The document provides an overview of artificial intelligence services available on Amazon Web Services (AWS), including Amazon Lex, Amazon Polly, Amazon Rekognition, and Apache MXNet. It discusses the capabilities and use cases of each service, such as converting text to speech (Amazon Polly), computer vision capabilities like object detection (Amazon Rekognition), and building conversational chatbots (Amazon Lex). It also covers deep learning frameworks like Apache MXNet and resources for developing AI solutions on AWS.
An Overview of AI at AWS: Amazon Lex, Amazon Polly, Amazon Rekognition, Apach...Amazon Web Services
by Keith Steward, Solutions Architect, AWS
AI services on the AWS cloud bring deep learning technologies like natural language understanding, automatic speech recognition, computer vision, text-to-speech, and machine learning within reach of every developer. For more in-depth deep learning applications, the Deep Learning AMIs let you create managed, auto-scaling clusters of GPUs for large scale training, or run inference on trained models with compute-optimized or general-purpose CPU 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. Level 200
AWS Summit Singapore - Get to Know Your Customers - Modern Data ArchitectureAmazon Web Services
Craig Stires, Business Development Manager - Big Data & Analytics, APAC, AWS
Your customers probably want a better experience with your brand. Your different business teams want and need better insights in their decision making. Almost certainly, your finance and operations teams require this to happen at a fraction of the cost of traditional on-premises options. Modern data architectures on AWS help many of our best customers realise all of those goals. Your business data contains critical information about customer behaviours, operational decisions, and many factors that have financial impact on your organisation. Increasingly, this data sits beyond your transactional systems, and is too big, too fast, and too complex for existing systems to handle. AWS Data and Analytics services are designed from our customers' requirements to ingest, store, analyse, and consume information at record-breaking scale. In this session you will learn how these services work together to deliver business automation, enhance customer engagement and intelligence.
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
An Overview of AI at AWS: Amazon Lex, Amazon Polly, Amazon Rekognition, Apach...Amazon Web Services
by Keith Steward, Solutions Architect, AWS
AI services on the AWS cloud bring deep learning technologies like natural language understanding, automatic speech recognition, computer vision, text-to-speech, and machine learning within reach of every developer. For more in-depth deep learning applications, the Deep Learning AMIs let you create managed, auto-scaling clusters of GPUs for large scale training, or run inference on trained models with compute-optimized or general-purpose CPU 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. Level 200
AWS Summit Singapore - Get to Know Your Customers - Modern Data ArchitectureAmazon Web Services
Craig Stires, Business Development Manager - Big Data & Analytics, APAC, AWS
Your customers probably want a better experience with your brand. Your different business teams want and need better insights in their decision making. Almost certainly, your finance and operations teams require this to happen at a fraction of the cost of traditional on-premises options. Modern data architectures on AWS help many of our best customers realise all of those goals. Your business data contains critical information about customer behaviours, operational decisions, and many factors that have financial impact on your organisation. Increasingly, this data sits beyond your transactional systems, and is too big, too fast, and too complex for existing systems to handle. AWS Data and Analytics services are designed from our customers' requirements to ingest, store, analyse, and consume information at record-breaking scale. In this session you will learn how these services work together to deliver business automation, enhance customer engagement and intelligence.
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
by Dario Rivera, Solutions Architect, AWS
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.
Using artificial intelligence to enhance your customer experienceAmazon Web Services
Artificial Intelligence (AI) is enhancing many of the services that we interact with today. It can improve the customer experience of many services to make them more accessible, whilst providing information faster in a format that feels more natural.
AWS provides a collection of highly scalable, pre-trained and pre-tuned managed AI services that you can adopt without any previous artificial intelligence or deep learning knowledge. In this webinar, Steve explains how to implement each of these services to improve the user journey for a flight booking and check-in system.
The AWS solutions discussed here include Amazon Polly, which provides audio instructions for sight-impaired users and Amazon Rekognition, which provides an additional layer of security during the check-in process, matching users with customer data on file. Finally, Amazon Lex is used to enable customers to make future flight bookings using only their voices.
