Automating Compliance Defense in the Cloud - Toronto FSI Symposium - October ...Amazon Web Services
Jodi Scrofani
Global Financial Services Compliance Strategist for AWS takes us on a journey of Security and Compliance mechanisms, that are mandatory in the Financial Services Industry, and explains how they are addressed by customers today on the AWS Cloud. She explains the AWS Shared Security Model, gives a detailed overview of audit and certifications achieved by AWS, and shows best practices and steps that FSI customers should take to ensure compliance and security.
Automating Compliance Defense in the Cloud - Toronto FSI Symposium - October ...Amazon Web Services
Jodi Scrofani
Global Financial Services Compliance Strategist for AWS takes us on a journey of Security and Compliance mechanisms, that are mandatory in the Financial Services Industry, and explains how they are addressed by customers today on the AWS Cloud. She explains the AWS Shared Security Model, gives a detailed overview of audit and certifications achieved by AWS, and shows best practices and steps that FSI customers should take to ensure compliance and security.
Powering Real-Time Analytics with Data Virtualization on AWS (ASEAN & ANZ)Denodo
Watch full webinar here: https://bit.ly/3h2yLnb
Presented at AWS Summit Online 2021 (ASEAN & ANZ)
Is your organization challenged with modernizing analytics in the cloud, while driving smarter data integration capabilities? With a logical data warehouse powered by data virtualization, you can combine all of the data across the enterprise and make it available to analytical and visualization tools that facilitate timely, insightful, and impactful decisions.
In this session, you will learn how data virtualization helps enterprises gain a unified view of the data across AWS, multi-cloud and hybrid cloud easily with a virtual data layer that abstracts business users from the technical details of where data resides.
HBX: Harvard Business School's Digital Education Goes Data-Centric with Amaz...Amazon Web Services
Learn how HBX, the digital learning initiative of Harvard Business School, became data-centric to deliver an innovative online business learning experience that improves student outcomes, teaching process and staff effectiveness while promoting continuous innovation across teams.
In this webinar, you’ll find out how Informatica and Amazon Redshift helped HBX deliver a solution to:
Rapidly and automatically integrate and unify multiple siloed data sources into a trusted cloud data warehouse.
Accelerate reporting, dashboarding, and self-service analytics for data-informed decisions, ongoing agile experimentation, and business enhancement, and much more.
In addition, learn how AWS and Informatica can help you deliver your own agile analytics initiative and use the power of scalable cloud data warehousing environments to fuel all of your data-centric initiatives.
Want to learn the basics of cloud computing with AWS and how various infrastructure building blocks fit together? If so, then join us in this webinar to find out how the AWS Cloud provides rapid access to flexible resources for your organization’s needs.
Enterprises that are embracing cloud computing are interested in driving fundamental changes in their business so they can compete in the future. IT transformation, enabled by cloud adoption, is a key component of this future success—from tighter alignment with business unit stakeholders to increased agility and pace of innovation. In this session, we explore the potential for transformation that comes with cloud adoption, and we discuss how some of the world’s leading enterprises were able to transform. We also explore organizational and technology best practices that you can implement to support transformation in your organization.
Demystifying Cloud Economics – Think Big: How to Build an Investment Case for...Amazon Web Services
While cloud is fast becoming the new normal for organisations of all sizes, many IT executives & budget owners still struggle to articulate the business value of moving to the cloud in terms that resonate with the Board and Broader C suite. In this session, we will talk through the impact cloud computing is having on the overall IT cost base, not just the infrastructure layer. We will also cover what the typical non-cost benefits are, how they can be measured and communicated. Finally we will provide a framework that can be used to calculate the transformation costs associated with moving to the cloud.
Blair Layton, Business Development Manager – Database Services, Amazon Web Services, APAC
Learn how cloud is the "New Normal" and the benefits of a cloud environment, with benefits for every industry, customer type, and stage of cloud adoption.
Today Financial Services Industry organisations are using AWS to both develop new sources of customer value as well as reinventing their core. In this session we will provide insights into the successful adoption patterns and trends that have emerged. We will also discuss how organisations in the FSI space have successfully navigated the people and processes challenges that initially inhibited enterprise wide adoption. Finally, through our experience in helping enterprises navigate this change, AWS has developed the Cloud Adoption Framework (CAF) to assist with planning, creating, managing and supporting the shift. We will spend time taking you through this framework to assess where you are on your Cloud journey and tangible takeaways that will help you accelerate.
