AWS re:Invent is an annual global conference of the Amazon Web Services community held in Las Vegas. In 2017, we held 1000+ breakout sessions and attracted over 40,000 attendees. The event offers expanded opportunities to learn about the latest AWS releases, use cases and business benefits, not to mention diving deep into hot topics and meeting with our subject matter experts.
Missed it? Don’t worry, we are bringing AWS re:Invent to Hong Kong on Jan 18, 2018. Packed in a day, AWS re:Invent 2017 Recap Hong Kong will showcase new releases announced at re:Invent 2017 on Serverless & Container, DevOps & Mobile, Artificial Intelligence & Machine Learning and more. Local customers will also be invited to share their re:Invent experience and success stories with AWS.
Discover the latest services and features from Amazon Web Services and learn how to integrate them into your applications
Build Data Driven Apps with Real-time and Offline CapabilitiesAmazon Web Services
All application developers today need to be concerned with offline access, realtime communications and efficient data fetching. These techniques are no longer optional for great user experiences yet are difficult to engineer and scale from scratch. In this session you’ll get a deep dive on using AWS AppSync to enable your applications for offline access, including optimistic updates on lossy connections, with just a few lines of code. You’ll learn how application data synchronization takes place with the cloud, how you can control the process, programming interfaces for native applications such as iOS and JavaScript based applications across the web, React Native, and Ionic. Additionally you’ll see how using GraphQL enables your application to efficiently leverage the network for queries and mutations while still having a scalable and fast connection for realtime updates when using subscriptions to data changes.
Analyzing Streaming Data in Real-time with Amazon KinesisAmazon Web Services
As more and more organizations strive to gain real-time insights into their business, streaming data has become ubiquitous. Typical streaming data analytics solutions require specific skills and complex infrastructure. However, with Amazon Kinesis Analytics, you can analyze streaming data in real-time with standard SQL—there is no need to learn new programming languages or processing frameworks.
In this session, we dive deep into the capabilities of Amazon Kinesis Analytics using real-world examples. We’ll present an end-to-end streaming data solution using Amazon Kinesis Streams for data ingestion, Amazon Kinesis Analytics for real-time processing, and Amazon Kinesis Firehose for persistence. We review in detail how to write SQL queries using streaming data and discuss best practices to optimize and monitor your Amazon Kinesis Analytics applications. Lastly, we discuss how to estimate the cost of the entire system.
Unlocking New Todays: Artificial Intelligence and Data Platforms on AWSAmazon Web Services
In this session, you will learn about our strategy for driving machine learning innovation for our customers and learn what’s new from AWS in the machine learning space. We will discuss and demonstrate the latest new services for ML on AWS: Amazon SageMaker, AWS DeepLens, Amazon Rekogntion Video, Amazon Translate, Amazon Transcribe, and Amazon Comprehend. Attend this session to understand how to make the most of machine learning in the cloud.
AWS re:Invent is an annual global conference of the Amazon Web Services community held in Las Vegas. In 2017, we held 1000+ breakout sessions and attracted over 40,000 attendees. The event offers expanded opportunities to learn about the latest AWS releases, use cases and business benefits, not to mention diving deep into hot topics and meeting with our subject matter experts.
Missed it? Don’t worry, we are bringing AWS re:Invent to Hong Kong on Jan 18, 2018. Packed in a day, AWS re:Invent 2017 Recap Hong Kong will showcase new releases announced at re:Invent 2017 on Serverless & Container, DevOps & Mobile, Artificial Intelligence & Machine Learning and more. Local customers will also be invited to share their re:Invent experience and success stories with AWS.
Hear how local customers have successfully built and migrated their applications to AWS and learn their tips and tricks
Build Data Driven Apps with Real-time and Offline CapabilitiesAmazon Web Services
All application developers today need to be concerned with offline access, realtime communications and efficient data fetching. These techniques are no longer optional for great user experiences yet are difficult to engineer and scale from scratch. In this session you’ll get a deep dive on using AWS AppSync to enable your applications for offline access, including optimistic updates on lossy connections, with just a few lines of code. You’ll learn how application data synchronization takes place with the cloud, how you can control the process, programming interfaces for native applications such as iOS and JavaScript based applications across the web, React Native, and Ionic. Additionally you’ll see how using GraphQL enables your application to efficiently leverage the network for queries and mutations while still having a scalable and fast connection for realtime updates when using subscriptions to data changes.
Analyzing Streaming Data in Real-time with Amazon KinesisAmazon Web Services
As more and more organizations strive to gain real-time insights into their business, streaming data has become ubiquitous. Typical streaming data analytics solutions require specific skills and complex infrastructure. However, with Amazon Kinesis Analytics, you can analyze streaming data in real-time with standard SQL—there is no need to learn new programming languages or processing frameworks.
