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.
Building prediction models with Amazon Redshift and Amazon Machine Learning -...Amazon Web Services
Mining data with Redshift, using this data to build a prediction model with Amazon ML, performing batch predictions & real-time predictions (with a Java app).
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.
Building prediction models with Amazon Redshift and Amazon Machine Learning -...Amazon Web Services
Mining data with Redshift, using this data to build a prediction model with Amazon ML, performing batch predictions & real-time predictions (with a Java app).
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.
As serverless architectures become more popular, customers need a framework of patterns to help them identify how they can leverage AWS to deploy their workloads without managing servers or operating systems. This session describes re-usable serverless patterns while considering costs. For each pattern, we provide operational and security best practices and discuss potential pitfalls and nuances. We also discuss the considerations for moving an existing server-based workload to a serverless architecture. The patterns use services like AWS Lambda, Amazon API Gateway, Amazon Kinesis Streams, Amazon Kinesis Analytics, Amazon DynamoDB, Amazon S3, AWS Step Functions, AWS Config, AWS X-Ray, and Amazon Athena. This session can help you recognize candidates for serverless architectures in your own organizations and understand areas of potential savings and increased agility. What’s new in 2017: using X-Ray in Lambda for tracing and operational insight; a pattern on high performance computing (HPC) using Lambda at scale; how a query can be achieved using Athena; Step Functions as a way to handle orchestration for both the Automation and Batch patterns; a pattern for Security Automation using AWS Config rules to detect and automatically remediate violations of security standards; how to validate API parameters in API Gateway to protect your API back-ends; and a solid focus on CI/CD development pipelines for serverless –that includes testing, deploying, and versioning (SAM tools).
This session will introduce you to Amazon Rekognition, a service that makes it easy to add deep learning image analysis to your applications. Amazon Rekognition is based on the same proven, highly scalable, deep learning technology developed by Amazon’s
BDA306 An Introduction to Amazon Lex, your Service for Building Voice and Tex...Amazon Web Services
Amazon Lex is a service for building conversational interfaces into any application using voice and text. With Amazon Lex, the same deep learning technologies that power Amazon Alexa are now available to any developer, enabling you to quickly and easily build sophisticated, natural language conversational chatbots. No deep learning experience is required to immediately start creating chatbots that understand voice or text, to ask questions, get answers, and complete sophisticated tasks. Lex enables you to easily publish your chatbots to mobile devices, web apps, services, and platforms such as Facebook Messenger, Twilio and Slack. This session will go over the features available with Amazon Lex and how they can be used to build and deploy chatbots. Join us for this introductory presentation and learn more about Amazon Lex!
by Rohan Dubal, Software Development Engineer, AWS
One of the biggest time sinks and challenges for mobile application developers is developing, accessing, and managing all of the disparate data sources that are involved in delivering delightful, collaborative, and real-time mobile experiences for users while also enabling offline capabilities for when a user is not connected, but still wants to use the app. In this session, you be introduced to the new AWS AppSync service that speed and simplifies these tasks for developers using GraphQL to provide a data abstraction layer and easy query and update statements without having to know the details of the underlying data sources.
ABD322_Implementing a Flight Simulator Interface Using AI, Virtual Reality, a...Amazon Web Services
This workshop explores the technology options, architectures, and implementations associated with instrumenting AR, VR, and simulated worlds. Using flight simulation as the primary use case, you learn to consume, process, store, and analyze high velocity telemetry as well as exploring control plane implementations using AWS IoT, AWS Lambda, Amazon Kinesis, and Amazon SNS. This is a hands-on workshop and you need a laptop (tablets are not suitable). You should have a solid understanding of AWS products and Node.js.
Supercharge your Machine Learning Solutions with Amazon SageMakerAmazon Web Services
Amazon SageMaker is a fully-managed service that enables data scientists and developers to quickly and easily build, train, and deploy machine learning models, at scale. This session will introduce you the features of Amazon SageMaker, including a one-click training environment, highly-optimized machine learning algorithms with built-in model tuning, and deployment without engineering effort. With zero-setup required, Amazon SageMaker significantly decreases your training time and overall cost of building production machine learning systems. You'll also hear how and why Intuit is using Amazon SageMaker on AWS for real-time fraud detection.
