Amazon SageMaker is a fully-managed service that enables data scientists and developers to quickly and easily build, train, and deploy machine learning models, at scale. This session will introduce you the features of Amazon SageMaker, including a one-click training environment, highly-optimized machine learning algorithms with built-in model tuning, and deployment without engineering effort. With zero-setup required, Amazon SageMaker significantly decreases your training time and overall cost of building production machine learning systems.

1. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Build, train and deploy ML models using Amazon SageMaker

2. The Amazon Machine Learning Stack
PLATFORM SERVICES
APPLICATION SERVICES
FRAMEWORKS & INTERFACES
Caffe2 CNTK
Apache
MXNet
PyTorch TensorFlow Chainer Keras Gluon
AWS Deep Learning AMIs
Amazon SageMaker
Rekognition Transcribe Translate Polly Comprehend Lex
AWS
DeepLens
EDUCATION
Amazon Mechanical Turk

3. What is Amazon SageMaker?

4. Amazon SageMaker
A fully-managed platform
that provides the quickest and easiest way for
data scientists and developers to get
ML models from idea to production.

5. Amazon SageMaker components
Amazon’s fast, scalable algorithms
Distributed TensorFlow, Apache MXNet, Chainer, PyTorch
Bring your own algorithm
Hyperparameter Tuning
Building HostingTraining

6. Amazon SageMaker components
Amazon’s fast, scalable algorithms
Distributed TensorFlow, Apache MXNet, Chainer, PyTorch
Bring your own algorithm
Hyperparameter Tuning
Building HostingTraining

7. Building
… or Apache Spark
through EMR and
the SageMaker
Spark SDK...
Use SageMaker‘s
hosted Notebook
Instances...
... or the Console
for a point and click
experience...
... or your own
device (EC2,
laptop, etc.)

8. Amazon SageMaker components
Amazon’s fast, scalable algorithms
Distributed TensorFlow, Apache MXNet, Chainer, PyTorch
Bring your own algorithm
Hyperparameter Tuning
Building HostingTraining

9. Training
Zero setup Streaming datasets
+ distributed
compute
Docker / ECS Deploy trained models
locally or to Amazon
SageMaker, AWS
Greengrass, AWS
DeepLens

10. Amazon SageMaker components
Amazon’s fast, scalable algorithms
Distributed TensorFlow, Apache MXNet, Chainer, PyTorch
Bring your own algorithm
Hyperparameter Tuning
Building HostingTraining

11. Hosting
One-click
deployment
Low latency, high
throughput, and
high reliability
A/B testing Bring your own
model

12. Amazon SageMaker components
Amazon’s fast, scalable algorithms
Distributed TensorFlow, Apache MXNet, Chainer, PyTorch
Bring your own algorithm
Hyperparameter Tuning
Building HostingTraining

13. Built-in algorithms
XGBoost, FM,
Linear, and
Forecasting
for supervised
learning
Kmeans, PCA,
and Word2Vec
for clustering
and pre-
processing
Image
classification
with
convolutional
neural networks
LDA and NTM
for topic
modeling,
seq2seq for
translation

14. Amazon SageMaker components
Amazon’s fast, scalable algorithms
Distributed TensorFlow, Apache MXNet, Chainer, PyTorch
Bring your own algorithm
Hyperparameter Tuning
Building HostingTraining

15. TensorFlow and Apache MXNet Docker Containers
… explore and
refine models in a
single Notebook
instance
… deploy to
production
Sample your
data… Use the same code
to train on the full
dataset in a cluster
of instances…

16. Amazon SageMaker components
Amazon’s fast, scalable algorithms
Distributed TensorFlow, Apache MXNet, Chainer, PyTorch
Bring your own algorithm
Hyperparameter Tuning
Building HostingTraining

17. Bring your own algorithm
... add algorithm
code to a Docker
container...
Pick your preferred
framework...
... publish to ECS
Amazon ECS

18. Amazon SageMaker components
Amazon’s fast, scalable algorithms
Distributed TensorFlow, Apache MXNet, Chainer, PyTorch
Bring your own algorithm
Hyperparameter Tuning
Building HostingTraining

19. Hyperparameter Tuning
(Automated Model Tuning)
Run a large set of training
jobs with varying
hyperparameters...
... and search the
hyperparameter space for
improved accuracy.

20. Zero setup for data exploration
Resizable as you
need
Common tools
pre-installed
Easy access to
your data sources
No servers to
manage

21. M o d u lar a r chi te ctu re
Past
Data
Training
algorithm
Model
artifacts
Inference
code
Client
application
Model
Data
Inference
Ground
truth
Amazon SageMaker

22. Pay as you go and inexpensive
ML compute by the
second starting
at $0.0464/hr
ML storage by the
second
at $0.14
per GB-month
Data processed in
notebooks and hosting
at $0.016 per GB
Free trial to get started
quickly

23. Getting Started
https://console.aws.amazon.com/sagemaker

24. Start with sample notebooks

25. Modify to access your data sources

26. Train your model

27. Deploy your model

28. Perform inferences

29. Demo:
Neural networks and embeddings
for recommendations

30. Demo:
Bringing your own algorithm

31. Get Started today with Amazon
SageMaker
https://console.aws.amazon.com/sagemaker

32. Questions?

33. Thanks!