In this session, we dive deep into the capabilities of Amazon Kinesis Analytics using real-world examples. We’ll present an end-to-end streaming data solution using Amazon Kinesis Streams for data ingestion, Amazon Kinesis Analytics for real-time processing, and Amazon Kinesis Firehose for persistence. We review in detail how to write SQL queries using streaming data and discuss best practices to optimize and monitor your Amazon Kinesis Analytics applications. Lastly, we discuss how to estimate the cost of the entire system.
Unlocking New Todays: Artificial Intelligence and Data Platforms on AWSAmazon Web Services
In this session, you will learn about our strategy for driving machine learning innovation for our customers and learn what’s new from AWS in the machine learning space. We will discuss and demonstrate the latest new services for ML on AWS: Amazon SageMaker, AWS DeepLens, Amazon Rekogntion Video, Amazon Translate, Amazon Transcribe, and Amazon Comprehend. Attend this session to understand how to make the most of machine learning in the cloud.
AWS re:Invent is an annual global conference of the Amazon Web Services community held in Las Vegas. In 2017, we held 1000+ breakout sessions and attracted over 40,000 attendees. The event offers expanded opportunities to learn about the latest AWS releases, use cases and business benefits, not to mention diving deep into hot topics and meeting with our subject matter experts.
Missed it? Don’t worry, we are bringing AWS re:Invent to Hong Kong on Jan 18, 2018. Packed in a day, AWS re:Invent 2017 Recap Hong Kong will showcase new releases announced at re:Invent 2017 on Serverless & Container, DevOps & Mobile, Artificial Intelligence & Machine Learning and more. Local customers will also be invited to share their re:Invent experience and success stories with AWS.
Hear how local customers have successfully built and migrated their applications to AWS and learn their tips and tricks
In this session, we discuss the evolution of database and analytics services in AWS, the new database and analytics services and features we launched this year, and our vision for continued innovation in this space. We are witnessing an unprecedented growth in the amount of data collected, in many different forms. Storage, management, and analysis of this data require database services that scale and perform in ways not possible before. AWS offers a collection of database and other data services—including Amazon Aurora, Amazon DynamoDB, Amazon RDS, Amazon Redshift, Amazon ElastiCache, Amazon Kinesis, and Amazon EMR—to process, store, manage, and analyze data. In this session, we provide an overview of AWS database and analytics services and discuss how customers are using these services today.
Mai-Lan Tomsen Bukovec, Vice President and General Manager Amazon S3.
Axel Winter, Group CTO, Central Group
George Wang, SVP IT, Singapore Airlines
Akshay Garg, CEO, FinAccel
Join builders, dreamers and innovators across South East Asia for the 2018 AWS Summit Singapore Keynote.
Watch live as Mai-Lan Tomsen Bukovec, Vice President and General Manager Amazon S3, together with AWS Customers Singapore Airlines, Central Group and FinAccel share how to build for tomorrow’s world.
Unlocking New Todays - Artificial Intelligence and Data Platforms on AWSAmazon Web Services
熱門創新服務專題
Unlocking New Todays: Artificial Intelligence and Data Platforms on AWS (Level 200)
Speaker: Paul Yung, Head of Territory Development HKT, AWS
Migrating your traditional Data Warehouse to a Modern Data LakeAmazon Web Services
In this session, we discuss the latest features of Amazon Redshift and Redshift Spectrum, and take a deep dive into its architecture and inner workings. We share many of the recent availability, performance, and management enhancements and how they improve your end user experience. You also hear from 21st Century Fox, who presents a case study of their fast migration from an on-premises data warehouse to Amazon Redshift. Learn how they are expanding their data warehouse to a data lake that encompasses multiple data sources and data formats. This architecture helps them tie together siloed business units and get actionable 360-degree insights across their consumer base.
ABD206-Building Visualizations and Dashboards with Amazon QuickSightAmazon Web Services
Just as a picture is worth a thousand words, a visual is worth a thousand data points. A key aspect of our ability to gain insights from our data is to look for patterns, and these patterns are often not evident when we simply look at data in tables. The right visualization will help you gain a deeper understanding in a much quicker timeframe. In this session, we will show you how to quickly and easily visualize your data using Amazon QuickSight. We will show you how you can connect to data sources, generate custom metrics and calculations, create comprehensive business dashboards with various chart types, and setup filters and drill downs to slice and dice the data.
Companies, from startups to enterprises across the globe, are looking to migrate data warehousing to the cloud to increase performance and lower costs. Data engineers, data analysts, and developers also need to access and consume this important data. The landscape is constantly evolving and there are many solutions available for enterprises of all sizes. In this workshop, we dive deep into architectural patterns, use cases, and best practices when designing an enterprise data warehouse in the cloud. We also address key issues such as data governance and democratization. At the end of this workshop, you’ll be equipped to design and implement a cloud enterprise data warehouse platform that provides the most benefit for your enterprise, data consumers, and customers.