Learning objectives:
- Understand why you may wish to use AI in your applications today
- Identify the common AI challenges and practical use cases for Amazon AI services
- Implement Amazon AI services without a PhD or Data Science background
Building Speech Enabled Products with Amazon Polly & Amazon LexAmazon Web Services
This session will introduce you to Amazon Polly, a deep learning service that turns text into lifelike speech. Polly enables existing applications to speak as a first class feature and creates the opportunity for entirely new categories of speech-enabled products – from mobile apps and cars, to devices and appliances. Polly includes 47 lifelike voices and support for 24 languages, so you can select the ideal voice and distribute your speech-enabled applications in many geographies. Polly is easy to use – you just send the text you want converted into speech to the Polly API, and Polly immediately returns the audio stream to your application so you can play it directly or store it in a standard audio file format, such as MP3. Polly supports Speech Synthesis Markup Language (SSML) tags like prosody so you can adjust the speech rate, pitch, or volume. Polly is a secure service that delivers all of these benefits at high scale and at low latency. You can cache and replay Polly’s generated speech at no additional cost. Polly lets you convert 5M characters per month for free during the first year. Polly’s pay-as-you-go pricing, low cost per request, and lack of restrictions on storage and reuse of voice output make it a cost-effective way to enable speech synthesis everywhere.
Demystifying Machine Learning On AWS - AWS Summit Sydney 2018Amazon Web Services
Demystifying Machine Learning on AWS
Machine Learning is having a major impact in our society, but how can we simplify the build, train, and deploy process for all developers and data scientists? Understand how cloud-based machine learning frameworks can help turn your data into intelligence. We will introduce the general machine learning process utilising the AWS Deep Learning AMIs and hear from carsales.com.au about how they built the Cyclops, a Super Human Image Recognition Software on AWS. We will then discuss the new capabilities delivered by Amazon SageMaker and how this product will further reduce the undifferentiated heavy lifting; freeing you up to focus on your business and allow your developers to quickly and easily expand into the world of Machine Learning.
Jenny Davies, Solutions Architect, Amazon Web Services and Agustinus Nalwan, AI and Machine Learning Technical Development Manager, Carsales.com.au
Building ChatBots with Amazon Lex - AWS Summit Tel Aviv 2017Amazon Web Services
Amazon Lex is a service for building conversational interfaces into any application using voice and text. Amazon Lex provides the advanced deep learning functionalities of automatic speech recognition (ASR) for converting speech to text, and natural language understanding (NLU) to recognize the intent of the text, to enable you to build applications with highly engaging user experiences and lifelike conversational interactions. Join this session and learn how get started with Amazon Lex, add conversational interface features to your applications and integrate with text-chat and voice based interfaces.
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.
Machine Learning is having a major impact in our society, but how can we simplify the build, train, and deploy process for all developers and data scientists? Understand how cloud-based machine learning frameworks can help turn your data into intelligence. We will introduce the general machine learning process utilising the AWS Deep Learning AMIs and hear from carsales.com.au about how they built the Cyclops, a Super Human Image Recognition Software on AWS. We will then discuss the new capabilities delivered by Amazon SageMaker and how this product will further reduce the undifferentiated heavy lifting; freeing you up to focus on your business and allow your developers to quickly and easily expand into the world of Machine Learning.
Introducing Amazon Lex – Service for Building Voice or Text ChatbotsAmazon Web Services
Register for the Chatbot Challenge!
https://awschatbot2017.devpost.com/
- Learn about the features of Amazon Lex
- Learn how to build a chatbot with Amazon Lex
- Learn how to deploy to different messaging platforms with Amazon Lex from the console
- Learn how to get quickly started with Amazon Lex
Conversational interfaces through text with applications like Slack, and through voice with platforms like Amazon Alexa, are rapidly becoming mainstream. Amazon Lex enables you to quickly and easily build sophisticated, natural language conversational chatbots with the same deep learning technologies that power Amazon Alexa. 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. This session will go over the features available with Amazon Lex and how they can be used, including how to easily publish your chatbots to mobile devices, web apps, services, and platforms such as Facebook Messenger, Twilio and Slack.
Engage your users with a natural language conversational interface using voice and text. Create a chat bot to understand your users’ intentions and fulfill their requests. Engage in a conversation to extract key pieces of data from the user. Fulfil the users’ intentions with AWS Lambda functions. Integration examples with Facebook messenger & Slack.
Getting Started with Amazon Lex - AWS Summit Cape Town 2017 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!
AWS Speaker: Herbert-John Kelly, Solutions Architect - Amazon Web Services
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
Building a Chatbot with Amazon Lex and AWS Lambda WorkshopAmazon Web Services
Like coffee? Or just want to build a bot that can take your order? Come learn how to build a chatbot using Amazon Lex and AWS Lambda. And if you’re up for it, bring a cable and a mobile device so you can see how easy it is to make a real app that talks back using AWS Mobile Hub.
