Machine Learning Workshops at the San Francisco Loft Build, Train, and Deploy ML Models Using SageMaker 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. Level: 200-300 Speaker: Martin Schade - R&D Engineer, AWS Solutions Architecture

1. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Pop-up Loft
Build, train and deploy ML models using Amazon SageMaker
Martin Schade
R&D Engineer

2. The AWS Machine Learning Stack
AI SERVICES
ML PLATFORMS
ML FRAMEWORKS
VISION
Rekognition
Video
Rekognition
SPEECH
TranscribePolly
LANGUAGE
ComprehendTranslate
CHATBOTS
Lex
AWS DeepLensAmazon SageMaker
TensorFlow MXNet PyTorch Caffe2 Chainer Horvod Gluon Keras
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,
object
detection 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 l a r a r c h i t e c t u r e
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:
Sagemaker build – train - host

30. Demo:
Bringing your own algorithm

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

32. Questions?

33. Thanks!