Over 90% of today’s data was generated in the last 2 years, and the rate of data growth isn’t slowing down. In this session, we’ll step through the challenges and best practices on how to capture all the data that is being generated, understand what data you have, and start driving insights and even predict the future using purpose built AWS Services. We’ll frame the session and demonstrations around common pitfalls of building Data Lakes and how to successful drive analytics and insights from the data. This session will focus on the architecture patterns bringing together key AWS Services and rather than a deep dive on any single service. We’ll show how services such as Amazon S3, Amazon Glue, AWS Glue, Amazon Redshift, Amazon Athena, Amazon EMR, and Amazon Kinesis, and Amazon Machine Learning services are put together to build a successful data lake for various role including both data scientists and business users.
How TrueCar Gains Actionable Insights with Splunk Cloud PPTAmazon Web Services
The vast amount of big data that today’s companies generate makes it difficult to separate the signal from the noise. Organizations need to derive meaningful insights into operations and business to take action. TrueCar needed a better way to manage, search, and analyze their hybrid environment. In this webinar, you’ll learn how TrueCar centralized all of their data in one place using Amazon Kinesis and Splunk Cloud, gaining deep visibility, scalability, and the ability to monitor and troubleshoot operational issues – all while migrating to AWS.
This session will highlight the most impactful announcements made at AWS re:Invent 2017 while giving you ideas on key use cases for new services and features. We’ll cover major themes of the conference, the new services and features within those themes and how they work together to make it faster and easier to build functionality into your app.
Come see first-hand how Amazon EC2 Systems Manager can help you manage your servers at scale with the agility and security you need in today's dynamic cloud-enabled world. To be truly agile, you need a way to define and track system configurations, prevent drift, and maintain software compliance. At the same time, you need to collect software inventory, apply OS patches, automate your system image maintenance, and configure anything in the OSs of your EC2 instances and on-premises servers. Amazon EC2 Systems Manager does all of that and more for both Linux and Windows systems. In this session, learn about the seven services that make up Amazon EC2 Systems Manager and see them in action. No matter if you are managing 10 or 10,000 instances, see how you can manage your systems, increasing your agility and security with EC2 Systems Manager.
AMF302-Alexa Wheres My Car A Test Drive of the AWS Connected Car Reference.pdfAmazon Web Services
Today's trends in auto technology are all about connecting cars and their occupants to the outside world in a seamless and safe manner. In this session, we discuss how automotive companies are leveraging AWS for a variety of connected vehicle use cases. You'll leave this session with source code, architecture diagrams, and an understanding of how to apply the AWS Connected Vehicle Reference Architecture to build your own prototypes. You'll also learn how car companies can leverage Amazon services such as Alexa and AWS services such as AWS IOT, AWS Greengrass, AWS Lambda and Amazon API Gateway to rapidly develop and deploy innovative connected vehicle services.
FSV307-Capital Markets Discovery How FINRA Runs Trade Analytics and Surveilla...Amazon Web Services
FINRA’s analytics platform unlocks the value in capital markets data by accelerating trade analytics and providing a foundation for machine learning at scale. The platform enables FINRA’s analysts to perform discovery on petabytes of trade data to identify instances of potential fraud, market manipulation, and insider trading. By centralizing all data in S3, FINRA’s architecture offers improved agility, scalability, and cost effectiveness. Analytics services such as Amazon EMR and Amazon Redshift have freed FINRA’s data scientists from the constraints of desktop tools, allowing them to apply machine learning techniques to develop and test new surveillance patterns. All of this is done while meeting FINRA’s security and compliance responsibilities as a financial regulator. At the end of this session, you’ll have an understanding of how to apply FINRA’s architecture to trade analytics and other financial services use cases, including meeting regulatory requirements such as the Consolidated Audit Trail (CAT) reporting.
講師: Jhen-Wei Huang, Solutions Architect, Amazon Web Services
Do your customers love you?
Recommendations and Voice of Customer
Analytics and AI with AWS
AWS Summit Singapore - Artificial Intelligence to Delight Your CustomersAmazon Web Services
Andrew Watts-Curnow, Senior Cloud Architect – Professional Services, APAC, AWS
Learn how advances in AI are enabling improvements in customer experience. This is a deep dive using machine learning frameworks for people who are familiar with building their own models. In this session, we will detail a facial recognition solution that can detect known customers and alert customer service staff.
In this session, we discuss the evolution of database and analytics services in AWS, the new database and analytics services and features we launched this year, and our vision for continued innovation in this space. We are witnessing an unprecedented growth in the amount of data collected, in many different forms. Storage, management, and analysis of this data require database services that scale and perform in ways not possible before. AWS offers a collection of database and other data services—including Amazon Aurora, Amazon DynamoDB, Amazon RDS, Amazon Redshift, Amazon ElastiCache, Amazon Kinesis, and Amazon EMR—to process, store, manage, and analyze data. In this session, we provide an overview of AWS database and analytics services and discuss how customers are using these services today.