Amazon Machine Learning (Amazon ML) is a service that makes it easy for developers of all skill levels to use machine learning technology. Amazon Machine Learning’s powerful algorithms create machine learning models by finding patterns in your existing data. The service uses these models to process new data and generate predictions for your application. In this session, we will show you how to use machine learning with the data you already have to arrive at accurate and actionable predictions - to create smart applications. You will learn how to use and integrate Amazon ML into your applications to take advantage of predictive analysis in the cloud.
"Serverless architectures let you build and deploy applications and services with infrastructure resources that require zero administration. In the past, you had to provision and scale servers to run your application code, install and operate distributed databases, and build and run custom software to handle API requests. Now, AWS provides a stack of scalable, fully-managed services that eliminates these operational complexities. In this session, you will learn about serverless architectures, their benefits, and the basics of the AWS’s serverless stack (e.g., AWS Lambda, Amazon API Gateway, and AWS Step Functions).
We will discuss how to use serverless architectures for a variety of use cases including data processing, website backends, serverless applications, and “operational glue.” You will also get practical tips and tricks, best practices, and architecture patterns that you can take back and implement immediately."
LFS301-SAGE Bionetworks, Digital Mammography DREAM Challenge and How AWS Enab...Amazon Web Services
DREAM Challenges pose fundamental questions about systems biology and translational medicine. Designed and run by a community of researchers from a variety of organizations, the challenges invite participants to propose solutions, fostering collaboration and building communities in the process. The Sage Bionetworks Synapse platform, which powers many research consortiums including the DREAM Challenges, are starting to put into practice model cloud-initiatives that not only provide impactful discoveries in the areas of neuroscience, infectious disease, and cancer, but are also revolutionizing scientific research by enabling an interactive consortium science platform. In this session, you learn how to build a "consortium model" of research in order to connect research organizations with non-profit organizations, technology companies, biotechnology, and pharmaceutical companies. You can also learn about how to leverage machine learning, Amazon ECS, and R for consortium-based science initiatives.
SRV301-Optimizing Serverless Application Data Tiers with Amazon DynamoDBAmazon Web Services
"As a fully managed database service, Amazon DynamoDB is a natural fit for serverless architectures. In this session, we dive deep into why and how to use DynamoDB in serverless applications, followed by a real-world use case from CapitalOne.
First, we dive into the relevant DynamoDB features, and how you can use it effectively with AWS Lambda in solutions ranging from web applications to real-time data processing. We show how some of the new features in DynamoDB, such as Auto Scaling and Time to Live (TTL), are particularly useful in serverless architectures, and distill the best practices to help you create effective serverless applications. In the second part, we talk about how CapitalOne migrated billions of transactions to a completely serverless architecture and built a scalable, resilient and fast transaction platform by leveraging DynamoDB, AWS Lambda and other services within the serverless ecosystem."
Amazon Lex is a service for building conversational interfaces into any application using voice and text, and Amazon Polly is a service that turns text into lifelike speech. This workshop combines both of these AWS services in an interactive session during which attendees build service and informational chatbots that feature spoken-voice interfaces. These modules provide foundational skills for any developer looking to enrich their applications with natural, conversational interfaces.
Amazon Elastic Compute Cloud (Amazon EC2) provides a broad selection of instance types to accommodate a diverse mix of workloads. In this technical session, we provide an overview of the Amazon EC2 instance platform, key platform features, and the concept of instance generations. We dive into the current-generation design choices of the different instance families, including the General Purpose, Compute Optimized, Storage Optimized, Memory Optimized, and GPU instance families. We also detail best practices and share performance tips for getting the most out of your Amazon EC2 instances.
Title:
Auto Scaling in Amazon DynamoDB - August 2017 AWS Online Tech Talks
Learning Objectives:
- Get an overview of DynamoDB Auto Scaling and how it works - Learn about the key benefits of using Auto Scaling in terms of application availability and costs reduction
- Understand best practices for using Auto Scaling and its configuration settings
Recently, DynamoDB announced Auto Scaling, to help automate capacity management for tables and global secondary indexes. Previously, customers had to manually provision read and write capacity based on anticipated application demands. This could result in underprovisioning or overprovisioning capacity. Underprovisioning could slow down application performance, and overprovisioning could result in underutilized resources and higher costs. With DynamoDB Auto Scaling, you simply set your desired throughput utilization target (within your desired minimum and maximum limits), and auto scaling takes care of the rest. In this webinar, we will share how Auto Scaling works in DynamoDB, show how you can enable Auto Scaling with just a few clicks in the AWS Management Console and manage programmatically using the AWS Command Line Interface and the AWS Software Development Kits.