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.
by Dario Rivera, Solutions Architect, AWS
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.
Using artificial intelligence to enhance your customer experienceAmazon Web Services
Artificial Intelligence (AI) is enhancing many of the services that we interact with today. It can improve the customer experience of many services to make them more accessible, whilst providing information faster in a format that feels more natural.
AWS provides a collection of highly scalable, pre-trained and pre-tuned managed AI services that you can adopt without any previous artificial intelligence or deep learning knowledge. In this webinar, Steve explains how to implement each of these services to improve the user journey for a flight booking and check-in system.
The AWS solutions discussed here include Amazon Polly, which provides audio instructions for sight-impaired users and Amazon Rekognition, which provides an additional layer of security during the check-in process, matching users with customer data on file. Finally, Amazon Lex is used to enable customers to make future flight bookings using only their voices.
Learning objectives:
- Understand why you may wish to use AI in your applications today
- Identify the common AI challenges and practical use cases for Amazon AI services
- Implement Amazon AI services without a PhD or Data Science background
Building Speech Enabled Products with Amazon Polly & Amazon LexAmazon Web Services
This session will introduce you to Amazon Polly, a deep learning service that turns text into lifelike speech. Polly enables existing applications to speak as a first class feature and creates the opportunity for entirely new categories of speech-enabled products – from mobile apps and cars, to devices and appliances. Polly includes 47 lifelike voices and support for 24 languages, so you can select the ideal voice and distribute your speech-enabled applications in many geographies. Polly is easy to use – you just send the text you want converted into speech to the Polly API, and Polly immediately returns the audio stream to your application so you can play it directly or store it in a standard audio file format, such as MP3. Polly supports Speech Synthesis Markup Language (SSML) tags like prosody so you can adjust the speech rate, pitch, or volume. Polly is a secure service that delivers all of these benefits at high scale and at low latency. You can cache and replay Polly’s generated speech at no additional cost. Polly lets you convert 5M characters per month for free during the first year. Polly’s pay-as-you-go pricing, low cost per request, and lack of restrictions on storage and reuse of voice output make it a cost-effective way to enable speech synthesis everywhere.
Demystifying Machine Learning On AWS - AWS Summit Sydney 2018Amazon Web Services
Demystifying Machine Learning on AWS
Machine Learning is having a major impact in our society, but how can we simplify the build, train, and deploy process for all developers and data scientists? Understand how cloud-based machine learning frameworks can help turn your data into intelligence. We will introduce the general machine learning process utilising the AWS Deep Learning AMIs and hear from carsales.com.au about how they built the Cyclops, a Super Human Image Recognition Software on AWS. We will then discuss the new capabilities delivered by Amazon SageMaker and how this product will further reduce the undifferentiated heavy lifting; freeing you up to focus on your business and allow your developers to quickly and easily expand into the world of Machine Learning.
Jenny Davies, Solutions Architect, Amazon Web Services and Agustinus Nalwan, AI and Machine Learning Technical Development Manager, Carsales.com.au
Building ChatBots with Amazon Lex - AWS Summit Tel Aviv 2017Amazon Web Services
Amazon Lex is a service for building conversational interfaces into any application using voice and text. Amazon Lex provides the advanced deep learning functionalities of automatic speech recognition (ASR) for converting speech to text, and natural language understanding (NLU) to recognize the intent of the text, to enable you to build applications with highly engaging user experiences and lifelike conversational interactions. Join this session and learn how get started with Amazon Lex, add conversational interface features to your applications and integrate with text-chat and voice based interfaces.
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.
Machine Learning is having a major impact in our society, but how can we simplify the build, train, and deploy process for all developers and data scientists? Understand how cloud-based machine learning frameworks can help turn your data into intelligence. We will introduce the general machine learning process utilising the AWS Deep Learning AMIs and hear from carsales.com.au about how they built the Cyclops, a Super Human Image Recognition Software on AWS. We will then discuss the new capabilities delivered by Amazon SageMaker and how this product will further reduce the undifferentiated heavy lifting; freeing you up to focus on your business and allow your developers to quickly and easily expand into the world of Machine Learning.
