Building a Recommender System Using Amazon SageMaker's Factorization Machine Algorithm: Machine Learning Week San Francisco

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Machine Learning Week at the San Francisco Loft: Building a Recommender System Using Amazon SageMaker's Factorization Machine Algorithm Factorization Machines are a powerful algorithm in the click prediction and recommendation space. Amazon SageMaker has a nearly infinitely scalable implementation that we'll show you how to use to build a recommender of your own. Speaker: David Arpin - AI Platform Selections Leader, AI Platforms

© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
David Arpin – Amazon SageMaker Team
2018-09-05
Building a recommender with
Amazon SageMaker

© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Agenda
• Amazon SageMaker overview
• Q & A
• 10 minute break / setup
• Recommender system example
• Factorization machines
• BlazingText

© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Frameworks
& Hardware
AWS Deep Learning AMI
GPU
(P3 Instances)
MobileCPU
IoT
(Greengrass)
Vision:
Rekognition Image
Rekognition Video
Speech:
Polly
Transcribe
Language:
Lex Translate
Comprehend
Apache
MXNet
PyTorch
Cognitive
Toolkit
Keras
Caffe2
& Caffe
TensorFlow Gluon
Application
Services
Platform
Services
Amazon Machine
Learning
Mechanical
Turk
Spark &
EMR
Amazon
SageMaker
AWS
DeepLens
AWS ML & AI

© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Amazon SageMaker
Build
Algorithms
Frameworks
Docker
Tune
Train Deploy

© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
… or Apache Spark
through EMR and
the SageMaker
Spark SDK...
UX
Use SageMaker‘s
hosted Notebook
Instances...
... or SageMaker‘s
Console for a point
and click
experience...
... or your own
device (EC2,
laptop, etc.)

© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Zero setup
Training
Streaming
datasets +
distributed
compute
Docker / ECS Deploy trained
models locally or to
SageMaker,
Greengrass, DeepLens

© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
One step
deployment
Scalable, high
throughput, and
high reliability
A/B testing Use your own
model
Hosting

© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Zero setup
Batch
Stream data from
and back to S3
Distribute across
CPU or GPU
instances
Use your own
model

© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
XGBoost, FM,
Linear, k-NN, and
Forecasting for
supervised
learning
k-Means, PCA, and
Random Cut
Forest for
unsupervised
learning
Image
classification and
object detection
for computer
vision
LDA, Neural Topic
Model, Seq2seq,
and Word2Vec for
text and NLP
Built-in algorithms

© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
… explore and
refine models in a
single Notebook
Instance
Deep learning framework containers
… deploy to
production
Sample your
data…
Use the same code
to train on the full
dataset in a cluster
of GPU instances…

© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Bring your own algorithm
... add algorithm
code to a Docker
container...
Pick your
preferred
framework...
... publish to ECS

© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Automatic Model Tuning
Run a large set of training
jobs with varying
hyperparameters...
... and search the
hyperparameter space for
improved accuracy.

© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Examples

© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Getting started
• Create a new SageMaker notebook instance
• Last name: A-I use Oregon, J-R use Virginia, S-Z use Ohio
• Create a new SageMaker IAM role with access to any S3
bucket
• No VPC, etc. needed
• Open a terminal:
• cd SageMaker/
• aws s3 cp s3://sagemaker-conferences-
oregon/fm_amazon_recommender.ipynb ./

© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
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Amazon Web Services e VMware organizzano un evento virtuale gratuito il prossimo mercoledì 14 Ottobre dalle 12:00 alle 13:00 dedicato a VMware Cloud ™ on AWS, il servizio on demand che consente di eseguire applicazioni in ambienti cloud basati su VMware vSphere® e di accedere ad una vasta gamma di servizi AWS, sfruttando a pieno le potenzialità del cloud AWS e tutelando gli investimenti VMware esistenti. Molte organizzazioni sfruttano i vantaggi del cloud migrando i propri carichi di lavoro Oracle e assicurandosi notevoli vantaggi in termini di agilità ed efficienza dei costi. La migrazione di questi carichi di lavoro, può creare complessità durante la modernizzazione e il refactoring delle applicazioni e a questo si possono aggiungere rischi di prestazione che possono essere introdotti quando si spostano le applicazioni dai data center locali.

Crea la tua prima serverless ledger-based app con QLDB e NodeJS

Amazon Web Services

Molte aziende oggi, costruiscono applicazioni con funzionalità di tipo ledger ad esempio per verificare lo storico di accrediti o addebiti nelle transazioni bancarie o ancora per tenere traccia del flusso supply chain dei propri prodotti. Alla base di queste soluzioni ci sono i database ledger che permettono di avere un log delle transazioni trasparente, immutabile e crittograficamente verificabile, ma sono strumenti complessi e onerosi da gestire. Amazon QLDB elimina la necessità di costruire sistemi personalizzati e complessi fornendo un database ledger serverless completamente gestito. In questa sessione scopriremo come realizzare un'applicazione serverless completa che utilizzi le funzionalità di QLDB.