AWS re:Invent 2016: Machine Learning State of the Union Mini Con (MAC206)Amazon Web Services
With the growing number of business cases for artificial intelligence (AI), machine learning (ML) and deep learning (DL) continue to drive the development of cutting edge technology solutions. We see this manifested in computer vision, predictive modeling, natural language understanding, and recommendation engines. During this full afternoon of sessions and workshops, learn how you can develop your own applications to leverage the benefits of these services. Join this State of the Union presentation to hear more about ML and DL at AWS and see how Motorola Solutions is leveraging these state-of-the-art technologies to solve public safety challenges, and how Ohio Health intends to inject AI into the medical system.
As serverless architectures become more popular, customers need a framework of patterns to help them identify how they can leverage AWS to deploy their workloads without managing servers or operating systems. This session describes re-usable serverless patterns while considering costs. For each pattern, we provide operational and security best practices and discuss potential pitfalls and nuances. We also discuss the considerations for moving an existing server-based workload to a serverless architecture. The patterns use services like AWS Lambda, Amazon API Gateway, Amazon Kinesis Streams, Amazon Kinesis Analytics, Amazon DynamoDB, Amazon S3, AWS Step Functions, AWS Config, AWS X-Ray, and Amazon Athena. This session can help you recognize candidates for serverless architectures in your own organizations and understand areas of potential savings and increased agility. What’s new in 2017: using X-Ray in Lambda for tracing and operational insight; a pattern on high performance computing (HPC) using Lambda at scale; how a query can be achieved using Athena; Step Functions as a way to handle orchestration for both the Automation and Batch patterns; a pattern for Security Automation using AWS Config rules to detect and automatically remediate violations of security standards; how to validate API parameters in API Gateway to protect your API back-ends; and a solid focus on CI/CD development pipelines for serverless –that includes testing, deploying, and versioning (SAM tools).
This session will introduce you to Amazon Rekognition, a service that makes it easy to add deep learning image analysis to your applications. Amazon Rekognition is based on the same proven, highly scalable, deep learning technology developed by Amazon’s
BDA306 An Introduction to Amazon Lex, your Service for Building Voice and Tex...Amazon Web Services
Amazon Lex is a service for building conversational interfaces into any application using voice and text. With Amazon Lex, the same deep learning technologies that power Amazon Alexa are now available to any developer, enabling you to quickly and easily build sophisticated, natural language conversational chatbots. No deep learning experience is required to immediately start creating chatbots that understand voice or text, to ask questions, get answers, and complete sophisticated tasks. Lex enables you to easily publish your chatbots to mobile devices, web apps, services, and platforms such as Facebook Messenger, Twilio and Slack. This session will go over the features available with Amazon Lex and how they can be used to build and deploy chatbots. Join us for this introductory presentation and learn more about Amazon Lex!
by Rohan Dubal, Software Development Engineer, AWS
One of the biggest time sinks and challenges for mobile application developers is developing, accessing, and managing all of the disparate data sources that are involved in delivering delightful, collaborative, and real-time mobile experiences for users while also enabling offline capabilities for when a user is not connected, but still wants to use the app. In this session, you be introduced to the new AWS AppSync service that speed and simplifies these tasks for developers using GraphQL to provide a data abstraction layer and easy query and update statements without having to know the details of the underlying data sources.
ABD322_Implementing a Flight Simulator Interface Using AI, Virtual Reality, a...Amazon Web Services
This workshop explores the technology options, architectures, and implementations associated with instrumenting AR, VR, and simulated worlds. Using flight simulation as the primary use case, you learn to consume, process, store, and analyze high velocity telemetry as well as exploring control plane implementations using AWS IoT, AWS Lambda, Amazon Kinesis, and Amazon SNS. This is a hands-on workshop and you need a laptop (tablets are not suitable). You should have a solid understanding of AWS products and Node.js.