Introducing Amazon Lex – Service for Building Voice or Text ChatbotsAmazon Web Services
Register for the Chatbot Challenge!
https://awschatbot2017.devpost.com/
- Learn about the features of Amazon Lex
- Learn how to build a chatbot with Amazon Lex
- Learn how to deploy to different messaging platforms with Amazon Lex from the console
- Learn how to get quickly started with Amazon Lex
Conversational interfaces through text with applications like Slack, and through voice with platforms like Amazon Alexa, are rapidly becoming mainstream. Amazon Lex enables you to quickly and easily build sophisticated, natural language conversational chatbots with the same deep learning technologies that power Amazon Alexa. 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. This session will go over the features available with Amazon Lex and how they can be used, including how to easily publish your chatbots to mobile devices, web apps, services, and platforms such as Facebook Messenger, Twilio and Slack.
Engage your users with a natural language conversational interface using voice and text. Create a chat bot to understand your users’ intentions and fulfill their requests. Engage in a conversation to extract key pieces of data from the user. Fulfil the users’ intentions with AWS Lambda functions. Integration examples with Facebook messenger & Slack.
Getting Started with Amazon Lex - AWS Summit Cape Town 2017 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!
AWS Speaker: Herbert-John Kelly, Solutions Architect - Amazon Web Services
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
Building a Chatbot with Amazon Lex and AWS Lambda WorkshopAmazon Web Services
Like coffee? Or just want to build a bot that can take your order? Come learn how to build a chatbot using Amazon Lex and AWS Lambda. And if you’re up for it, bring a cable and a mobile device so you can see how easy it is to make a real app that talks back using AWS Mobile Hub.
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.
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.
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.
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
New Artificial Intelligence and IoT Services (Lex, Polly, Rekognition, Greeng...Amazon Web Services
This talk will recap new AI and IoT services announced or released at re:Invent. In the AI space, Gavin will cover Lex, the same deep learning engine that power Alexa; Polly, a service that turns text into lifelike speech; and Rekognition, a service that makes it easy to add image analysis to your applications. For IoT, he will cover Greengrass, which is software that lets you run local compute, messaging and data caching for connected devices. Also covered will be the 2nd generation AWS IoT Button, and Enterprise Program.
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.
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.
Amazon AI services bring natural language understanding (NLU), automatic speech recognition (ASR), visual search and image recognition, text-to-speech (TTS), and machine learning (ML) technologies within the reach of every developer. Based on the same proven, highly scalable products and services built by the thousands of deep learning and machine learning experts across Amazon, Amazon AI services provide high-quality, high-accuracy AI capabilities that are scalable and cost-effective. Speaker: Ian Massingham, AWS Evangelist
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.
Similar to Overview of Artificial Intelligence at AWS - DevDay Los Angeles 2017 (20)
Come costruire servizi di Forecasting sfruttando algoritmi di ML e deep learn...Amazon Web Services
Il Forecasting è un processo importante per tantissime aziende e viene utilizzato in vari ambiti per cercare di prevedere in modo accurato la crescita e distribuzione di un prodotto, l’utilizzo delle risorse necessarie nelle linee produttive, presentazioni finanziarie e tanto altro. Amazon utilizza delle tecniche avanzate di forecasting, in parte questi servizi sono stati messi a disposizione di tutti i clienti AWS.
In questa sessione illustreremo come pre-processare i dati che contengono una componente temporale e successivamente utilizzare un algoritmo che a partire dal tipo di dato analizzato produce un forecasting accurato.
Big Data per le Startup: come creare applicazioni Big Data in modalità Server...Amazon Web Services
La varietà e la quantità di dati che si crea ogni giorno accelera sempre più velocemente e rappresenta una opportunità irripetibile per innovare e creare nuove startup.
Tuttavia gestire grandi quantità di dati può apparire complesso: creare cluster Big Data su larga scala sembra essere un investimento accessibile solo ad aziende consolidate. Ma l’elasticità del Cloud e, in particolare, i servizi Serverless ci permettono di rompere questi limiti.
Vediamo quindi come è possibile sviluppare applicazioni Big Data rapidamente, senza preoccuparci dell’infrastruttura, ma dedicando tutte le risorse allo sviluppo delle nostre le nostre idee per creare prodotti innovativi.
