: Helpful, but…
exactly which logo was detected?

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Let’s Go Deeper

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The Amazon Machine Learning Stack
PLATFORM SERVICES
APPLICATION SERVICES
FRAMEWORKS & INTERFACES
Caffe2 CNTK
Apache
MXNet
PyTorch
TensorFlo
w
Torch Keras Gluon
AWS Deep Learning AMIs
Amazon SageMaker AWS DeepLens
Rekognition Transcribe Translate Polly Comprehend Lex
FRAMEWORKS & INTERFACES

19. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
The Amazon Machine Learning Stack
PLATFORM SERVICES
APPLICATION SERVICES
FRAMEWORKS & INTERFACES
Caffe2 CNTK
Apache
MXNet
PyTorch
TensorFlo
w
Torch Keras Gluon
AWS Deep Learning AMIs
Amazon SageMaker AWS DeepLens
Rekognition Transcribe Translate Polly Comprehend Lex
FRAMEWORKS & INTERFACES

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Machine Learning Is
Still Too Complicated
For Everyday Developers

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Collect and
prepare training
data
Choose and
optimise your ML
algorithm
Set up and manage
environments for
training
Train and tune
model
(trial and error)
Deploy model
in production
Scale and manage
the production
environment
Typical Machine Learning Pipeline Activities

22. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Machine Learning Is
Still Too Complicated
For Everyday Developers

23. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Amazon SageMaker
Easily build, train, and deploy machine learning models
Collect and
prepare training
data
Choose and
optimise your
ML algorithm
Set up and
manage
environments for
training
Train and tune
model
(trial and error)
Deploy model
in production
Scale and
manage the
production
environment

24. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Amazon SageMaker
Collect and
prepare training
data
Choose and
optimise your
ML algorithm
Set up and
manage
environments for
training
Train and tune
model
(trial and error)
Deploy model
in production
Scale and
manage the
production
environment
Easily build, train, and deploy machine learning models

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Amazon SageMaker
Pre-built
notebooks
for common
problems
Set up and
manage
environments
for training
Train and
tune model
(trial and
error)
Built-in, high
performance
algorithms Deploy model
in production
Scale and
manage the
production
environment
BUILD
Easily build, train, and deploy machine learning models

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SageMaker Built-in
Algorithms
K-means Clustering
PCA
Neural Topic Modelling
Factorisation Machines
Linear Learner
XGBoost
Latent Dirichlet Allocation
Image Classification
Seq2Seq
DeepAR Forecasting
BlazingText (word2vec)
Training ML Models Using Amazon SageMaker

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Bring Your
Own
Algorithms
ML Algorithms
R
MXNet
TensorFlow
Caffe
PyTorch
Keras
CNTK
…
SageMaker Built-in
Algorithms
K-means Clustering
PCA
Neural Topic Modelling
Factorisation Machines
Linear Learner – Regression
XGBoost
Latent Dirichlet Allocation
Image Classification
Seq2Seq
Linear Learner –
Classification
DeepAR Forecasting
Training ML Models Using Amazon SageMaker

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MXNet & TensorFlow
SDK
TensorFlow SDK
MXNet (Gluon) SDK
SageMaker Built-in
Algorithms
K-means Clustering
PCA
Neural Topic Modelling
Factorisation Machines
Linear Learner – Regression
XGBoost
Latent Dirichlet Allocation
Image Classification
Seq2Seq
Linear Learner –
Classification
DeepAR Forecasting
Bring Your
Own
Algorithms
ML Algorithms
R
MXNet
TensorFlow
Caffe
PyTorch
Keras
CNTK
…
Training ML Models Using Amazon SageMaker

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MXNet & TensorFlow
SDK
TensorFlow SDK
MXNet (Gluon) SDK
SageMaker Built-in
Algorithms
K-means Clustering
PCA
Neural Topic Modelling
Factorisation Machines
Linear Learner – Regression
XGBoost
Latent Dirichlet Allocation
Image Classification
Seq2Seq
Linear Learner –
Classification
DeepAR Forecasting
Bring Your
Own
Algorithms
ML Algorithms
R
MXNet
TensorFlow
Caffe
PyTorch
Keras
CNTK
…
Apache Spark
Estimator
Apache Spark Python library
Apache Spark Scala library
Amazon
EMR
Training ML Models Using Amazon SageMaker

30. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Amazon SageMaker
Pre-built
notebooks
for common
problems
Built-in, high
performance
algorithms
One-click
training
Hyperparameter
optimisation
BUILD TRAIN
Deploy model
in production
Scale and
manage the
production
environment
Easily build, train, and deploy machine learning models

31. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Amazon SageMaker
Fully managed
hosting with
auto-scaling
One-click
deployment
Pre-built
notebooks
for common
problems
Built-in, high
performance
algorithms
One-click
training
BUILD TRAIN DEPLOY
Easily build, train, and deploy machine learning models
Hyperparameter
optimisation

32. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
From Command Line
training_image=382416733822.dkr.ecr.us-east-1.amazonaws.com/kmeans:1
aws sagemaker create-training-job
--training-job-name kmeans-cluster-20180410
--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

33. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
From Command Line
training_image=382416733822.dkr.ecr.us-east-1.amazonaws.com/kmeans:1
aws sagemaker create-training-job
--training-job-name kmeans-cluster-20180410
--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
Container

34. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
From Command Line
training_image=382416733822.dkr.ecr.us-east-1.amazonaws.com/kmeans:1
aws sagemaker create-training-job
--training-job-name kmeans-cluster-20180410
--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
Container
AWS CLI

35. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
From Command Line
training_image=382416733822.dkr.ecr.us-east-1.amazonaws.com/kmeans:1
aws sagemaker create-training-job
--training-job-name kmeans-cluster-20180410
--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
Algorithm
Container
AWS CLI

36. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Input Data
From Command Line
training_image=382416733822.dkr.ecr.us-east-1.amazonaws.com/kmeans:1
aws sagemaker create-training-job
--training-job-name kmeans-cluster-20180410
--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
Algorithm
Container
AWS CLI

37. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Input Data
From Command Line
training_image=382416733822.dkr.ecr.us-east-1.amazonaws.com/kmeans:1
aws sagemaker create-training-job
--training-job-name kmeans-cluster-20180410
--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
Hardware
Algorithm
Container
AWS CLI

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From Python
Import

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From Python
Hardware
Import

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From Python
Parameters
Hardware
Import

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From Python
Parameters
Hardware
Start Training
Import

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From Python
Parameters
Hardware
Start Training
Deploy model
Import

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DEMO:
Scripting Amazon SageMaker

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Amazon SageMaker:
Puts advanced machine learning in the
hands of every developer and data scientist
Machine Learning At AWS

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• Getting Started with Amazon SageMaker: https://aws.amazon.com/sagemaker/
• Amazon SageMaker SDK:
• For Python: https://github.com/aws/sagemaker-python-sdk
• For Spark: https://github.com/aws/sagemaker-spark
• Amazon SageMaker Examples/Workshops:
https://github.com/awslabs/amazon-sagemaker-examples
https://github.com/awslabs/amazon-sagemaker-workshop
If you would like to know more, swipe your badge on your way out!
Go Forth And Build!

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Thank You