The Modern Day Pressures and Trends Driving Cloud Access RequirementsAmazon Web Services
As the business landscape continues to shift towards cloud services, the need for businesses to move their critical applications and data from public internet connections to secure, private connections is growing. In this session you will learn how the telecoms industry is evolving its connectivity services to adopt cloud and data centre concepts such as orchestration, on-demand and pay for what you use. We will explore what you should look for and expect for direct cloud connectivity provided by these new and emerging services and what they can do for your business.
Vijay Rangarajan, Partner Solutions Architect, Amazon Web Services, APAC
Mark Daley, Director for Corporate Strategy and Product, Epsilon
With cloud, you have the flexibility to acquire and use IT resources and services on-demand, which represents a major shift from traditional approaches managing cost. A key first step on your organization’s cloud journey is to establish best practices for cost management in the cloud. AWS' cost optimization techniques help our customers understand cost drivers and effectively manage the cost of running existing application workloads or new ones in the cloud.
Financial Services companies are using machine learning to reduce fraud, streamline processes, and improve their bottom line. AWS provides tools that help them easily use AI tools like MXNet and Tensor Flow to perform predictive analytics, clustering, and more advanced data analyses. In this session, you'll hear how IHS Markit has used Machine Learning on AWS to help global banking institutions manage their commodities portfolios. You will also learn how the Amazon Machine Learning Service can take the hassle out of AI.
Powering Real-Time Analytics with Data Virtualization on AWS (ASEAN & ANZ)Denodo
Watch full webinar here: https://bit.ly/3h2yLnb
Presented at AWS Summit Online 2021 (ASEAN & ANZ)
Is your organization challenged with modernizing analytics in the cloud, while driving smarter data integration capabilities? With a logical data warehouse powered by data virtualization, you can combine all of the data across the enterprise and make it available to analytical and visualization tools that facilitate timely, insightful, and impactful decisions.
In this session, you will learn how data virtualization helps enterprises gain a unified view of the data across AWS, multi-cloud and hybrid cloud easily with a virtual data layer that abstracts business users from the technical details of where data resides.
HBX: Harvard Business School's Digital Education Goes Data-Centric with Amaz...Amazon Web Services
Learn how HBX, the digital learning initiative of Harvard Business School, became data-centric to deliver an innovative online business learning experience that improves student outcomes, teaching process and staff effectiveness while promoting continuous innovation across teams.
In this webinar, you’ll find out how Informatica and Amazon Redshift helped HBX deliver a solution to:
Rapidly and automatically integrate and unify multiple siloed data sources into a trusted cloud data warehouse.
Accelerate reporting, dashboarding, and self-service analytics for data-informed decisions, ongoing agile experimentation, and business enhancement, and much more.
In addition, learn how AWS and Informatica can help you deliver your own agile analytics initiative and use the power of scalable cloud data warehousing environments to fuel all of your data-centric initiatives.
Want to learn the basics of cloud computing with AWS and how various infrastructure building blocks fit together? If so, then join us in this webinar to find out how the AWS Cloud provides rapid access to flexible resources for your organization’s needs.
Enterprises that are embracing cloud computing are interested in driving fundamental changes in their business so they can compete in the future. IT transformation, enabled by cloud adoption, is a key component of this future success—from tighter alignment with business unit stakeholders to increased agility and pace of innovation. In this session, we explore the potential for transformation that comes with cloud adoption, and we discuss how some of the world’s leading enterprises were able to transform. We also explore organizational and technology best practices that you can implement to support transformation in your organization.
Demystifying Cloud Economics – Think Big: How to Build an Investment Case for...Amazon Web Services
While cloud is fast becoming the new normal for organisations of all sizes, many IT executives & budget owners still struggle to articulate the business value of moving to the cloud in terms that resonate with the Board and Broader C suite. In this session, we will talk through the impact cloud computing is having on the overall IT cost base, not just the infrastructure layer. We will also cover what the typical non-cost benefits are, how they can be measured and communicated. Finally we will provide a framework that can be used to calculate the transformation costs associated with moving to the cloud.
