The document provides an overview of Amazon SageMaker, a fully managed platform for building, training, and deploying machine learning models. It discusses how SageMaker allows users to load and prepare training data, choose from built-in algorithms or custom frameworks, train and tune models, and deploy models for production. The agenda then outlines labs that will cover loading data from S3, training and deploying with built-in algorithms like XGBoost, tuning hyperparameters, and making predictions from deployed models.

2. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Build, Train, and Deploy
Machine Learning for the Enterprise
with Amazon SageMaker
Julien Simon
Principal Technical Evangelist, AI & Machine Learning
Amazon Web Services
@julsimon
E N T 3 2 1

3. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Agenda
• Welcome & housekeeping
• Slides: quick overview of Amazon SageMaker
• Labs
• What we’ll cover today:
• Loading data from Amazon Simple Storage Service (Amazon S3)
• Training and deploying with built-in algorithms
• Finding optimal hyperparameters with Automatic Model Tuning
• Running HTTPS predictions and batch predictions
• Beyond built-in algorithms: a peek at Deep Learning

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Welcome !
• Marc Cabocel, Solutions Architect, France
• Phillipe Desmaison, Sr. Manager, Solutions Architecture, France
• Sergey Ermolin, Principal Solutions Architect, US
• Matthieu Fuzelier, IoT Architect, Professional Services, US
• Randy Hand, Sr. Manager, Solutions Architecture, US
• Chaitanya Hazarey, Partner Solutions Architect, US
• Adrian Hornsby, Technical Evangelist, Nordics
• Rob Navarro, Associate Solutions Architect, US
• Inigo Soto, Sr. Practice Manager, Professional Services, France & Iberia
• Maxime Thomas, DevOps Partner Solutions Architect, EMEA

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Housekeeping
• Please be a good neighbor 
• Turn off network backups and any network-hogging apps
• Switch your phones to silent mode
• Help the people around you if you can

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7. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Amazon SageMaker
Collect and prepare
training data
Choose and optimize
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
Notebook
instances
K-Means Clustering
Principal Component Analysis
Neural Topic Modelling
Factorization Machines
Linear Learner
XGBoost
Latent Dirichlet Allocation
Image Classification
Seq2Seq,
And more!
ALGORITHMS
Apache MXNet, Chainer
TensorFlow, PyTorch
Caffe2, CNTK,
Torch
FRAMEWORKS
Set up and manage
environments for
training
Train and tune
model (trial and
error)
Deploy model
in production
Scale and manage the
production environment
Built-in, high-
performance
algorithms
Build

9. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Amazon SageMaker
Notebook
instances
Built-in, high-
performance
algorithms
One-click
training
Automatic
Model Tuning
Build Train
Deploy model
in production
Scale and manage
the production
environment

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Amazon SageMaker
Fully managed
hosting with auto-
scaling
One-click
deployment
Notebook
instances
Built-in, high-
performance
algorithms
One-click
training
Automatic
Model Tuning
Build Train Deploy

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Model Training (on EC2)
Model Hosting (on EC2)
Trainingdata
Modelartifacts
Training code Helper code
Helper codeInference code
GroundTruth
Client application
Inference code
Training code
Inference requestInference response
Inference Endpoint

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Training Code
Factorization Machines
Linear Learner
Principal Component Analysis
K-Means
XGBoost
And more
Built-in Algorithms Bring Your Own ContainerBring Your Own Script
Model options

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Amazon SageMaker SDK
• Python SDK orchestrating all Amazon SageMaker activity
• Algorithm selection, training, deploying, hyperparameter optimization, and so on
• There’s also a Spark SDK (Python and Scala) which we won’t cover today
• High-level objects for:
• Some built-in algos: Kmeans, PCA, and the like
• Deep learning libraries: TensorFlow, MXNet, PyTorch, Chainer
• Sagemaker.estimator.estimator for everything else
https://github.com/aws/sagemaker-python-sdk
https://sagemaker.readthedocs.io/en/latest/

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Built-in algorithms
pink: supervised,blue:unsupervised
Linear learner: regression, classification Image classification: deep learning (ResNet)
Factorization machines: regression, classification,
recommendation
Object detection: deep learning
(VGG or ResNet)
K-Nearest neighbors: non-parametric regression and
classification
Neural topic model: topic modeling
XGBoost: regression, classification, ranking
https://github.com/dmlc/xgboost
Latent Dirichlet allocation: topic modeling (mostly)
K-Means: clustering Blazing text: GPU-based Word2Vec,
and text classification
Principal component analysis: dimensionality reduction Sequence to sequence: machine translation, speech-to-text
and more
Random cut forest: anomaly detection DeepAR: time-series forecasting (RNN)
Object2Vec general-purpose embeddings IP Insights: usage patterns for IP addresses

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XGBoost
• Open Source project
• Popular tree-based algorithm
for regression, classification
and ranking
• Handles missing values
and sparse data
• Supports distributed training
• Can work with data sets larger
than RAM
https://github.com/dmlc/xgboost
https://xgboost.readthedocs.io/en/latest/
https://arxiv.org/abs/1603.02754

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Loading training data from Amazon S3
• Two modes: File Mode and Pipe Mode
• input_mode parameter in sagemaker.estimator.estimator
• File Mode copies the data set to training instances
• You need to provision enough storage
• S3DataSource object
• S3DataDistributionType : FullyReplicated | ShardedByS3Key
• Different data formats are supported: CSV, protobuf, JSON, libsvm (check algo docs!)
• Pipe Mode streams the data set to training instances
• This allows you to process infinitely-large data sets
• Training starts faster
• This mode is supported by some built-in algos as well as TensorFlow
• Your data set must be in recordIO-encoded protobuf format.

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Labs
1. Training, deploying, and predicting with XGBoost
2. Finding optimal hyperparameters with Automatic Model Tuning
3. Running HTTPS predictions and batch predictions
4. Beyond built-in algorithms: a peek at TensorFlow

20. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.

21. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Resources
https://ml.aws
https://aws.amazon.com/sagemaker
https://github.com/awslabs/amazon-sagemaker-examples
https://github.com/aws/sagemaker-python-sdk
https://medium.com/@julsimon

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Related sessions
Friday, November 30
Deep Learning Applications Using TensorFlow – AIM401
10:45AM – 11:45AM | Venetian, Level 5, Palazzo O

23. Thank you!
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Julien Simon
Principal Technical Evangelist, AI & Machine Learning
Amazon Web Services
@julsimon

24. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.