Mai-Lan Tomsen Bukovec, Vice President and General Manager Amazon S3.
Axel Winter, Group CTO, Central Group
George Wang, SVP IT, Singapore Airlines
Akshay Garg, CEO, FinAccel
Join builders, dreamers and innovators across South East Asia for the 2018 AWS Summit Singapore Keynote.
Watch live as Mai-Lan Tomsen Bukovec, Vice President and General Manager Amazon S3, together with AWS Customers Singapore Airlines, Central Group and FinAccel share how to build for tomorrow’s world.
Unlocking New Todays - Artificial Intelligence and Data Platforms on AWSAmazon Web Services
熱門創新服務專題
Unlocking New Todays: Artificial Intelligence and Data Platforms on AWS (Level 200)
Speaker: Paul Yung, Head of Territory Development HKT, AWS
Migrating your traditional Data Warehouse to a Modern Data LakeAmazon Web Services
In this session, we discuss the latest features of Amazon Redshift and Redshift Spectrum, and take a deep dive into its architecture and inner workings. We share many of the recent availability, performance, and management enhancements and how they improve your end user experience. You also hear from 21st Century Fox, who presents a case study of their fast migration from an on-premises data warehouse to Amazon Redshift. Learn how they are expanding their data warehouse to a data lake that encompasses multiple data sources and data formats. This architecture helps them tie together siloed business units and get actionable 360-degree insights across their consumer base.
ABD206-Building Visualizations and Dashboards with Amazon QuickSightAmazon Web Services
Just as a picture is worth a thousand words, a visual is worth a thousand data points. A key aspect of our ability to gain insights from our data is to look for patterns, and these patterns are often not evident when we simply look at data in tables. The right visualization will help you gain a deeper understanding in a much quicker timeframe. In this session, we will show you how to quickly and easily visualize your data using Amazon QuickSight. We will show you how you can connect to data sources, generate custom metrics and calculations, create comprehensive business dashboards with various chart types, and setup filters and drill downs to slice and dice the data.
Companies, from startups to enterprises across the globe, are looking to migrate data warehousing to the cloud to increase performance and lower costs. Data engineers, data analysts, and developers also need to access and consume this important data. The landscape is constantly evolving and there are many solutions available for enterprises of all sizes. In this workshop, we dive deep into architectural patterns, use cases, and best practices when designing an enterprise data warehouse in the cloud. We also address key issues such as data governance and democratization. At the end of this workshop, you’ll be equipped to design and implement a cloud enterprise data warehouse platform that provides the most benefit for your enterprise, data consumers, and customers.
Over 90% of today’s data was generated in the last 2 years, and the rate of data growth isn’t slowing down. In this session, we’ll step through the challenges and best practices on how to capture all the data that is being generated, understand what data you have, and start driving insights and even predict the future using purpose built AWS Services. We’ll frame the session and demonstrations around common pitfalls of building Data Lakes and how to successful drive analytics and insights from the data. This session will focus on the architecture patterns bringing together key AWS Services and rather than a deep dive on any single service. We’ll show how services such as Amazon S3, Amazon Glue, AWS Glue, Amazon Redshift, Amazon Athena, Amazon EMR, and Amazon Kinesis, and Amazon Machine Learning services are put together to build a successful data lake for various role including both data scientists and business users.
How TrueCar Gains Actionable Insights with Splunk Cloud PPTAmazon Web Services
The vast amount of big data that today’s companies generate makes it difficult to separate the signal from the noise. Organizations need to derive meaningful insights into operations and business to take action. TrueCar needed a better way to manage, search, and analyze their hybrid environment. In this webinar, you’ll learn how TrueCar centralized all of their data in one place using Amazon Kinesis and Splunk Cloud, gaining deep visibility, scalability, and the ability to monitor and troubleshoot operational issues – all while migrating to AWS.
This session will highlight the most impactful announcements made at AWS re:Invent 2017 while giving you ideas on key use cases for new services and features. We’ll cover major themes of the conference, the new services and features within those themes and how they work together to make it faster and easier to build functionality into your app.
Come see first-hand how Amazon EC2 Systems Manager can help you manage your servers at scale with the agility and security you need in today's dynamic cloud-enabled world. To be truly agile, you need a way to define and track system configurations, prevent drift, and maintain software compliance. At the same time, you need to collect software inventory, apply OS patches, automate your system image maintenance, and configure anything in the OSs of your EC2 instances and on-premises servers. Amazon EC2 Systems Manager does all of that and more for both Linux and Windows systems. In this session, learn about the seven services that make up Amazon EC2 Systems Manager and see them in action. No matter if you are managing 10 or 10,000 instances, see how you can manage your systems, increasing your agility and security with EC2 Systems Manager.