Blair Layton, Business Development Manager – Database Services, Amazon Web Services, APAC
Learn how cloud is the "New Normal" and the benefits of a cloud environment, with benefits for every industry, customer type, and stage of cloud adoption.
Today Financial Services Industry organisations are using AWS to both develop new sources of customer value as well as reinventing their core. In this session we will provide insights into the successful adoption patterns and trends that have emerged. We will also discuss how organisations in the FSI space have successfully navigated the people and processes challenges that initially inhibited enterprise wide adoption. Finally, through our experience in helping enterprises navigate this change, AWS has developed the Cloud Adoption Framework (CAF) to assist with planning, creating, managing and supporting the shift. We will spend time taking you through this framework to assess where you are on your Cloud journey and tangible takeaways that will help you accelerate.
The Modern Day Pressures and Trends Driving Cloud Access RequirementsAmazon Web Services
As the business landscape continues to shift towards cloud services, the need for businesses to move their critical applications and data from public internet connections to secure, private connections is growing. In this session you will learn how the telecoms industry is evolving its connectivity services to adopt cloud and data centre concepts such as orchestration, on-demand and pay for what you use. We will explore what you should look for and expect for direct cloud connectivity provided by these new and emerging services and what they can do for your business.
Vijay Rangarajan, Partner Solutions Architect, Amazon Web Services, APAC
Mark Daley, Director for Corporate Strategy and Product, Epsilon
With cloud, you have the flexibility to acquire and use IT resources and services on-demand, which represents a major shift from traditional approaches managing cost. A key first step on your organization’s cloud journey is to establish best practices for cost management in the cloud. AWS' cost optimization techniques help our customers understand cost drivers and effectively manage the cost of running existing application workloads or new ones in the cloud.
Financial Services companies are using machine learning to reduce fraud, streamline processes, and improve their bottom line. AWS provides tools that help them easily use AI tools like MXNet and Tensor Flow to perform predictive analytics, clustering, and more advanced data analyses. In this session, you'll hear how IHS Markit has used Machine Learning on AWS to help global banking institutions manage their commodities portfolios. You will also learn how the Amazon Machine Learning Service can take the hassle out of AI.
Slidedeck for my session on Insider Dev Tour 2019 (Lisbon Jul 29th).
Mostly based on tools and platform support for AI workloads and the options for edge computing and cloud computing.
ML.NET, WinML, DirectML, Model Builder, Azure Cognitive Services, ...
- Overview of a use case - Sentiment analysis
- Introduction - Using Jupyter Notebook & AWS SageMaker
- Setup New Project
- Setup and Run the Build CI/CD Pipeline
- Setup the Release Pipeline
- Test Build and Release Pipelines
- Testing the deployed solution
- Examining deployed model performance
Join us to see how Public-sector organizations and AWS Partners are combining Smart Devices and Artificial Intelligence to create flexible, secure and cost-effective solutions. Applying machine learning models to live video/audio, cameras can be transformed into flexible IoT devices that perform critical functions around public safety, security, property management, smart parking & environmental management. Learn how these solutions are architected using AWS services such as AWS IoT Core, AWS GreenGrass, AWS DeepLens, Amazon SageMaker and Amazon Alexa.
Leverage the power of machine learning on windowsMia Chang
Note:
The Content was modified from the Microsoft Content team.
Deck Owner: Nitah Onsongo
Tech/Msg Review: Cesar De La Torre, Simon Tao, Clarke Rahrig
---
Event: Insider Dev Tour Berlin
Event Description: Microsoft is going on a world tour with the announcements of Build 2019. The Insider Dev Tour focuses on innovations related to Microsoft 365 from a developer's perspective.
Date: June 7th, 2019
Event link: https://www.microsoft.com/de-de/techwiese/news/best-of-build-insider-dev-tour-am-7-juni-in-berlin.aspx
Linkedin: http://linkedin.com/in/mia-chang/
The PPT contains the following content:
1. What is Google Cloud Study Jam
2. What is Cloud Computing
3. Fundamentals of cloud computing
4. what is Generative AI
5. Fundamentals of Generative AI
6. Breif overview on Google Cloud Study Jam.
7. Networking Session.
Recommendation is one of the most popular applications in machine learning (ML). In this workshop, we’ll show you how to build a movie recommendation model based on factorization machines — one of the built-in algorithms of Amazon SageMaker — and the popular MovieLens dataset.
