Build Machine Models with Amazon SageMaker...ML FRAMEWORKS & INFRASTRUCTURE. The Amazon ML Stack:...

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© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. SUMMIT Build Machine Models with Amazon SageMaker Julien Simon Global Evangelist, AI & Machine Learning @julsimon

Transcript of Build Machine Models with Amazon SageMaker...ML FRAMEWORKS & INFRASTRUCTURE. The Amazon ML Stack:...

Page 1: Build Machine Models with Amazon SageMaker...ML FRAMEWORKS & INFRASTRUCTURE. The Amazon ML Stack: Broadest & Deepest Set of Capabilities. AI SERVICES. REKOGNITION. IMAGE. POLLY. TRANSCRIBE.

© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T

Build Machine Models with Amazon SageMaker

Julien SimonGlobal Evangelist, AI & Machine Learning@julsimon

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M L F R A M E W O R K S &

I N F R A S T R U C T U R E

The Amazon ML Stack: Broadest & Deepest Set of Capabilities

A I S E R V I C E SR E K O G N I T I O N

I M A G EP O L L Y T R A N S C R I B E T R A N S L A T E C O M P R E H E N D

C O M P R E H E N DM E D I C A L

L E XR E K O G N I T I O NV I D E O

Vis ion Speech Chatbots

A M A Z O N S A G E M A K E R

B U I L D T R A I N

F O R E C A S TT E X T R A C T P E R S O N A L I Z E

D E P L O YPre-bui l t algorithms & notebooks

Data label ing (G R O U N D T R U T H )

One-cl ick model training & tuning

Optimization ( N E O )

One-cl ick deployment & host ingM L S E R V I C E S

F r a m e w o r k s I n t e r f a c e s I n f r a s t r u c t u r e

E C 2 P 3 & P 3 d n

E C 2 C 5 F P G A s G R E E N G R A S S E L A S T I CI N F E R E N C E

Models without training data (REINFORCEMENT LEARNING)Algorithms & models ( A W S M A R K E T P L A C E )

Language Forecast ing Recommendat ions

NEW NEWNEW

NEW

NEWNEWNEW

NEW

NEW

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Amazon SageMaker: Build, Train, and Deploy ML Models at Scale

Collect and prepare training data

Choose and optimize yourML algorithm

Train andTune ML Models

Set up andmanage

environmentsfor training

Deploy modelsin production

Scale and managethe productionenvironment

123

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Machine learning cycle

Business Problem

ML problem framing Data collection

Data integration

Data preparation and cleaning

Data visualization and analysis

Feature engineering

Model training and parameter tuning

Model evaluation

Monitoring and debugging

Model deployment

Predictions

Are business

goals met?

YESNO

Dat

a au

gmen

tatio

n

Feat

ure

augm

enta

tion

Re-training

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Successful models require high-quality data

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Successful models require high-quality data

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Amazon SageMaker Ground Truthhttps://aws.amazon.com/blogs/aws/amazon-sagemaker-ground-truth-build-highly-accurate-datasets-and-reduce-labeling-costs-by-up-to-70

Easily integrate human labelers

Get accurateresults

K E Y F E A T U R E S

Automatic labeling via machine learning

Ready-made and custom workflows for image bounding box,

segmentation, and text

Labelmanagement

Quickly label training data

Private and public human workforce

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Manage data on AWS

Business Problem

ML problem framing Data collection

Data integration

Data preparation and cleaning

Data visualization and analysis

Feature engineering

Model training and parameter tuning

Model evaluation

Monitoring and debugging

Model deployment

Predictions

Are business

goals met?

YESNO

Dat

a au

gmen

tatio

n

Feat

ure

augm

enta

tion

Re-training

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© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T

Build and train models using SageMaker

Business Problem

ML problem framing Data collection

Data integration

Data preparation and cleaning

Data visualization and analysis

Feature engineering

Model training and parameter tuning

Model evaluation

Monitoring and debugging

Model deployment

Predictions

Are business

goals met?

YESNO

Dat

a au

gmen

tatio

n

Feat

ure

augm

enta

tion

Re-training

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Deploy models using SageMaker

Business Problem

ML problem framing Data collection

Data integration

Data preparation and cleaning

Data visualization and analysis

Feature engineering

Model training and parameter tuning

Model evaluation

Monitoring and debugging

Model deployment

Predictions

Are business

goals met?