AMF302-Alexa Wheres My Car A Test Drive of the AWS Connected Car Reference.pdfAmazon Web Services
Today's trends in auto technology are all about connecting cars and their occupants to the outside world in a seamless and safe manner. In this session, we discuss how automotive companies are leveraging AWS for a variety of connected vehicle use cases. You'll leave this session with source code, architecture diagrams, and an understanding of how to apply the AWS Connected Vehicle Reference Architecture to build your own prototypes. You'll also learn how car companies can leverage Amazon services such as Alexa and AWS services such as AWS IOT, AWS Greengrass, AWS Lambda and Amazon API Gateway to rapidly develop and deploy innovative connected vehicle services.
FSV307-Capital Markets Discovery How FINRA Runs Trade Analytics and Surveilla...Amazon Web Services
FINRA’s analytics platform unlocks the value in capital markets data by accelerating trade analytics and providing a foundation for machine learning at scale. The platform enables FINRA’s analysts to perform discovery on petabytes of trade data to identify instances of potential fraud, market manipulation, and insider trading. By centralizing all data in S3, FINRA’s architecture offers improved agility, scalability, and cost effectiveness. Analytics services such as Amazon EMR and Amazon Redshift have freed FINRA’s data scientists from the constraints of desktop tools, allowing them to apply machine learning techniques to develop and test new surveillance patterns. All of this is done while meeting FINRA’s security and compliance responsibilities as a financial regulator. At the end of this session, you’ll have an understanding of how to apply FINRA’s architecture to trade analytics and other financial services use cases, including meeting regulatory requirements such as the Consolidated Audit Trail (CAT) reporting.
講師: Jhen-Wei Huang, Solutions Architect, Amazon Web Services
Do your customers love you?
Recommendations and Voice of Customer
Analytics and AI with AWS
AWS Summit Singapore - Artificial Intelligence to Delight Your CustomersAmazon Web Services
Andrew Watts-Curnow, Senior Cloud Architect – Professional Services, APAC, AWS
Learn how advances in AI are enabling improvements in customer experience. This is a deep dive using machine learning frameworks for people who are familiar with building their own models. In this session, we will detail a facial recognition solution that can detect known customers and alert customer service staff.
Driving AI Innovation with Machine Learning powered by AWS. AI is opening up new insights and efficiencies in enterprises of every industry. Learn how enterprises are using AWS’ machine learning capabilities combined with its deep storage, compute, analytics, and security services to deliver intelligent applications today. Strategies to develop ML expertise within your org will also be discussed.
AWS Summit Singapore 2019 | Opening Keynote with Peter DeSantisAWS Summits
Speaker: Peter DeSantis, Vice President, AWS Global Infrastructure and Customer Support.
AWS Customers:
Certis – Chua Chwee Koh, Chief Group Technology & Operations
Traveloka – Sergei Shvetsov, Head of Engineering
Data Summer Conf 2018, “Build, train, and deploy machine learning models at s...Provectus
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 is a fully-managed service that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. Apache MXNet and TensorFlow are pre-installed, and Amazon SageMaker offers a range of built-in, high-performance machine learning algorithms. If you want to train with an alternative framework or algorithm, you can bring your own in a Docker container.
DataPalooza at the San Francisco Loft: In this workshop you will use AWS and Intel technologies to learn how to build, deploy, and run ML inference on the cloud as well as on the IoT Edge. You will learn to use Amazon SageMaker with Intel C5 Instances, AWS DeepLens, AWS Greengrass, Amazon Rekognition, and AWS Lambda to build an end-to-end IoT solution that performs machine learning.
Cost Optimization for Microsoft Workloads on AWS - AWS Transformation Day: Sa...Amazon Web Services
Enterprises around the world are driving growth through innovation when they run Windows based solutions on the leading cloud platform. In addition, enterprises can significantly reduce total cost of ownership and optimize their costs when they choose AWS to host legacy and 3rd party Microsoft applications optimized for Windows Server and SQL Server by taking advantage of our cutting-edge infrastructure, flexible pricing options and licensing solutions. AWS also offers solutions and programs that empower .NET developers to leverage their skills and tools to continue developing cutting edge solutions. So, whether you are migrating a small application or considering divesting an entire datacenter, AWS can scale and support hosting of Windows solutions that help you run your business today.
About the Event:
AWS Transformation Day is designed for enterprise organizations migrating to the cloud to become more responsive, agile and innovative, while staying secure and compliant. Join us for this one-day event and we’ll share our experiences of helping enterprise customers accelerate the pace of migration and adoption of strategic services.
Who should attend?