1) Learn about Myplanet's Headless CMS solution using Gatsby Preview and Contentful’s UI Extensions (https://www.contentful.com/resources/serverless/)
2) their Serverless project with IBM - using Apache OpenWhisk (https://www.ibm.com/cloud/functions)
3) how Myplanet got involved with AWS DeepRacer - a fun way to get started with Reinforcement Learning (RL), and their racing experience at re:Invent DeepRacer League (https://reinvent.awsevents.com/learn/deepracer/)
4) their Machine Learning (ML) research related to finding DeepRacer’s ideal line (https://medium.com/myplanet-musings/the-best-path-a-deepracer-can-learn-2a468a3f6d64).
BONUS: Two TED Talks referenced in the intro
5) When ideas have sex | Matt Ridley | Jul 14, 2010 https://www.ted.com/talks/matt_ridley_when_ideas_have_sex
6) Why The Best Leaders Make Love The Top Priority | Matt Tenney | Dec 5, 2019 https://www.youtube.com/watch?v=qCVoohdyI6I
VIDEO: https://youtu.be/ZH1xxmBNx5k
Integrating Amazon SageMaker into your Enterprise - AWS Online Tech TalksAmazon Web Services
Learning Objectives:
- Get an introduction to Amazon SageMaker
- Learn how to integrate Amazon SageMaker and other AWS Services within an Enterprise environment
- View a walkthrough of the machine learning lifecycle to cover best practices in the ML process
Similar to Track 2 Session 5_ 利用 SageMaker 深度學習容器化在廣告推播之應用 (20)
Come costruire servizi di Forecasting sfruttando algoritmi di ML e deep learn...Amazon Web Services
Il Forecasting è un processo importante per tantissime aziende e viene utilizzato in vari ambiti per cercare di prevedere in modo accurato la crescita e distribuzione di un prodotto, l’utilizzo delle risorse necessarie nelle linee produttive, presentazioni finanziarie e tanto altro. Amazon utilizza delle tecniche avanzate di forecasting, in parte questi servizi sono stati messi a disposizione di tutti i clienti AWS.
In questa sessione illustreremo come pre-processare i dati che contengono una componente temporale e successivamente utilizzare un algoritmo che a partire dal tipo di dato analizzato produce un forecasting accurato.
Big Data per le Startup: come creare applicazioni Big Data in modalità Server...Amazon Web Services
La varietà e la quantità di dati che si crea ogni giorno accelera sempre più velocemente e rappresenta una opportunità irripetibile per innovare e creare nuove startup.
Tuttavia gestire grandi quantità di dati può apparire complesso: creare cluster Big Data su larga scala sembra essere un investimento accessibile solo ad aziende consolidate. Ma l’elasticità del Cloud e, in particolare, i servizi Serverless ci permettono di rompere questi limiti.
Vediamo quindi come è possibile sviluppare applicazioni Big Data rapidamente, senza preoccuparci dell’infrastruttura, ma dedicando tutte le risorse allo sviluppo delle nostre le nostre idee per creare prodotti innovativi.
Ora puoi utilizzare Amazon Elastic Kubernetes Service (EKS) per eseguire pod Kubernetes su AWS Fargate, il motore di elaborazione serverless creato per container su AWS. Questo rende più semplice che mai costruire ed eseguire le tue applicazioni Kubernetes nel cloud AWS.In questa sessione presenteremo le caratteristiche principali del servizio e come distribuire la tua applicazione in pochi passaggi
Vent'anni fa Amazon ha attraversato una trasformazione radicale con l'obiettivo di aumentare il ritmo dell'innovazione. In questo periodo abbiamo imparato come cambiare il nostro approccio allo sviluppo delle applicazioni ci ha permesso di aumentare notevolmente l'agilità, la velocità di rilascio e, in definitiva, ci ha consentito di creare applicazioni più affidabili e scalabili. In questa sessione illustreremo come definiamo le applicazioni moderne e come la creazione di app moderne influisce non solo sull'architettura dell'applicazione, ma sulla struttura organizzativa, sulle pipeline di rilascio dello sviluppo e persino sul modello operativo. Descriveremo anche approcci comuni alla modernizzazione, compreso l'approccio utilizzato dalla stessa Amazon.com.