Supercharge your Machine Learning Solutions with Amazon SageMakerAmazon Web Services
Amazon SageMaker is a fully-managed service that enables data scientists and developers to quickly and easily build, train, and deploy machine learning models, at scale. This session will introduce you the features of Amazon SageMaker, including a one-click training environment, highly-optimized machine learning algorithms with built-in model tuning, and deployment without engineering effort. With zero-setup required, Amazon SageMaker significantly decreases your training time and overall cost of building production machine learning systems. You'll also hear how and why Intuit is using Amazon SageMaker on AWS for real-time fraud detection.
Amazon Machine Learning (Amazon ML) is a service that makes it easy for developers of all skill levels to use machine learning technology. Amazon Machine Learning’s powerful algorithms create machine learning models by finding patterns in your existing data. The service uses these models to process new data and generate predictions for your application. In this session, we will show you how to use machine learning with the data you already have to arrive at accurate and actionable predictions - to create smart applications. You will learn how to use and integrate Amazon ML into your applications to take advantage of predictive analysis in the cloud.
"Serverless architectures let you build and deploy applications and services with infrastructure resources that require zero administration. In the past, you had to provision and scale servers to run your application code, install and operate distributed databases, and build and run custom software to handle API requests. Now, AWS provides a stack of scalable, fully-managed services that eliminates these operational complexities. In this session, you will learn about serverless architectures, their benefits, and the basics of the AWS’s serverless stack (e.g., AWS Lambda, Amazon API Gateway, and AWS Step Functions).
We will discuss how to use serverless architectures for a variety of use cases including data processing, website backends, serverless applications, and “operational glue.” You will also get practical tips and tricks, best practices, and architecture patterns that you can take back and implement immediately."
LFS301-SAGE Bionetworks, Digital Mammography DREAM Challenge and How AWS Enab...Amazon Web Services
DREAM Challenges pose fundamental questions about systems biology and translational medicine. Designed and run by a community of researchers from a variety of organizations, the challenges invite participants to propose solutions, fostering collaboration and building communities in the process. The Sage Bionetworks Synapse platform, which powers many research consortiums including the DREAM Challenges, are starting to put into practice model cloud-initiatives that not only provide impactful discoveries in the areas of neuroscience, infectious disease, and cancer, but are also revolutionizing scientific research by enabling an interactive consortium science platform. In this session, you learn how to build a "consortium model" of research in order to connect research organizations with non-profit organizations, technology companies, biotechnology, and pharmaceutical companies. You can also learn about how to leverage machine learning, Amazon ECS, and R for consortium-based science initiatives.
SRV301-Optimizing Serverless Application Data Tiers with Amazon DynamoDBAmazon Web Services
"As a fully managed database service, Amazon DynamoDB is a natural fit for serverless architectures. In this session, we dive deep into why and how to use DynamoDB in serverless applications, followed by a real-world use case from CapitalOne.
First, we dive into the relevant DynamoDB features, and how you can use it effectively with AWS Lambda in solutions ranging from web applications to real-time data processing. We show how some of the new features in DynamoDB, such as Auto Scaling and Time to Live (TTL), are particularly useful in serverless architectures, and distill the best practices to help you create effective serverless applications. In the second part, we talk about how CapitalOne migrated billions of transactions to a completely serverless architecture and built a scalable, resilient and fast transaction platform by leveraging DynamoDB, AWS Lambda and other services within the serverless ecosystem."
Amazon Lex is a service for building conversational interfaces into any application using voice and text, and Amazon Polly is a service that turns text into lifelike speech. This workshop combines both of these AWS services in an interactive session during which attendees build service and informational chatbots that feature spoken-voice interfaces. These modules provide foundational skills for any developer looking to enrich their applications with natural, conversational interfaces.
Amazon Elastic Compute Cloud (Amazon EC2) provides a broad selection of instance types to accommodate a diverse mix of workloads. In this technical session, we provide an overview of the Amazon EC2 instance platform, key platform features, and the concept of instance generations. We dive into the current-generation design choices of the different instance families, including the General Purpose, Compute Optimized, Storage Optimized, Memory Optimized, and GPU instance families. We also detail best practices and share performance tips for getting the most out of your Amazon EC2 instances.