Ora puoi utilizzare Amazon Elastic Kubernetes Service (EKS) per eseguire pod Kubernetes su AWS Fargate, il motore di elaborazione serverless creato per container su AWS. Questo rende più semplice che mai costruire ed eseguire le tue applicazioni Kubernetes nel cloud AWS.In questa sessione presenteremo le caratteristiche principali del servizio e come distribuire la tua applicazione in pochi passaggi
Vent'anni fa Amazon ha attraversato una trasformazione radicale con l'obiettivo di aumentare il ritmo dell'innovazione. In questo periodo abbiamo imparato come cambiare il nostro approccio allo sviluppo delle applicazioni ci ha permesso di aumentare notevolmente l'agilità, la velocità di rilascio e, in definitiva, ci ha consentito di creare applicazioni più affidabili e scalabili. In questa sessione illustreremo come definiamo le applicazioni moderne e come la creazione di app moderne influisce non solo sull'architettura dell'applicazione, ma sulla struttura organizzativa, sulle pipeline di rilascio dello sviluppo e persino sul modello operativo. Descriveremo anche approcci comuni alla modernizzazione, compreso l'approccio utilizzato dalla stessa Amazon.com.
Come spendere fino al 90% in meno con i container e le istanze spot Amazon Web Services
L’utilizzo dei container è in continua crescita.
Se correttamente disegnate, le applicazioni basate su Container sono molto spesso stateless e flessibili.
I servizi AWS ECS, EKS e Kubernetes su EC2 possono sfruttare le istanze Spot, portando ad un risparmio medio del 70% rispetto alle istanze On Demand. In questa sessione scopriremo insieme quali sono le caratteristiche delle istanze Spot e come possono essere utilizzate facilmente su AWS. Impareremo inoltre come Spreaker sfrutta le istanze spot per eseguire applicazioni di diverso tipo, in produzione, ad una frazione del costo on-demand!
In recent months, many customers have been asking us the question – how to monetise Open APIs, simplify Fintech integrations and accelerate adoption of various Open Banking business models. Therefore, AWS and FinConecta would like to invite you to Open Finance marketplace presentation on October 20th.
Event Agenda :
Open banking so far (short recap)
• PSD2, OB UK, OB Australia, OB LATAM, OB Israel
Intro to Open Finance marketplace
• Scope
• Features
• Tech overview and Demo
The role of the Cloud
The Future of APIs
• Complying with regulation
• Monetizing data / APIs
• Business models
• Time to market
One platform for all: a Strategic approach
Q&A
Rendi unica l’offerta della tua startup sul mercato con i servizi Machine Lea...Amazon Web Services
Per creare valore e costruire una propria offerta differenziante e riconoscibile, le startup di successo sanno come combinare tecnologie consolidate con componenti innovativi creati ad hoc.
AWS fornisce servizi pronti all'utilizzo e, allo stesso tempo, permette di personalizzare e creare gli elementi differenzianti della propria offerta.
Concentrandoci sulle tecnologie di Machine Learning, vedremo come selezionare i servizi di intelligenza artificiale offerti da AWS e, anche attraverso una demo, come costruire modelli di Machine Learning personalizzati utilizzando SageMaker Studio.
OpsWorks Configuration Management: automatizza la gestione e i deployment del...Amazon Web Services
Con l'approccio tradizionale al mondo IT per molti anni è stato difficile implementare tecniche di DevOps, che finora spesso hanno previsto attività manuali portando di tanto in tanto a dei downtime degli applicativi interrompendo l'operatività dell'utente. Con l'avvento del cloud, le tecniche di DevOps sono ormai a portata di tutti a basso costo per qualsiasi genere di workload, garantendo maggiore affidabilità del sistema e risultando in dei significativi miglioramenti della business continuity.
AWS mette a disposizione AWS OpsWork come strumento di Configuration Management che mira ad automatizzare e semplificare la gestione e i deployment delle istanze EC2 per mezzo di workload Chef e Puppet.
Scopri come sfruttare AWS OpsWork a garanzia e affidabilità del tuo applicativo installato su Instanze EC2.
Microsoft Active Directory su AWS per supportare i tuoi Windows WorkloadsAmazon Web Services
Vuoi conoscere le opzioni per eseguire Microsoft Active Directory su AWS? Quando si spostano carichi di lavoro Microsoft in AWS, è importante considerare come distribuire Microsoft Active Directory per supportare la gestione, l'autenticazione e l'autorizzazione dei criteri di gruppo. In questa sessione, discuteremo le opzioni per la distribuzione di Microsoft Active Directory su AWS, incluso AWS Directory Service per Microsoft Active Directory e la distribuzione di Active Directory su Windows su Amazon Elastic Compute Cloud (Amazon EC2). Trattiamo argomenti quali l'integrazione del tuo ambiente Microsoft Active Directory locale nel cloud e l'utilizzo di applicazioni SaaS, come Office 365, con AWS Single Sign-On.
