Build, Train, & Deploy ML Models Using SageMaker: Machine Learning Week San Francisco

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Machine Learning Week 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: Amit Sharma - Principal Solutions Architect, AWS

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Pop-up Loft
Build, Train and Deploy ML models using Amazon SageMaker
Amit Sharma
Principal Solutions Architect

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

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.

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

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.)

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

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

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

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…

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

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

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

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

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

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

Demo:
Neural networks and embeddings
for recommendations

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

Amazon reInvent 2020 Recap: AI and Machine Learning Video here: https://youtu.be/YSXe02Y5pHM NEW RELEASE! Build, Automate, Manage, and Scale ML Workflows with the NEW Amazon SageMaker Pipelines by Hallie Crosby Weishahn. Description of Talk and Demo AWS recently announced Amazon SageMaker Pipelines (https://aws.amazon.com/sagemaker/pipelines/), the first purpose-built, easy-to-use Continuous Integration and Continuous Delivery (CI/CD) service for machine learning. SageMaker Pipelines has three main components which improve the operational resilience and reproducibility of your workflows: 1) pipelines, 2) model registry, and 3) projects. In this talk and demo, Hallie will walk us through the new Amazon SageMaker Pipelines feature including MLOps support. Date/Time 9-10am US Pacific Time (Third Monday of Every Month) RSVP: https://www.eventbrite.com/e/1-hr-free-workshop-pipelineai-gpu-tpu-spark-ml-tensorflow-ai-kubernetes-kafka-scikit-tickets-45852865154 Meetup: https://www.meetup.com/Data-Science-on-AWS/ Zoom: https://zoom.us/j/690414331 Webinar ID: 690 414 331 Phone: +1 646 558 8656 (US Toll) or +1 408 638 0968 (US Toll) Related Links Meetup: https://meetup.datascienceonaws.com GitHub Repo: https://github.com/data-science-on-aws/ O'Reilly Book: https://datascienceonaws.com YouTube: https://youtube.datascienceonaws.com Slideshare: https://slideshare.datascienceonaws.com Support: https://support.pipeline.ai Monthly Workshop: https://www.eventbrite.com/e/full-day-workshop-kubeflow-gpu-kerastensorflow-20-tf-extended-tfx-kubernetes-pytorch-xgboost-tickets-63362929227 RSVP: https://www.eventbrite.com/e/1-hr-free-workshop-pipelineai-gpu-tpu-spark-ml-tensorflow-ai-kubernetes-kafka-scikit-tickets-45852865154

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Vuoi conoscere le opzioni per eseguire Microsoft Active Directory su AWS? Quando si spostano carichi di lavoro Microsoft in AWS, è importante considerare come distribuire Microsoft Active Directory per supportare la gestione, l'autenticazione e l'autorizzazione dei criteri di gruppo. In questa sessione, discuteremo le opzioni per la distribuzione di Microsoft Active Directory su AWS, incluso AWS Directory Service per Microsoft Active Directory e la distribuzione di Active Directory su Windows su Amazon Elastic Compute Cloud (Amazon EC2). Trattiamo argomenti quali l'integrazione del tuo ambiente Microsoft Active Directory locale nel cloud e l'utilizzo di applicazioni SaaS, come Office 365, con AWS Single Sign-On.

Computer Vision con AWS

Amazon Web Services

Database Oracle e VMware Cloud on AWS i miti da sfatare

Amazon Web Services

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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Build, Train, & Deploy ML Models Using SageMaker: Machine Learning Week San Francisco

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 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: 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!

Build, Train, & Deploy ML Models Using SageMaker: Machine Learning Week San Francisco

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