Title:
Auto Scaling in Amazon DynamoDB - August 2017 AWS Online Tech Talks
Learning Objectives:
- Get an overview of DynamoDB Auto Scaling and how it works - Learn about the key benefits of using Auto Scaling in terms of application availability and costs reduction
- Understand best practices for using Auto Scaling and its configuration settings
Recently, DynamoDB announced Auto Scaling, to help automate capacity management for tables and global secondary indexes. Previously, customers had to manually provision read and write capacity based on anticipated application demands. This could result in underprovisioning or overprovisioning capacity. Underprovisioning could slow down application performance, and overprovisioning could result in underutilized resources and higher costs. With DynamoDB Auto Scaling, you simply set your desired throughput utilization target (within your desired minimum and maximum limits), and auto scaling takes care of the rest. In this webinar, we will share how Auto Scaling works in DynamoDB, show how you can enable Auto Scaling with just a few clicks in the AWS Management Console and manage programmatically using the AWS Command Line Interface and the AWS Software Development Kits.
AWS re:Invent 2016: Machine Learning State of the Union Mini Con (MAC206)Amazon Web Services
With the growing number of business cases for artificial intelligence (AI), machine learning (ML) and deep learning (DL) continue to drive the development of cutting edge technology solutions. We see this manifested in computer vision, predictive modeling, natural language understanding, and recommendation engines. During this full afternoon of sessions and workshops, learn how you can develop your own applications to leverage the benefits of these services. Join this State of the Union presentation to hear more about ML and DL at AWS and see how Motorola Solutions is leveraging these state-of-the-art technologies to solve public safety challenges, and how Ohio Health intends to inject AI into the medical system.
An Introduction to the AI services at AWS - AWS Summit Tel Aviv 2017Amazon Web Services
Artificial Intelligence (AI) services on the AWS cloud bring deep learning (DL) technologies like natural language understanding (NLU), automatic speech recognition (ASR), image recognition and computer vision (CV), text-to-speech (TTS), and machine learning (ML) within reach of every developer. In this session, you will be introduced to several new AI services: Amazon Lex, to build sophisticated text and voice chatbots; Amazon Rekognition, for deep learning-based image recognition; and Amazon Polly, for turning text into lifelike speech. The opportunities to apply one or more of these DL services are nearly boundless and this session will provide a number of examples and use cases to help you get started.
Amazon AI services bring natural language understanding (NLU), automatic speech recognition (ASR), visual search and image recognition, text-to-speech (TTS), and ML technologies within the reach of every developer. In this session, we will dive deep into 2 specific AWS services: Amazon Lex and Amazon Polly. Amazon Lex uses the same technology as Amazon Alexa to provide advanced deep learning functionalities of automatic speech recognition (ASR) and natural language understanding (NLU) to enable you to build applications with conversational interfaces, commonly called chatbots. Amazon Polly is a service that turns text into lifelike speech. Polly lets you create applications that speak in over two dozen languages with a wide variety of natural sounding male and female voices to enable you to build entirely new categories of speech-enabled products.
Make your solution see, hear and talk, leveraging artificial intelligence services based on deep learning and neural networks. We will discover three new AI tools from AWS - Lex, Polly and Rekognition; integrated with a physical world device for human interaction and environmental awareness
Speaker: Sunil Mallya, Solutions Architect, AWS Deep Learning
by Dan Romuald Mbanga, Business Development Manager, AWS
Deep learning continues to push the state of the art in domains such as computer vision, natural language understanding and recommendation engines. One of the key reasons for this progress is the availability of highly flexible and developer friendly deep learning frameworks. In this workshop, we will provide an overview of deep learning focusing on getting started with the TensorFlow and Keras frameworks on AWS. Level 100
An Overview of AI on the AWS Platform - June 2017 AWS Online Tech TalksAmazon Web Services
Learning Objectives:
- Learn about the breadth of AI services available on the AWS Cloud
- Gain insight into Amazon Lex, Amazon Polly, and Amazon Rekognition
- Learn more about why Apache MXNet is the deep learning framework of choice for AWS
Make your solution see, hear and talk, leveraging artificial intelligence services based on deep learning and neural networks. We will discover three new AI tools from AWS - Lex, Polly and Rekognition; integrated with AWS IoT and a physical world device for human interaction and environmental awareness.