Dal riconoscimento facciale al riconoscimento di frodi o difetti di fabbricazione, l'analisi di immagini e video che sfruttano tecniche di intelligenza artificiale, si stanno evolvendo e raffinando a ritmi elevati. In questo webinar esploreremo le possibilità messe a disposizione dai servizi AWS per applicare lo stato dell'arte delle tecniche di computer vision a scenari reali.
Amazon Web Services e VMware organizzano un evento virtuale gratuito il prossimo mercoledì 14 Ottobre dalle 12:00 alle 13:00 dedicato a VMware Cloud ™ on AWS, il servizio on demand che consente di eseguire applicazioni in ambienti cloud basati su VMware vSphere® e di accedere ad una vasta gamma di servizi AWS, sfruttando a pieno le potenzialità del cloud AWS e tutelando gli investimenti VMware esistenti.
Molte organizzazioni sfruttano i vantaggi del cloud migrando i propri carichi di lavoro Oracle e assicurandosi notevoli vantaggi in termini di agilità ed efficienza dei costi.
La migrazione di questi carichi di lavoro, può creare complessità durante la modernizzazione e il refactoring delle applicazioni e a questo si possono aggiungere rischi di prestazione che possono essere introdotti quando si spostano le applicazioni dai data center locali.
Crea la tua prima serverless ledger-based app con QLDB e NodeJSAmazon Web Services
Molte aziende oggi, costruiscono applicazioni con funzionalità di tipo ledger ad esempio per verificare lo storico di accrediti o addebiti nelle transazioni bancarie o ancora per tenere traccia del flusso supply chain dei propri prodotti.
Alla base di queste soluzioni ci sono i database ledger che permettono di avere un log delle transazioni trasparente, immutabile e crittograficamente verificabile, ma sono strumenti complessi e onerosi da gestire.
Amazon QLDB elimina la necessità di costruire sistemi personalizzati e complessi fornendo un database ledger serverless completamente gestito.
In questa sessione scopriremo come realizzare un'applicazione serverless completa che utilizzi le funzionalità di QLDB.
Con l’ascesa delle architetture di microservizi e delle ricche applicazioni mobili e Web, le API sono più importanti che mai per offrire agli utenti finali una user experience eccezionale. In questa sessione impareremo come affrontare le moderne sfide di progettazione delle API con GraphQL, un linguaggio di query API open source utilizzato da Facebook, Amazon e altro e come utilizzare AWS AppSync, un servizio GraphQL serverless gestito su AWS. Approfondiremo diversi scenari, comprendendo come AppSync può aiutare a risolvere questi casi d’uso creando API moderne con funzionalità di aggiornamento dati in tempo reale e offline.
Inoltre, impareremo come Sky Italia utilizza AWS AppSync per fornire aggiornamenti sportivi in tempo reale agli utenti del proprio portale web.
Database Oracle e VMware Cloud™ on AWS: i miti da sfatareAmazon Web Services
Molte organizzazioni sfruttano i vantaggi del cloud migrando i propri carichi di lavoro Oracle e assicurandosi notevoli vantaggi in termini di agilità ed efficienza dei costi.
La migrazione di questi carichi di lavoro, può creare complessità durante la modernizzazione e il refactoring delle applicazioni e a questo si possono aggiungere rischi di prestazione che possono essere introdotti quando si spostano le applicazioni dai data center locali.
In queste slide, gli esperti AWS e VMware presentano semplici e pratici accorgimenti per facilitare e semplificare la migrazione dei carichi di lavoro Oracle accelerando la trasformazione verso il cloud, approfondiranno l’architettura e dimostreranno come sfruttare a pieno le potenzialità di VMware Cloud ™ on AWS.
Amazon Elastic Container Service (Amazon ECS) è un servizio di gestione dei container altamente scalabile, che semplifica la gestione dei contenitori Docker attraverso un layer di orchestrazione per il controllo del deployment e del relativo lifecycle. In questa sessione presenteremo le principali caratteristiche del servizio, le architetture di riferimento per i differenti carichi di lavoro e i semplici passi necessari per poter velocemente migrare uno o più dei tuo container.