Come spendere fino al 90% in meno con i container e le istanze spot Amazon Web Services
L’utilizzo dei container è in continua crescita.
Se correttamente disegnate, le applicazioni basate su Container sono molto spesso stateless e flessibili.
I servizi AWS ECS, EKS e Kubernetes su EC2 possono sfruttare le istanze Spot, portando ad un risparmio medio del 70% rispetto alle istanze On Demand. In questa sessione scopriremo insieme quali sono le caratteristiche delle istanze Spot e come possono essere utilizzate facilmente su AWS. Impareremo inoltre come Spreaker sfrutta le istanze spot per eseguire applicazioni di diverso tipo, in produzione, ad una frazione del costo on-demand!
In recent months, many customers have been asking us the question – how to monetise Open APIs, simplify Fintech integrations and accelerate adoption of various Open Banking business models. Therefore, AWS and FinConecta would like to invite you to Open Finance marketplace presentation on October 20th.
Event Agenda :
Open banking so far (short recap)
• PSD2, OB UK, OB Australia, OB LATAM, OB Israel
Intro to Open Finance marketplace
• Scope
• Features
• Tech overview and Demo
The role of the Cloud
The Future of APIs
• Complying with regulation
• Monetizing data / APIs
• Business models
• Time to market
One platform for all: a Strategic approach
Q&A
Rendi unica l’offerta della tua startup sul mercato con i servizi Machine Lea...Amazon Web Services
Per creare valore e costruire una propria offerta differenziante e riconoscibile, le startup di successo sanno come combinare tecnologie consolidate con componenti innovativi creati ad hoc.
AWS fornisce servizi pronti all'utilizzo e, allo stesso tempo, permette di personalizzare e creare gli elementi differenzianti della propria offerta.
Concentrandoci sulle tecnologie di Machine Learning, vedremo come selezionare i servizi di intelligenza artificiale offerti da AWS e, anche attraverso una demo, come costruire modelli di Machine Learning personalizzati utilizzando SageMaker Studio.
OpsWorks Configuration Management: automatizza la gestione e i deployment del...Amazon Web Services
Con l'approccio tradizionale al mondo IT per molti anni è stato difficile implementare tecniche di DevOps, che finora spesso hanno previsto attività manuali portando di tanto in tanto a dei downtime degli applicativi interrompendo l'operatività dell'utente. Con l'avvento del cloud, le tecniche di DevOps sono ormai a portata di tutti a basso costo per qualsiasi genere di workload, garantendo maggiore affidabilità del sistema e risultando in dei significativi miglioramenti della business continuity.
AWS mette a disposizione AWS OpsWork come strumento di Configuration Management che mira ad automatizzare e semplificare la gestione e i deployment delle istanze EC2 per mezzo di workload Chef e Puppet.
Scopri come sfruttare AWS OpsWork a garanzia e affidabilità del tuo applicativo installato su Instanze EC2.
Microsoft Active Directory su AWS per supportare i tuoi Windows WorkloadsAmazon Web Services
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.
Dal riconoscimento facciale al riconoscimento di frodi o difetti di fabbricazione, l'analisi di immagini e video che sfruttano tecniche di intelligenza artificiale, si stanno evolvendo e raffinando a ritmi elevati. In questo webinar esploreremo le possibilità messe a disposizione dai servizi AWS per applicare lo stato dell'arte delle tecniche di computer vision a scenari reali.
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 NodeJSAmazon 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.
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 sfatareAmazon 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.
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.
2. Machine learning workflow
Data acquisition
curation and
labeling
Data preparation
for training
Design and run
experiments
Model
development
Model
optimization
Deployment
4. Deep learning is computationally expensive,
but can be scaled-out
How do we go from
AWS
CLI
this,
AWS
CLI
to this
. . .
. . .
. . .
. . .
AWS Cloud
EC2 instance
5. Scaling-out deep learning training
Parallel experiments Distributed training
Distributing training of
a single model to train
faster
Different models
running parallel to find
the best model
6. But there are challenges to scaling
AWS
CLI
Code and
dependencies
Infrastructure
management
Cluster
management
. . .
. . .
. . .
. . .