An Overview of AI on the AWS Platform - February 2017 Online Tech TalksAmazon Web Services
AWS offers a family of intelligent services that provide cloud-native machine learning and deep learning technologies to address your different use cases and needs. For developers looking to add managed AI services to their applications, AWS brings natural language understanding (NLU) and automatic speech recognition (ASR) with Amazon Lex, visual search and image recognition with Amazon Rekognition, text-to-speech (TTS) with Amazon Polly, and developer-focused machine learning with Amazon Machine Learning.
For more in-depth deep learning applications, the AWS Deep Learning AMI lets you run deep learning in the cloud, at any scale. Launch instances of the AMI, pre-installed with open source deep learning engines (Apache MXNet, TensorFlow, Caffe, Theano, Torch and Keras), to train sophisticated, custom AI models, experiment with new algorithms, and learn new deep learning skills and techniques; all backed by auto-scaling clusters of GPU-based instances.
Whether you’re just getting started with AI or you’re a deep learning expert, this session will provide a meaningful overview of how to improve scale and efficiency with the AWS Cloud.
Learning Objectives
• Learn about the breadth of AI services available on the AWS Cloud
• Gain insight into practical use cases for Amazon Lex, Amazon Polly, and Amazon Rekognition
• Understand why Amazon has selected MXNet as its deep learning framework of choice due its programmability, portability, and performance
How Amazon AI Can Help You Transform Your Education Business | AWS WebinarAmazon Web Services
Machine Learning (ML) is a hot topic in the education industry. In this webinar, you will learn how AWS customers are using ML on AWS to transform their companies and their products. We'll go through many use cases across different industries (education, media, finance, retail, etc.), both in the enterprise and the startup worlds. In the process, we'll introduce you to the growing family of API-driven ML services that provide EdTechs with everything they need to innovate and build transformative learning solutions for the education marketplace.
This presentation is focused on building solutions and strategy to solve business or customer engagement challenges. It tells the Amazon Machine Learning story and describes core AWS Artificial Intelligence services such as Polly, Lex and Rekognition can be applied to business problems.
AI & Deep Learning At Amazon - April 2017 AWS Online Tech TalksAmazon Web Services
AWS offers a family of intelligent services that provide cloud-native machine learning and deep learning technologies to address your different use cases and needs. For developers looking to add managed AI services to their applications, AWS brings natural language understanding (NLU) and automatic speech recognition (ASR) with Amazon Lex, visual search and image recognition with Amazon Rekognition, text-to-speech (TTS) with Amazon Polly, and developer-focused machine learning with Amazon Machine Learning. For more in-depth deep learning applications, the AWS Deep Learning AMI lets you run deep learning in the cloud, at any scale. Launch instances of the AMI, pre-installed with open source deep learning engines (Apache MXNet, TensorFlow, Caffe, Theano, Torch and Keras), to train sophisticated, custom AI models, experiment with new algorithms, and learn new deep learning skills and techniques; all backed by auto-scaling clusters of GPU-based instances. Whether you’re just getting started with AI or you’re a deep learning expert, this session will provide a meaningful overview of how to improve scale and efficiency with the AWS Cloud.
Learning Objectives:
• Learn about the breadth of AI services available on the AWS Cloud
• Gain insight into practical use cases for Amazon Lex, Amazon Polly, and Amazon Rekognition
• Understand why Amazon has selected MXNet as its deep learning framework of choice due its programmability, portability, and performance
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.
Durante i laboratori pratici, gli esperti AWS ti mostrano quali strumenti aiutano a sviluppare le applicazioni Serverless in locale e nel cloud AWS e ti aiuteranno a programmare i prossimi passi per iniziare ad utilizzare questa tecnologia nella tua azienda.
14. A Flywheel For Data
Machine Learning
Deep Learning
AI
More Users Better Products
More Data Better Analytics
Object Storage
Databases
Data warehouse
Streaming analytics
BI
Hadoop
Spark/Presto
Elasticsearch
Click stream
User activity
Generated content
Purchases
Clicks
Likes
Sensor data
15. Machine Learning &
Artificial Intelligence
Big Data
More Users Better Products
More Data Better Analytics
A Flywheel For Data
20. Thousands Of Employees Across The Company Focused on AI
Discovery &
Search
Fulfilment &
Logistics
Enhance
Existing Products
Define New
Product
Categories
Bring Machine
Learning To All
Artificial Intelligence At Amazon
21. Thousands Of Employees Across The Company Focused on AI
Discovery &
Search
Artificial Intelligence At Amazon
23. Thousands Of Employees Across The Company Focused on AI
Discovery &
Search
Fulfilment &
Logistics
Artificial Intelligence At Amazon
24.