6. An Introduction to the AI Services at AWS
Apache
Apache
MXNet
Deep learning framework
7. An Introduction to the AI Services at AWS
Apache
Amazon
Polly
Text-to-Speech
Apache
MXNet
Deep learning framework
8. An Introduction to the AI Services at AWS
Apache
Amazon
Polly
Text-to-Speech
Amazon
Rekognition
Computer Vision
Apache
MXNet
Deep learning framework
9. An Introduction to the AI Services at AWS
Apache
Amazon
Polly
Text-to-Speech
Amazon
Rekognition
Amazon
Lex
Computer Vision ASR & NLU
Apache
MXNet
Deep learning framework
10. An Introduction to the AI Services at AWS
Apache
MXNet
Apache
Deep learning framework
11. Apache MXNet
Programmable Portable High Performance
Near linear scaling
across hundreds of GPUs
Highly efficient
models for mobile
and IoT
Simple syntax,
multiple languages
12. Why Apache MXNet?
Most Open Best On AWS
Optimized for
deep learning on AWS
Accepted into the
Apache Incubator
(Integration with AWS)
13. Apache MXNet is the deep learning framework
of choice for AWS
18. “Today in Seattle, WA, it’s 11°F”
‘"We live for the music" live from the Madison Square Garden.’
1. Automatic, Accurate Text Processing
Amazon Polly:
A Focus On Voice Quality & Pronunciation
19. 2. Intelligible and Easy to Understand
1. Automatic, Accurate Text Processing
Amazon Polly:
A Focus On Voice Quality & Pronunciation
20. 2. Intelligible and Easy to Understand
3. Add Semantic Meaning to Text
“Richard’s number is 2122341237“
“Richard’s number is 2122341237“
Telephone Number
Amazon Polly:
A Focus On Voice Quality & Pronunciation
1. Automatic, Accurate Text Processing
21. 2. Intelligible and Easy to Understand
3. Add Semantic Meaning to Text
4. Customized Pronunciation
“My daughter’s name is Kaja.”
“My daughter’s name is Kaja.”
1. Automatic, Accurate Text Processing
Amazon Polly:
A Focus On Voice Quality & Pronunciation
22. Amazon Polly: Common Use Cases
• Internet of Things (smart home, connected devices)
• Education (language learning, training videos)
• Voiced Media (news, blogs, email)
• Voiced Chat Bots (Amazon Lex, Alexa skills)
• Gaming (avatars, Amazon Lumberyard)
#VoiceFirst Movement
23. An Introduction to the AI Services at AWS
Amazon
Rekognition
Computer Vision
Apache
24. Amazon Rekognition: Computer Vision Service
Object and Scene
Detection
Facial
Analysis
Facial
Comparison
Facial
Recognition
28. Amazon Rekognition: Facial Search
Facial
verification
Face
Search
Visual Similarity
Search
(compare two faces) (compare many faces) (find similar faces)
29. Amazon Rekognition: A few use cases
Best photo: use the attributes smile and eyesOpen to determine the best photos to post
Demographic detection: collect the age and gender of customers in your store
Sentiment capture: detect the emotions of your customers as they try your product
A/B tuning: identify visually similar alternatives to high-scoring images for A/B testing
Smart filtering: identify images with high visual similarity to ensure only one is displayed
Verify face: compare two faces, receive a confidence score that they are the same person
Protected images: identify visually similar images that are protected by trademarks
31. The Advent of Conversational Interactions
1st gen: Machine-oriented
interactions
32. The Advent of Conversational Interactions
1st gen: Machine-oriented
interactions
2nd gen: Control-oriented
& translated
33. The Advent of Conversational Interactions
1st gen: Machine-oriented
interactions
2nd gen: Control-oriented
& translated
3rd gen:
Intent-oriented
34. Amazon Lex ... for Conversational Interactions
Powered by the same deep learning technology as Alexa
Enterprise SaaS Connectors
Deployment to chat platforms, like Slack, Facebook
Messenger, Twilio SMS
Build Voice and Text Chatbots
Interactions on mobile, web, and devices
37. Amazon Lex Use Cases
Informational Bots
Chatbots for everyday consumer requests
Application Bots
Build powerful interfaces to mobile applications
• News updates
• Weather information
• Game scores ….
• Book tickets
• Order food
• Manage bank accounts ….