AWS Cloud
7. Machine learning stack is complex
• “My code requires building several dependencies from source”
• “My code isn’t taking advantage of the GPU/GPUs”
• “Is cuDNN, NCCL installed? Is it the right version?”
• “My code is running slow on CPUs”
• “Oh wait, is it taking advantage of AVX instruction set”
• “I updated my drivers and training is now slower/errors out”
• “My cluster runs a different version of framework/Linux distro”
Makes portability, collaboration, and
scaling training really, really hard!
8. NVIDIA drivers 436.15
Ubuntu 16.04
TensorFlow 1.13
Keras
horovod
numpy
scipy
others…
Mkl 2019 v3CPU:
cuDNN 7.1
cublas 10
nccl 2
CUDA toolkit 10
GPU:
scikit-learn
pandas
openmpi
Python
My code
Development system
NVIDIA drivers 410.68
Centos 7
Training
cluster
TensorFlow 1.14
Keras
horovod
numpy
scipy
others…
Mkl 2019 v2CPU:
cuDNN 7.5
cublas 10
nccl 2.4
CUDA toolkit 10
GPU:
scikit-learn
pandas
openmpi
Python
My code
Multiple
points
of failureDevelopment
system
Training
cluster
9. Containers
for
machine
learning
Container runtime
Infrastructure
NVIDIA drivers
Host OS
Packages:
• Training code
• Dependencies
• Configurations
TensorFlow
mkl
cuDNN
cuBLAS
NCCL
CUDA toolkit
CPU:
GPU:
TensorFlow
container
image
Keras
horovod
numpy
scipy
others
scikit-
learn
pandas
openmpi
Python
+
Your training
scripts
ML environments that
are:
• Lightweight
• Portable
• Scalable
• Consistent
10. TensorFlow
mkl
cuDNN
cuBLAS
NCCL
CUDA toolkit
NVIDIA drivers
Host OS
CPU:
GPU:
Container runtime
TensorFlow
container
image
Keras
horovod
numpy
scipy
others
scikit-
learn
pandas
openmpi
Python
Development system
NVIDIA drivers
Host OS
Container runtime
Training cluster
TensorFlow
mkl
cuDNN
cuBLAS
NCCL
CUDA toolkit
CPU:
GPU:
TensorFlow
container
image
Keras
horovod
numpy
scipy
others
scikit-
learn
pandas
openmpi
Python
+
Your training
scripts
+
Your training
scripts
Container
registry
Amazon ECR
11. AWS Deep Learning Containers
https://docs.aws.amazon.com/dlami/latest/devguide/deep-learning-containers-
images.html
Prepackaged machine learning
container images fully
configured and validated
Optimized for performance with
latest NVIDIA driver, CUDA
libraries, and Intel libraries
12. Challenges with scaling deep learning
AWS
CLI
Code and
dependencies
Infrastructure
management
Cluster
management
. . .
. . .
. . .
. . .
AWS Cloud
13. ML infrastructure and cluster management
Compute
Where the containers run
Amazon
EC2
• Getting started easy, scaling hard
• Rely on IT/Ops for setup management
• DIY setup for ML use-cases
• Optimizing cost: DIY
Jupyter notebook
instances
High-performance
algorithms
Large-scale
training
Optimization One-click
deployment
Fully managed with
auto scaling
ML services
Fully managed service that
covers the entire machine
learning workflow
Amazon SageMaker • Easy, couple of LOC to scale
• Fully managed, no infrastructure
effort
• Designed for machine learning
• Optimizing cost: on-demand / Spot
Management
Deployment, scheduling, scaling,
and management of containerized
applications
Amazon Elastic
Kubernetes Service
Amazon Elastic
Container Service
• Getting started hard, scaling easy
• Rely on IT/Ops for setup management
• DIY setup for ML use-cases
• Optimizing cost: DIY
14. Hyperparameter search experiment using Amazon SageMaker
Local laptop or
desktop with
Amazon SageMaker
SDK
Custom container
Code files
Docker build
Approach:
1. Build a Docker image with your
training scripts
2. Specify instance type (CPU, GPU)
3. Specify number of instances and
hyperparameters to tune
4. Launch the tuning job
AWS CLI
Container
registry
Amazon ECR
Amazon Simple Storage
ServiceFully managed
Amazon SageMaker
cluster
17. The Leading Big Data Company in Asia
Data Drives Transactions
18. Milestone