25. Thousands Of Employees Across The Company Focused on AI
Discovery &
Search
Fulfilment &
Logistics
Enhance
Existing Products
Artificial Intelligence At Amazon
26. Thousands Of Employees Across The Company Focused on AI
Discovery &
Search
Fulfilment &
Logistics
Enhance
Existing Products
Define New
Product
Categories
Artificial Intelligence At Amazon
27.
28.
29. Thousands Of Employees Across The Company Focused on AI
Discovery &
Search
Fulfilment &
Logistics
Enhance
Existing Products
Define New
Product
Categories
Bring Machine
Learning To All
Artificial Intelligence At Amazon
35. Tons of GPUs and CPUs
Serverless
At the Edge, On IoT Devices
Prediction
The Challenge For Artificial Intelligence: SCALE
Tons of GPUs
Elastic capacity
Training
Pre-built images
Aggressive migration
New data created on AWS
Data
PBs of existing data
36. Can We Help Customers
Put Intelligence At The Heart Of
Every Application & Business?
37. Machine Learning &
Artificial Intelligence
Big Data
More Users Better Products
More Data Better Analytics
A Flywheel For Data
42. AWS offers a range of tools to make AI/ML more accessible
PollyLex Rekognition
Deep Learning FrameworksMachine Learning PlatformsAmazon AI/ML Services
Usability/simplicity:
leverages AWS AI/ML expertise
Greater control:
customer-specific models
These solutions are underpinned by proven,
scalable AWS products and services
AWS
Greengrass
AWS
IoT
AWS
Lambda
Amazon EC2
(P2 and G2 GPUs)
Amazon
S3
Amazon
DynamoDB
Amazon
Redshift
Amazon EC2
(CPUs)
Amazon EC2
(ENA)
Amazon ML
Spark & EMR
Kinesis
Batch
ECS
MXNet, TensorFlow, Theano, Caffe, Torch
43. One-Click GPU
Deep Learning
AWS Deep Learning AMI
Up to~40k CUDA cores
MXNet
TensorFlow
Theano
Caffe
Torch
Pre-configured CUDA drivers
Anaconda, Python3
+ CloudFormation template
+ Container Image
46. Converts text
to life-like speech
47 voices 24 languages Low latency,
real time
Fully managed
Polly: Life-like Speech Service
Voice Quality & Pronunciation
1. Automatic, Accurate Text Processing
2. Intelligible and Easy to Understand
3. Add Semantic Meaning to Text
4. Customized Pronunciation
Articles and Blogs
Training Material
Chatbots (Lex)
Public Announcements
53. Still lost in the forest, Marry started to whisper:
"Don't make any noise, they will find us"
54. Duolingo voices its language learning service Using Polly
Duolingo is a free language learning service where
users help translate the web and rate translations.
With Amazon Polly our users
benefit from the most lifelike
Text-to-Speech voices
available on the market.
Severin Hacker
CTO, Duolingo
”
“ • Spoken language crucial for
language learning
• Accurate pronunciation matters
• Faster iteration thanks to TTS
• As good as natural human speech
58. Voice & Text
“Chatbots”
Powers
Alexa
Voice interactions
on mobile, web
& devices
Text interaction
with Slack & Messenger
Enterprise
Connectors
(with more coming) Salesforce
Microsoft Dynamics
Marketo
Zendesk
Quickbooks
Hubspot
Lex: Build Natural, Conversational
Interactions In Voice & Text
Improving human interactions…
• Contact, service, and support center interfaces (text + voice)
• Employee productivity and collaboration (minutes into seconds)
59.
60. Origin
Destination
Departure Date
Flight Booking
“Book a flight
to London”
Automatic
Speech Recognition
Natural Language
Understanding
Book Flight
London
Utterances
Flight booking
London Heathrow
Intent /
Slot model
London Heathrow
61. Origin
Destination
Departure Date
Flight Booking
“Book a flight
to London”
Automatic
Speech Recognition
Natural Language
Understanding
Book Flight
London
Utterances
Flight booking
London Heathrow
Intent /
Slot model
London Heathrow
LocationLocation
Seattle
62. Origin
Destination
Departure Date
Flight Booking
“Book a flight
to London”
Automatic
Speech Recognition
Natural Language
Understanding
Book Flight
London
Utterances
Flight booking
London Heathrow
Intent /
Slot model
London Heathrow
LocationLocation
Seattle
Prompt
“When would you like to fly?”