Enterprise Productivity Bots
Streamline enterprise work activities and improve efficiencies
• Check sales numbers
• Marketing performance
• Inventory status ….
Internet of Things (IoT) Bots
Enable conversational interfaces for device interactions
• Wearables
• Appliances
• Auto ….
43. 43@IntelAI
Hardware for DL Workloads
§ Up to 2X better peak performance on
compute-intensive analytics
§ 100x improvement in inference
performance on EC2 C5 instance*
§ NEW C5 more computational power,
lower costs – customers do more with
less
Blazingly Fast Data Access
§ New microarchitecture, hardware
acceleration, Intel® AVX-512
§ 50% more memory than previous
generation
§ Novartis conducted 39 years of
computational chemistry in 9 hours*
High Speed Scalability
§ Up to 1.73x faster completion of
massively parallel research simulations
than the previous generation
§ Seamless data transfer via interconnects
Training AI: Intel® xeon® scalable processor
Best-in-Class Deep Learning Training Performance
Accelerator for training compute density in deep learning centric environments
+
44. 44@IntelAI
Inference in the cloud: amazon & Intel®
Math Kernel Library for Deep Neural Networks
For developers of deep learning frameworks featuring optimized performance on Intel hardware
6.1 2.4 1.2 0.8
679.4
262.5
79.7 73.9
0
200
400
600
800
AlexNet GoogLeNet v1 ResNet-50 Inception v3
Images/Sec
c4.8xlarge MXNet Inference
No MKL MKL
§ Up to 2X better peak performance on compute-intensive analytics
§ 100x improvement in inference performance on EC2 C5 instance*
§ Intel-optimized Caffe, Intel® MKL for high performance distributed training and inference
§ CloudFormation template with AWS services and EC2, CfnCluster, DynamoDB, EBS and Spot Instance support
§ Classify text, train a Convolutional neural network, visualize the training using Tensorboard using BigDL on AWS
45. Intel Confidential
INTEL® IOT GATEWAY REAL TIME ANALYTICSAWS IOT PLATFORM
Amazon EC2
X1
Inference at the edge: AWS & Intel®
cost savings
with scalability
End-to-end interoperability
to scale applications and services
streamlined
manageability and
analytics
Seamless data management
and analytics from thing
to network to cloud
multilayered,
end-to-end security
A chain of trust rooted
in the hardware and linked throughout the
software
46. 46@IntelAI
Libraries, frameworks & tools
Intel® Math Kernel Library
Intel® MLSL
Intel® Data
Analytics
Acceleration
Library
(DAAL)
Intel®
Distribution
Open Source
Frameworks
Intel Deep
Learning SDK
Intel® Computer
Vision SDKIntel® MKL MKL-DNN
High
Level
Overview
Computation
primitives; high
performance math
primitives granting
low level of control
Computation
primitives; free open
source DNN
functions for high-
velocity integration
with deep learning
frameworks
Communication
primitives; building
blocks to scale deep
learning framework
performance over a
cluster
Broad data analytics
acceleration object
oriented library
supporting distributed
ML at the algorithm
level
Most popular and
fastest growing
language for
machine learning
Toolkits driven by
academia and industry
for training machine
learning algorithms
Accelerate deep
learning model design,
training and
deployment
Toolkit to develop &
deploying vision-
oriented solutions that
harness the full
performance of Intel
CPUs and SOC
accelerators
Primary
Audience
Consumed by
developers of higher
level libraries and
Applications
Consumed by
developers of the next
generation of deep
learning frameworks
Deep learning
framework developers
and optimizers
Wider Data Analytics
and ML audience,
Algorithm level
development for all
stages of data analytics
Application
Developers and
Data Scientists
Machine Learning
App Developers,
Researchers and Data
Scientists.
Application Developers
and Data Scientists
Developers who create
vision-oriented
solutions
Example
Usage
Framework
developers call
matrix multiplication,
convolution functions
New framework with
functions developers
call for max CPU
performance
Framework developer
calls functions to
distribute Caffe
training compute
across an Intel® Xeon
Phi™ cluster
Call distributed
alternating least squares
algorithm for a
recommendation
system
Call scikit-learn
k-means function
for credit card fraud
detection
Script and train a
convolution neural
network for image
recognition
Deep Learning training
and model creation,
with optimization for
deployment on
constrained end device
Use deep learning to do
pedestrian detection
…
Find out more at software.intel.com/ai