This event is recommended for IT and business leaders who are looking to create sustainable benefits and a competitive advantage by using the AWS Cloud. CIOs, CTOs, CISOs, CDOs, CFOs, IT leaders and IT professionals, enterprise developers, business decision makers, and finance executives.
by Mahendra Bairagi, AI Specialist Solutions Architect, AWS
As the CTO of a new startup, you have taken up a challenge of improving the EDM music festival experience. At venues with multiple stages, festival-goers are always looking to identify DJ stage areas with the liveliest atmosphere. This causes them to constantly move around between different stages and miss out on having fun. You are looking to use Machine Learning and IoT technologies to solve this unique problem.
Do you accept the Challenge?
The objective of this task is to help the festival-goers quickly identify the DJ stage where crowd is the happiest. You've seen a lot of buzz around computer vision, machine learning, and IoT and want to use this technology to detect crowd emotions. From your initial research there are existing ML models that you can leverage to do face and emotion detection, but there are two ways that the predictions (inference) can be done; on the cloud and on the camera itself, but which one will work the best for your needs at the festival? You are going to test both approaches and find out!
In this workshop you will use AWS and Intel technologies to learn how to build, deploy, and run ML inference on the cloud as well as on the IoT Edge. You will learn to use Amazon SageMaker with Intel C5 Instances, AWS DeepLens, AWS Greengrass, Amazon Rekognition, and AWS Lambda to build an end-to-end IoT solution that performs machine learning.
Microsoft & Machine Learning / Artificial Intelligenceİbrahim KIVANÇ
In this presentation you'll find Machine Learning / Deep Learning tools and services from Microsoft. Including Azure Machine Learning Workbench, Azure Notebooks, Azure Data Science Virtual Machines and more.
Here are the demos & resources
https://github.com/ikivanc/Azure-ML-Workbench-Iris-Dataset-Classification
https://github.com/ikivanc/Azure-ML-Resources
Understand the core concepts of “Cloud Computing” and how businesses around the world are running the infrastructure that supports their websites to lower costs, improve time-to-market, and enable rapid scalability matching resource to demands of users. Whether you are an enterprise looking for IT innovation, agility and resiliency or small and medium business who wants to accelerate growth without a big upfront investment in cash or time for technology, the AWS Cloud provides a complete set of services at zero upfront costs which are available with a few clicks and within minutes.
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.
1. W E L C O M E
AWS re:Invent 2 0 1 7 Recap
Solutions Updates
D e a n S a m u e l s
H e a d o f S o l u t i o n s A r c h i t e c t u r e
H o n g K o n g & Ta i w a n
A m a z o n W e b S e r v i c e s
6. CORE SERVICES
Integrated Networking
Rules Engine
Device Shadows
Device SDKs
Device Gateway
Registry
Local Compute
Custom Model
Training & Hosting
Conversational Chatbots
Virtual Desktops
App Streaming
Schema Conversion
Image & Scene
Recognition
Sharing &
Collaboration
Exabyte-Scale
Data Migration
Text to Speech
Corporate Email Application Migration
Database Migration
Regions
Availability Zones
Points of Presence
Data Warehousing
Business Intelligence
Elasticsearch
Hadoop/Spark
Data Pipelines
Streaming Data
Collection
ETL
Streaming Data
Analysis
Interactive SQL
Queries
Queuing & Notifications
Workflow
Email
Transcoding
Deep Learning
(Apache MXNet,
TensorFlow, & others)
Server MigrationCommunications
MARKETPLACE
Business Apps Business Intelligence DevOps Tools Security Networking StorageDatabases
API Gateway
Single Integrated
Console
Identity
Sync
Mobile Analytics
Mobile App Testing
Targeted Push
Notifications
One-click App
Deployment
DevOps Resource
Management
Application Lifecycle
Management
Containers
Triggers
Resource Templates
Build & Test
Analyze & Debug
Identity
Management
Key Management
& Storage
Monitoring &
Logs
Configuration
Compliance
Web Application
Firewall
Assessment
& Reporting
Resource & Usage
Auditing
Access Control
Account
Grouping
DDOS
Protection
TECHNICAL & BUSINESS SUPPORT
Support
Professional
Services
Optimization
Guidance
Partner
Ecosystem
Training &
Certification
Solutions Management Account Management
Security & Billing
Reports
Personalized
Dashboard
Monitoring
Manage
Resources