“When would you
like to fly?”
Polly
64. Origin
Destination
Departure Date
Flight Booking
“Next Friday”
Automatic
Speech Recognition
Next Friday
Utterances
Natural Language
Understanding
Flight booking
02 / 24 / 2017
Intent /
Slot model
London Heathrow
Seattle
02/24/2017
65. Origin
Destination
Departure Date
Flight Booking
“Next Friday”
Automatic
Speech Recognition
Next Friday
Utterances
Natural Language
Understanding
Flight booking
02 / 24 / 2017
Intent /
Slot model
London Heathrow
Seattle
02/24/2017
Confirmation
“Your flight is booked for next Friday”
“Your flight is booked
for next Friday”
Polly
66. Origin
Destination
Departure Date
Flight Booking
“Next Friday”
Automatic
Speech Recognition
Next Friday
Utterances
Natural Language
Understanding
Flight booking
02 / 24 / 2017
Intent /
Slot model
London Heathrow
Seattle
02/24/2017
Hotel Booking
69. Amazon Rekognition
Deep learning-based image recognition service
Search, verify, and organize millions of images
Object and Scene
Detection
Facial
Analysis
Face
Comparison
Facial
Recognition
Integrated with S3, Lambda, Polly, Lex
78. Object and Scene Detection
Generate labels for thousands of objects, scenes, and
concepts, each with a confidence score
• Search, filter, and
curate image
libraries
• Smart searches for
user generated
content
• Photo, travel, real
estate, vacation
rental applications
Maple
Plant
Villa
Garden
Water
Swimming Pool
Tree
Potted Plant
Backyard
79. Facial Analysis
Locate faces within images and analyze face attributes to
detect emotion, pose, facial landmarks, and features
• Avoid faces when cropping
images and overlaying ads
• Capture user demographics
and sentiment
• Recommend the best photos
• Improve online dating match
recommendations
• Dynamic, personalized ads
83. Face Comparison
Measure the likelihood that faces in two images are of the
same person
• Add face verification to
applications and devices
• Extend physical security
controls
• Provide guest access to
VIP-only facilities
• Verify users for online
exams and polls
84. Facial Search
Identify people in images by finding the closest match for an
input face image against a collection of stored face vectors
• Add friend tagging to
social and messaging apps
• Assist public safety officers
find missing persons
• Identify employees as they
access sensitive locations
• Identify celebrities in
historical media archives
86. Travel and Hospitality
Anticipatory guest experiences for hotels using Amazon
Rekognition for facial recognition and sentiment capture
Kaliber is using Amazon Rekognition to help front desk agents
enhance relationships with guests:
• Recognize guests early for instant and personalized service
• Receive rich, contextualized guest information in real time
• Track guest sentiment throughout their stay
• Drive an 80% increase in guest satisfaction scores
87.
88. Media Case Study
Identify who is on camera at what time for
each of 8 networks so that recorded video
streams can be indexed and searched
Video frame-sampling facial recognition
solution using Amazon Rekognition:
• Indexed 97,000 people into a face collection in
1 day
• Sample frames every 6 secs and test for image
variance
• Upload images to S3 and call Rekognition to
find best facial match
• Store time stamp and faceID metadata
90. Influencer Marketing Case Study
Associate influencers with objects and scenes in social media
images in order to create high impact campaigns for clients
Using Rekognition for metadata extraction:
• Create rich media indexes of images from social media feeds, which
the application associates with influencers
• Enable analytics to profile environments where influence is strongest
• Connect client brands with the influencers most likely to have impact
91. Rekognition Customers
Media and Entertainment
Public Safety
Law Enforcement
Digital Asset Management
Influencer Marketing
Digital Advertising
Education
Consumer Storage
92.
93.
94. Amazon AI Services
• Leveraging Amazon internal experiences with AI / ML
• Managed API services with embedded AI for maximum
accessibility and simplicity
• Full stack of platforms and engines for specialized deep
learning applications