20172014 2015 2016 20182008 2011 2019
Won Agency &
Advertiser Of The
Year in 4 categories
Won three Hong
Kong Spark Awards
Won Agency & Advertiser Of
The Year in 3 categories: 2 Gold
& 1 Sliver
Festival of Media Global
MARKies Awards
ECI Awards
Top Mobile Awards (TMA)
Founded in
Taipei
Establishment of Hong
Kong andTokyo offices
Won Bronze for Campaign
Greater China Specialist
Agency of the Year
Won Bronze for Campaign
Greater China Specialist
Agency of the Year for two
consecutive years
Establishment of
Singapore office
Top 10 Big Data
Solutions Providers in
the APAC Region
Won Agency & Advertiser
Of The Year in 4 categories:
3 Golds & 1 Silver
Campaign Digital Media Awards
MARKies Awards 2017
Golden Mouse
Tiger Roar
Won Gold for Best In-
App Advertising
Won Bronze for Best
In-App Advertising
Won Bronze for Best
Mobile Advertising
Platform
Received$7Min
SeriesAFunding
RaisedUS$10Min
SeriesBFunding
Won 3rd for Forbes
China’s Top 100
Privately Held Small
Businesses
2010
Establishment of
Shanghaioffice
Establishment of
Osaka office
Won one Gold and
two Bronze for
Mob-Ex Awards 2018
Won Gold for Best Location-based at
Mob-Ex Awards 2019
Mob-ex Awards 2017
Japan Office
Expansion
Mob-ex Awards 2016
Launched1st LBS
mobileadnetwork
inAsia
Won Bronze in Best In-app
Advertising
Won Bronze in Best Mobile
Advertising Platform
Won Gold winner in the Best Data-
driven Marketing Campaign category at
Brain Magazine Awards 2019
Mediazone’s annual Most Valuable
Services Awards in Hong Kong 2019
‧ “Most Reliable Big Data Analytics and
Application Leader”
‧ “Best Cross-Border Marketing Services”
‧ “Excellence in Corporate Governance
Customer Services”
Won Silver for Best Idea– Mobile at
Markies Award 2019
Won Silver award for digital
transformation in eASIA Awards 2019
Won Big Data Solution award at the
Capital Magazine’s BOB Awards 2019
19. Tokyo
Taipei
Shanghai
Hong Kong
Singapore
Osaka
Bangkok
Vpon Big Data Group
The Leading Big Data Company in Asia
900Million
unique devices per month
21Billion
daily biddable inventory
12years
of services across APAC
7offices in Hong Kong,
Shanghai, Singapore,
Taipei, Bangkok,
Tokyo and Osaka
1500renowned brands with
collaborative experiences
20. Trata DMP - Largest Travel Audience Data Pool in Asia
100M+
Travel Intent
Data in Asia
60M+
China Passport
Holder
1000+
Traveler Tags
21. Vpon Available Tag Categories
Country
Province/ City
Gender
Income level
Device Language
Age group of family members
Ad Interests
Operation System
Ad Format Preference
Travel Pattern
App Interests
Lifestyle
Age
Fashion Style
DemographicsLocations InterestsBehaviors
Destination Country
Based on multiple combinations of the tags, you can identify some of the
hidden segment groups who may be your potential audiences with high chance.
24. Vpon AI Technology
More than 10 machine learning models with real case
applications.
Gender Prediction Model
Lookalike Modeling
Recommendation
Fraud Detection
Automated Location
Discovery
Behavior Prediction
Traffic Accident Prediction Intelligent Route Mining
Text Mining
Traveling behaviors
prediction
...
27. Research Stage
• Binary Classification.
• Given a set of available mobile related features during a specific period, we want to predict
whether the device owner behaves more like a man or woman.
• Features
• Device geo info, app usage patterns, device info, active time range, etc.
• Goals
• Accurately identify gender for new incoming devices.
• Improve prediction accuracy for previous mis-labeled devices.
Problem Definition
28. Research Stage
• Ground-truth Data Analysis
• Gender Label Distribution, Feature Importance, etc.
• Data visualization
• Matplotlib, Tableau.
Dataset Understanding
30. Research Stage
• Data Collection
• Collect and retrieve feature data.