Data Integration
Integrated Identity &
Access
Integrated Resource &
Deployment Management
Integrated Devices
& Edge Systems
Resource
Templates
Configuration
Tracking
Server
Management
Service
Catalogue
Search
MIGRATIONHYBRID ARCHITECTUREENTERPRISE APPSMACHINE LEARNINGIoTMOBILE SERVICESDEV OPSANALYTICS
APP SERVICES
INFRASTRUCTURE SECURITY & COMPLIANCE MANAGEMENT TOOLS
Compute
VMs, Auto-scaling, Load
Balancing, Containers,
Virtual Private Servers,
Batch Computing, Cloud
Functions, Elastic GPUs,
Edge Computing
Storage
Object, Blocks, File, Archivals,
Import/Export, Exabyte-scale
data transfer
CDN
Databases
Relational, NoSQL,
Caching, Migration,
PostgreSQL compatible
Networking
VPC, DX, DNS
Facial Recognition &
Analysis
Facial Search
Patching
Contact Center
M O S T R O B U S T , F U L L Y F E A T U R E D T E C H N O L O G Y I N F R A S T R U C T U R E P L A T F O R M
7. 516
24 48 61 82
159
280
722
1,017
LAUNCHES
2008 2009 2010 2011 2012 2013 2014 2015 2016
1,300+
2017
New capabilities daily
P A C E O F I N N O V A T I O N
9. B R O A D E S T S P E C T R U M O F C O M P U T E I N S T A N C E S
Burstable
T 2
Big Data
Optimized
H 1
Memory
Optimized
R 4
In-memory
X 1
High
I/O
I 3
Compute
Intensive
C 5
Graphics
Intensive
G 3
General
Purpose
GPU
P 3
Memory
Intensive
X 1 e
General
Purpose
M 5
Vir t ual
Pr ivat e
Ser ver s
Bare Metal
High I/O
I 3 m
Dense
Storage
D 2 F 1
FPGA
Amazon
Lightsail
EC2 Elastic GPUs
Graphics acceleration for
EC2 instances
EC2 Spot Instances
• Hibernation
• No Bid Pricing
N E
W !
NEW! NEW! NEW!
NEW!
11. Service integrations are at
the container level
Scales to support clusters
and applications of any size
Integration with entire
AWS platform
1 2 3
ALB, Auto Scaling, Batch, Elastic Beanstalk,
CloudFormation, CloudTrail, CloudWatch Events,
CloudWatch Logs, CloudWatch Metrics, ECR, EC2 Spot,
IAM, NLB, Parameter Store, and VPC
Why customers love ECS
AMAZON ELASTIC CONTAINER SERVICE (ECS)
The easiest way to deploy and manage containers
13. Managed Kubernetes on AWS
Amazon Elastic Container
Service for Kubernetes (EKS)
Available in preview today
NEW!
14. A M A Z O N E L A S T I C C O N T A I N E R S E R V I C E F O R K U B E R N E T E S ( E K S )
Hybrid cloud
compatible
Highly
available
Automated
upgrades and
patches
Integrated with
AWS Services
CloudTrail, CloudWatch, ELB,
IAM, VPC, PrivateLink
15. … B U T W H A T E L S E ?
M A N A G E D C L U S T E R S A R E G R E A T …
16. Run containers without managing
servers or clusters
AWS Fargate
Available for ECS today
Available for EKS in 2018
NEW!
17. A W S F A R G A T E
No clusters
to manage
Manages underlying
infrastructure
Easy to run,
easy to scale
Run containers on ECS and EKS without managing servers
19. A W S L A M B D A I S E V E R Y W H E R E
Event-driven services Event sources Lambda inside
AWS Lambda Amazon S3 Amazon CloudFormation AWS IoT
Amazon API Gateway Amazon DynamoDB Amazon CloudWatch Logs AWS IoT Button
AWS Step Functions Amazon Kinesis Streams Amazon CloudWatch Events AWS Greengrass
AWS X-Ray Amazon Kinesis Firehose AWS CodeCommit AWS Snowball Edge
Amazon SNS AWS Config AWS Lambda@Edge
Amazon SES Amazon Lex
Amazon Cognito Amazon CloudFront
AWS IoT
AWS Lambda
20. E V O L U T I O N O F C O M P U T E
AWS
CodeDeploy
AWS
CloudTrail
AWS Config
and Config
Rules
AWS IAM
AWS
PrivateLink
Managed NAT
Gateway
VMware Cloud
on AWS
Group
Resource
Tagging
21. Everything is ... a CLOUD!
Everything is … BIG DATA!
Everything is … MACHINE LEARNING!
A L O T O F M L B U Z Z O U T T H E R E T O D A Y
22. A L O N G H E R I T A G E O F M A C H I N E L E A R N I N G A T A M A Z O N
Personalized
recommendations
Inventing entirely
new customer
experiences
Fulfillment
automation and
inventory
management
Drones Voice driven
interactions
26. B O T T O M L A Y E R : F R A M E W O R K S A N D I N T E R F A C E S
P2
NVIDIA
Tesla V100
GPUs
P3
1 Petaflop of compute
NVLink 2.0
5,120 Tensor cores
128GB of memory
~14X faster than P2
P3 Instance AWS Deep Learning AMI
27. L I S T E N T O A N D I N V E N T F O R C U S T O M E R S
Frameworks
KERAS
Interfaces
29. ML IS STILL TOO COMPLICATED FOR EVERYDAY DEVELOPERS
Collect and prepare
training data
Choose and
optimize your ML
algorithm
Set up and manage
environments for
training
Train and tune model
(trial and error)
Deploy model
in production
Scale and manage
the production
environment
30. D E V E L O P E R S T H R O W U P T H E I R H A N D S I N F R U S T R A T I O N
31. Easily build, train, and deploy machine
learning models
Amazon SageMaker
Generally available today
NEW!