YESNO

Dat

a au

gmen

tatio

n

Feat

ure

augm

enta

tion

Re-training

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Amazon SageMaker

Collect and prepare training data

Choose and optimize yourML algorithm

Train andTune ML Models

Set up andmanage

environmentsfor training

Deploy modelsin production

Scale and managethe productionenvironment

123

Model compilationElastic inferenceInference pipelines

P3DN, C5NTensorFlow on 256 GPUsResume HPO tuning job

New built-in algorithmsscikit-learn environment

Model marketplaceSearch

Git integrationElastic inference

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AWS Machine Learning Marketplace

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S U M M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.

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The Amazon SageMaker API• Python SDK orchestrating all Amazon SageMaker activity

• High-level objects for algorithm selection, training, deploying, automatic model tuning, etc.

• Spark SDK (Python & Scala)

• AWS CLI: ‘aws sagemaker’

• AWS SDK: boto3, etc.

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Training code

Factorization MachinesLinear LearnerPrincipal Component AnalysisK-Means ClusteringXGBoostAnd more

Built-in Algorithms Bring Your Own ContainerBring Your Own Script

Model options

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IMAGE RECOGNITION | for the good of | PRODUCT SEARCH

Sébastien [email protected] IT Projects & CCOE ManagerTarkett

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→€2.8B Net sales(2018 figures)

→13,000employees

→Present in more than 100countries

→1.3M square meters of flooring sold each day

A worldwide leader in flooring & sports surfaces

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Could we recommend products to each user?

VINYL & LINOLEUM CARPET WOOD & LAMINATE ACCESSORIES & RUBBER

SPORTS SURFACES

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«Building a bot»

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Why image search?Why does it make sense in the context of flooring?

Field research showed that architects and designers were already leveraging image search engines

Because getting resultsfrom an image search

engine leads to inspiration

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Project Aladdin

Deep Learning

GPU instances

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Why we used Amazon SageMaker

• Go quicker from idea to production

• Distributed training out of the box

• One line of code to deploy models

• Almost the same cost as Amazon EC2 ($300/month)

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Demo: https://professionnels.tarkett.fr

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Next steps

Explore new possibilities opened by image search

• Find the best substitution when a product is out of stock • Understand « in-situ » the real user demand

• Incorporate external factors (seasons, fashion, styles)• Position our products against the competition• Generate new designs

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Merci!Sébastien [email protected] IT Projects & CCOE ManagerTarkett

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S U M M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.

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Built-in algorithmsorange: supervised, yellow: unsupervised

Linear Learner: regression, classification Image Classification: Deep Learning (ResNet)

Factorization Machines: regression, classification, recommendation

Object Detection (SSD): Deep Learning (VGG or ResNet)

K-Nearest Neighbors: non-parametric regression and classification

Neural Topic Model: topic modeling

XGBoost: regression, classification, rankinghttps://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 embedding IP Insights: usage patterns for IP addresses

Semantic Segmentation: Deep Learning

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Demo: Text Classification with BlazingText

https://github.com/awslabs/amazon-sagemaker-examples/tree/master/introduction_to_amazon_algorithms/blazingtext_text_classification_dbpedia

https://dl.acm.org/citation.cfm?id=3146354

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Demo: Image classification with Caltech-256

https://gitlab.com/juliensimon/dlnotebooks/sagemaker/

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Amazon SageMaker

Collect and prepare training data

Choose and optimize yourML algorithm

Train andTune ML Models

Set up andmanage

environmentsfor training

Deploy modelsin production

Scale and managethe productionenvironment

123

Build Train Deploy

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Getting startedhttp://aws.amazon.com/free

https://ml.awshttps://aws.amazon.com/sagemaker

https://github.com/aws/sagemaker-python-sdkhttps://github.com/aws/sagemaker-sparkhttps://github.com/awslabs/amazon-sagemaker-exampleshttps://gitlab.com/juliensimon/ent321

https://medium.com/@julsimonhttps://gitlab.com/juliensimon/dlnotebooks

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Thank you!

S U M M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.

Julien SimonGlobal Evangelist, AI and Machine Learning

@julsimonhttps://medium.com/julsimon

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