Build, train, and deploy machine learning models at scale Machine learning often feels a lot harder than it should be to most developers because the process to build and train models, and then deploy them into production is too complicated and too slow. Amazon SageMaker includes modules that can be used together or independently to build, train, and deploy your machine learning models. Olivier Bergeret - AWS https://dataxday.fr/ Video available: https://www.youtube.com/watch?v=3eV4x_GR_f8

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Machine learning
models at scale with
Amazon SageMaker
Olivier Bergeret, Solutions Architect mgr, AWS

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Fulfilment
& Logistics
At Amazon, we’ve been making investments
in ML for the last 20 years…

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At Amazon, we’ve been making investments
in ML for the last 20 years…
Fulfilment
& Logistics
Search &
Discovery

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At Amazon, we’ve been making investments
in ML for the last 20 years…
Fulfilment
& Logistics
Existing
Products
Search &
Discovery

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Fulfilment
& Logistics
Existing
Products
New
Products
Search &
Discovery
At Amazon, we’ve been making investments
in ML for the last 20 years…

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Put machine learning in the hands of every developer
and data scientist
ML @ AWS: Our mission

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The AWS Machine Learning Stack
Application
Services
Platform
Services
Frameworks
Infrastructure
Vision Rekognition Image
Rekognition Video
Language Lex
Translate
Comprehend
Speech Polly
Transcribe
Amazon
SageMaker
AWS DeepLens
Amazon Machine
Learning
Amazon EMR
Spark
Amazon
Mechanical Turk
AWS Deep Learning AMI
TensorFlow
Apache
MXNet
Gluon
Cognitive
Toolkit
Caffe Keras PyTorch Chainer
Compute GPU - P3
IoT - Greengrass
Inference
Mobile

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Amazon Rekognition
Object, Scene &
Activity Recognition
Facial
Recognition
Facial Analysis Person Tracking
Unsafe Content
Detection
Celebrity
Recognition
Text in Images
D e e p l e a r n i n g - b a s e d i m a g e a n d v i d e o a n a l y s i s

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SPEECH & LANGUAGE capabilities
Amazon Transcribe
Automatic conversion of speech into accurate,
grammatically correct text
Amazon Translate Natural and fluent language translation
Amazon Polly Turn text into lifelike speech using deep learning
Amazon
Comprehend
Discover insights and relationships in text
Amazon Lex
Conversational interfaces for text-based and voice-
based applications

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Amazon EC2 P3 Instances
• Up to eight NVIDIA Tesla V100 GPUs
• 1 PetaFLOP of computational performance
– 14x better than P2
• 300 GB/s GPU-to-GPU communication
(NVLink) – 9X better than P2
• 16 GB GPU memory with 900 GB/sec peak
GPU memory bandwidth
T h e f a s t e s t , m o s t p o w e r f u l G P U i n s t a n c e s i n t h e c l o u d

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• Get started quickly with easy-to-launch tutorials
• Hassle-free setup and configuration
• Pay only for what you use – no additional charge for
the AMI
• Accelerate your model training and deployment
• Support for popular deep learning frameworks
AWS Deep Learning AMI

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CUSTOMERS RUNNING MACHINE
LEARNING ON AWS TODAY

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

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Amazon’s fast, scalable algorithms
Idiomatic, distributed TensorFlow & MXNet
Bring your own algorithm
Hyperparameter optimization
UX HostingTraining
Amazon SageMaker Components

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Amazon’s fast, scalable algorithms
Idiomatic, distributed TensorFlow & MXNet
Bring your own algorithm
Hyperparameter optimization
UX HostingTraining
Amazon SageMaker Components

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Or Apache Spark
through Amazon EMR
and the SageMaker
Spark SDK
UX
Use the SageMaker
hosted notebook
instances
Or the SageMaker
console for a
point-and-click
experience
Or your own
device (EC2,
laptop, etc.)

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Amazon’s fast, scalable algorithms
Idiomatic, distributed TensorFlow & MXNet
Bring your own algorithm
Hyperparameter optimization
UX HostingTraining
Amazon SageMaker Components

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No setup
Training
Streaming datasets
and distributed
compute
Docker /
Amazon ECS
Deploy trained models
locally or to SageMaker,
AWS Greengrass, or
DeepLens

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Amazon’s fast, scalable algorithms
Idiomatic, distributed TensorFlow & MXNet
Bring your own algorithm
Hyperparameter optimization
UX HostingTraining
Amazon SageMaker Components

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One-step
deployment
Low latency, high
throughput, and
high reliability
A/B testing Use your own model
Hosting

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Amazon’s fast, scalable algorithms
Idiomatic, distributed TensorFlow & MXNet
Bring your own algorithm
Hyperparameter optimization
UX HostingTraining
Amazon SageMaker Components

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XGBoost, FM,
linear, and
forecasting for
supervised
learning
Kmeans, PCA, and
Word2Vec for
clustering and
preprocessing
Image
classification with
convolutional
neural networks
LDA and NTM for
topic modeling;
seq2seq for
translation
Built-in Algorithms

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Amazon’s fast, scalable algorithms
Idiomatic, distributed TensorFlow & MXNet
Bring your own algorithm
Hyperparameter optimization
UX HostingTraining
Amazon SageMaker Components

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Explore and refine
models in a single
notebook instance
TensorFlow & MXNet Containers
Deploy to
production
Sample your data Use the same code
to train on the full
dataset in a cluster
of GPU instances

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Amazon’s fast, scalable algorithms
Idiomatic, distributed TensorFlow & MXNet
Bring your own algorithm
Hyperparameter optimization
UX HostingTraining
Amazon SageMaker Components

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Bring Your Own Algorithm
Add algorithm code
to a Docker container
Choose your preferred framework Publish to Amazon ECS
Amazon ECS

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Amazon’s fast, scalable algorithms
Idiomatic, distributed TensorFlow & MXNet
Bring your own algorithm
Hyperparameter optimization
UX HostingTraining
Amazon SageMaker Components

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Hyperparameter Optimization
Run a large set of training
jobs with varying
hyperparameters
Then search the
hyperparameter space for
improved accuracy

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Resize as you
need
Common tools
pre-installed
Easy access to
your data sources
No servers to
manage
No Setup for Data Exploration

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M o d u l a r A r c h i t e c t u r e – U s e W h a t Y o u N e e d
Past
data
Training
algorithm
Model
artifacts
Inference
code
Client
application
Model
Data
Inference
Ground
truth
Amazon SageMaker

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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 quickly get
started
Pay as You Go – Inexpensive

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How do I get started using
Amazon SageMaker?

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Start with Notebook Samples

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Modify to Access Your Data Sources

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Train Your Model

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Deploy Your Model

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Perform Inferences

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Demo Part 1: Start Training and
Deployment

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Demo Part 2: Evaluate deployed
model

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THANK YOU!

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