• Data Integration
• Enrich raw data with other useful info, e.g. google store metadata, poi.
• Data Cleaning
• Remove null or abnormal values.
Dataset Preparation
32. Research Stage
• Feature Engineering
• One-hot encoding, Feature combination, etc.
• Model Training and Parameter Tuning
• Logistic Regression, XGboost, Deep Learning Framework, etc.
• Feature Normalization.
Model Training
36. Research Stage
• Research with local machine.
• Pull data from S3 and repeat research work on local machine.
• Research with cloud env.
• EC2.
• EMR.
• SageMaker.
Research Tools and Environments.
Amazon Simple Storage
Service
Client
Amazon Simple Storage
ServiceClient
AWS Cloud
AWS Cloud
VPC
Public subnet
Amazon EC2
Amazon EMR
37. From Research to Production
Hidden Technical Debt in Machine Learning Systems
Sculley et al., Hidden Technical Debt in Machine Learning Systems. NIPS 2015.
38. From Research to Production
Code + Model + Data
Breck et al., The ML Test Score: A Rubric for ML Production Readiness and
Technical Debt Reduction. IEEE Big Data 2017.
42. From Research to Production
• Pipeline Platform Requirements.
• Reliable.
• Visualization tool.
• Pipeline scripting languages.
Composability
43. From Research to Production
Composability
Amazon Simple Storage
Service
Client
AWS Cloud
VPC
Public subnet Private subnet
Amazon EMRAmazon EC2
Elastic IP
address
Security group Security group
44. From Research to Production
• Prediction Service.
• Prediction service is easily to be scaled out.
• Prediction service can process batch or on-line requests interchangeably.
Scalability
45. From Research to Production
• Multi-platform Model Deployment.
• Model deployment should not be limited to a specific platform.
• Model deployment should be easily to be integrated with other services, e.g. current existed
microservices.
• Model packaging is flexible so that adding self-made functions is achievable.
Portability
46. From Research to Production
• Open Source ML Pipeline Platform.
• Kubeflow, mlflow, airflow, TFX, etc.
• Prediction Service Framework.
• Sagemaker, self-made restful api service, etc.
Candidate Solutions
47. From Research to Production
• AWS ML Experts
• Organized 3 one-day offsite workshop together with AWS ML experts.
• Hands-on packaging ML model into container and deploying to SageMaker.
• Practice with cloud9 and SageMaker Notebook.
• Consult with ML marketplace opportunity.
Candidate Solutions
48. Production Stage
Tradeoff and Decision
ML Pipeline Platform Reason
Compatibility Jenkins. Lowest learning curve.
Portability Jenkins. Lowest platform transferring cost.
Scalability Jenkins. Jenkins can be deployed to k8s.
49. Production Stage
Tradeoff and Decision
Prediction Service Reason
Compatibility
Deploy customized SageMaker container on
VM.
1. Easily to be integrated with Jenkins.
2. Easily to be deployed back to SageMaker.
Portability
Deploy customized SageMaker container on
VM.
Easily migrate to other platforms.
Scalability Replicate VM and add LB. Easily scale out by LB.
50. Production Stage
Architecture
Amazon Simple Storage
Service
Client
AWS Cloud
VPC
Public subnet Private subnet
Amazon EMRAmazon EC2
Elastic IP
address
Security group Security group
Security group
Amazon EC2
Elastic IP
address
51. Production Stage
• Definition
• A containerized application for managing the prediction phase in machine learning product
lifecycle.
• Functions
• Official prediction requests API portal.
• http://[IP]:[port]/api/providers/prediction/create
• http://[IP]:[port]/api/providers/prediction/invoke
• http://[IP]:[port]/api/providers/prediction/check
• Monitoring
• Tracking prediction status.
• Recording and comparing prediction results.
MLapp
53. Conclusion
• Project time allocation
• 40% on research.
• 30% on model and prediction results validation.
• 30% on mlops and developments.
• Accuracy
• Precision can achieve more than 80%.
• Total gender prediction pipeline execution time
• Less than 30 minutes with monthly data.
Current Status
54. Conclusion
• Ensure prediction quality at the top.
• Always understand current data distribution.
• Establish monitors to control data quality from the root.
• Keep production engineering work simple and reliable.
Lessons Learned