32. A M A Z O N S A G E M A K E R S O L V E S A L L O F T H E S E P R O B L E M S
Collect and prepare
training data
Choose and
optimize your ML
algorithm
Set up and manage
environments for
training
Deploy model
in production
Scale and manage
the production
environment
Train and tune model
(trial and error)
33. A M A Z O N S A G E M A K E R
Pre-built
notebooks for
common
problems
BUILD
Choose and
optimize your ML
algorithm
Set up and manage
environments for
training
Train and tune model
(trial and error)
Deploy model
in production
Scale and manage
the production
environment
34. A M A Z O N S A G E M A K E R
Pre-built
notebooks for
common
problems
K-Means Clustering
Principal Component Analysis
Neural Topic Modelling
Factorization Machines
Linear Learner - Regression
XGBoost
Latent Dirichlet Allocation
Image Classification
Seq2Seq
Linear Learner - Classification
ALGORITHMS
Apache MXNet
TensorFlow
Caffe2, CNTK,
PyTorch, Torch
FRAMEWORKS
Set up and manage
environments for
training
Train and tune
model (trial and
error)
Deploy model
in production
Scale and manage the
production environment
Built-in, high
performance
algorithms
BUILD
36. A M A Z O N S A G E M A K E R
Pre-built
notebooks for
common
problems
Built-in, high
performance
algorithms
One-click
training
BUILD TRAIN
Train and tune model
(trial and error)
Deploy model
in production
Scale and manage
the production
environment
37. A M A Z O N S A G E M A K E R
Pre-built
notebooks for
common
problems
Built-in, high
performance
algorithms
One-click
training
Hyperparameter
optimization
BUILD TRAIN
Deploy model
in production
Scale and manage
the production
environment
38. A M A Z O N S A G E M A K E R
Pre-built
notebooks for
common
problems
Built-in, high
performance
algorithms
One-click
deployment
One-click
training
Hyperparameter
optimization
Scale and manage
the production
environment
BUILD TRAIN DEPLOY
39. A M A Z O N S A G E M A K E R
Fully managed
hosting with auto-
scaling
One-click
deployment
Pre-built
notebooks for
common
problems
Built-in, high
performance
algorithms
One-click
training
Hyperparameter
optimization
BUILD TRAIN DEPLOY
40. CAN WE DO MORE TO PUT ML IN
THE HANDS OF ALL DEVELOPERS
(LITERALLY)?
41. AWS DeepLens
The world’s first wireless, deep learning
enabled video camera for developers
A v a i l a b l e o n a m a z o n . c o m n e x t y e a r
NEW!
42. W H A T I S A W S D E E P L E N S ?
HD video camera
Custom-designed
deep learning
inference engine
Micro-SD
Mini-HDMI
USB
USB
Reset
Audio out
Power
HD video camera
with on-board
compute optimized
for deep learning
Tutorials, examples,
demos, and pre-built
models
From unboxing to
first inference in
<10 minutes
Integrates with Amazon
SageMaker and AWS
Lambda
10
MIN
44. Search, analyze, and organize millions of images
A M A Z O N R E K O G N I T I O N
Objects and scenes
Facial analysis and recognition
Inappropriate content detection
Celebrity recognition
Image in text recognition
A M A Z O N R E K O G N I T I O N
45. Real-time and batch video analytics
Amazon
Rekognition Video
Generally available
today
NEW!
46. A M A Z O N R E K O G N I T I O N V I D E O
A M A Z O N R E K O G N I T I O N
V I D E O
Video in. People, activities, and details out.
Objects and scenes
Facial analysis and recognition
Inappropriate content detection
Celebrity recognition
Person tracking
47. A M A Z O N R E K O G N I T I O N V I D E O
Easy to use Batch processing Processes
real-time video
Continually trained Low costTimestamp
generation
48. V I S I O N LAN GU AGE
A P P L I C A T I O N S E R V I C E S
Amazon Rekognition Image Amazon Polly Amazon LexAmazon Rekognition Video
49. S T R E A M I N G D A T A F R O M C O N N E C T E D D E V I C E S
50. Securely ingest and store video, audio, and
other time-encoded data
Amazon Kinesis
Video Streams
Generally available today
NEW!