API moderne real-time per applicazioni mobili e web

Amazon Web Services

Con l’ascesa delle architetture di microservizi e delle ricche applicazioni mobili e Web, le API sono più importanti che mai per offrire agli utenti finali una user experience eccezionale. In questa sessione impareremo come affrontare le moderne sfide di progettazione delle API con GraphQL, un linguaggio di query API open source utilizzato da Facebook, Amazon e altro e come utilizzare AWS AppSync, un servizio GraphQL serverless gestito su AWS. Approfondiremo diversi scenari, comprendendo come AppSync può aiutare a risolvere questi casi d’uso creando API moderne con funzionalità di aggiornamento dati in tempo reale e offline. Inoltre, impareremo come Sky Italia utilizza AWS AppSync per fornire aggiornamenti sportivi in tempo reale agli utenti del proprio portale web.

Database Oracle e VMware Cloud™ on AWS: i miti da sfatare

Amazon Web Services

Molte organizzazioni sfruttano i vantaggi del cloud migrando i propri carichi di lavoro Oracle e assicurandosi notevoli vantaggi in termini di agilità ed efficienza dei costi. La migrazione di questi carichi di lavoro, può creare complessità durante la modernizzazione e il refactoring delle applicazioni e a questo si possono aggiungere rischi di prestazione che possono essere introdotti quando si spostano le applicazioni dai data center locali. In queste slide, gli esperti AWS e VMware presentano semplici e pratici accorgimenti per facilitare e semplificare la migrazione dei carichi di lavoro Oracle accelerando la trasformazione verso il cloud, approfondiranno l’architettura e dimostreranno come sfruttare a pieno le potenzialità di VMware Cloud ™ on AWS.

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Introduzione a Amazon Elastic Container Service

Amazon Web Services

Amazon Elastic Container Service (Amazon ECS) è un servizio di gestione dei container altamente scalabile, che semplifica la gestione dei contenitori Docker attraverso un layer di orchestrazione per il controllo del deployment e del relativo lifecycle. In questa sessione presenteremo le principali caratteristiche del servizio, le architetture di riferimento per i differenti carichi di lavoro e i semplici passi necessari per poter velocemente migrare uno o più dei tuo container.

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Database Oracle e VMware Cloud on AWS i miti da sfatare

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Building a Recommender System Using Amazon SageMaker's Factorization Machine Algorithm: Machine Learning Week San Francisco

2. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Agenda • Amazon SageMaker overview • Q & A • 10 minute break / setup • Recommender system example • Factorization machines • BlazingText

3. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Frameworks & Hardware AWS Deep Learning AMI GPU (P3 Instances) MobileCPU IoT (Greengrass) Vision: Rekognition Image Rekognition Video Speech: Polly Transcribe Language: Lex Translate Comprehend Apache MXNet PyTorch Cognitive Toolkit Keras Caffe2 & Caffe TensorFlow Gluon Application Services Platform Services Amazon Machine Learning Mechanical Turk Spark & EMR Amazon SageMaker AWS DeepLens AWS ML & AI

6. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. … or Apache Spark through EMR and the SageMaker Spark SDK... UX Use SageMaker‘s hosted Notebook Instances... ... or SageMaker‘s Console for a point and click experience... ... or your own device (EC2, laptop, etc.)

8. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Zero setup Training Streaming datasets + distributed compute Docker / ECS Deploy trained models locally or to SageMaker, Greengrass, DeepLens

13. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. XGBoost, FM, Linear, k-NN, and Forecasting for supervised learning k-Means, PCA, and Random Cut Forest for unsupervised learning Image classification and object detection for computer vision LDA, Neural Topic Model, Seq2seq, and Word2Vec for text and NLP Built-in algorithms

15. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. … explore and refine models in a single Notebook Instance Deep learning framework containers … deploy to production Sample your data… Use the same code to train on the full dataset in a cluster of GPU instances…

17. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Bring your own algorithm ... add algorithm code to a Docker container... Pick your preferred framework... ... publish to ECS

19. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Automatic Model Tuning Run a large set of training jobs with varying hyperparameters... ... and search the hyperparameter space for improved accuracy.

21. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Getting started • Create a new SageMaker notebook instance • Last name: A-I use Oregon, J-R use Virginia, S-Z use Ohio • Create a new SageMaker IAM role with access to any S3 bucket • No VPC, etc. needed • Open a terminal: • cd SageMaker/ • aws s3 cp s3://sagemaker-conferences- oregon/fm_amazon_recommender.ipynb ./

Building a Recommender System Using Amazon SageMaker's Factorization Machine Algorithm: Machine Learning Week San Francisco

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Building a Recommender System Using Amazon SageMaker's Factorization Machine Algorithm: Machine Learning Week San Francisco