AWS re:Invent 2016: NEW LAUNCH! Workshop: Hands on with Amazon Lex, Amazon Polly, and Amazon Rekognition (MAC308)
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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 reach of every developer. Amazon Lex make it easy to build sophisticated text and voice chatbots, powered by Alexa; Amazon Rekognition provides deep learning-based image recognition; and Amazon Polly turns text into lifelike speech. In this workshop, you'll get a chance to use each of the new deep learning services. We'll see you there!
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Amazon Web Services proporciona una amplia gama de servicios que le ayudarán a crear e implementar aplicaciones de análisis de big data de forma rápida y sencilla. AWS ofrece un acceso rápido a recursos de TI económicos y flexibles, algo que permitirá escalar prácticamente cualquier aplicación de big data con rapidez, incluidos almacenamiento de datos, análisis de clics, detección de elementos fraudulentos, motores de recomendación, proceso ETL impulsado por eventos, informática sin servidor y procesamiento del Internet de las cosas.
SRV203 Getting Started with AWS Lambda and the Serverless Cloud
Serverless computing allows you to build and run applications without the need for provisioning or managing servers. With serverless computing, you can build web, mobile, and IoT backends; run stream processing or big data workloads; run chatbots, and more. In this session, you'll learn how to get started with serverless computing with AWS Lambda, which lets you run code without provisioning or managing servers. We'll introduce you to the basics of building with Lambda and how you can benefit from features such as continuous scaling, built-in high availability, integrations with AWS and third-party apps, and subsecond metering pricing. We'll also introduce you to the broader portfolio of AWS services that help you build serverless applications with Lambda, including Amazon API Gateway, Amazon DynamoDB, AWS Step Functions, and more.
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AWS re:Invent 2016: NEW LAUNCH! Introducing Amazon Lex (MAC304)
Amazon Lex is a service for building conversational interfaces into any applications using voice and text. With Lex, the same deep learning engine that powers Amazon Alexa is now available to any developer, enabling you to build sophisticated, natural language chatbots into your new and existing applications. Amazon Lex provides the deep functionality and flexibility of natural language understanding (NLU) and automatic speech recognition (ASR) to allow you to build highly engaging user experiences with lifelike, conversational interactions. In this introductory session, find out how Lex provides deep functionality and flexibility to empower you to define entirely new categories of products that are made possible through conversational interfaces.
Deep Dive: Amazon Lex
Amazon Lex is a service for building conversational interfaces into any application using voice and text.
把您的 Amazon Lex Chatbot 與訊息服務集成
Amazon Lex 提供與 Slack、Twilio、Marketo、Salesforce、Quickbooks、Microsoft Dynamics、Zendesk 和 Hubspot 的內置集成。除此之外，您還可以透過組合 Lex API 和 AWS API Gateway 將 Amazon Lex 與任何其他應用程式進行集成，毫不費力的將您的聊天機器人擴展到幾乎任何用例。
這場線上研討會，我們將向您展示如何做到這點。 我們所演示的設計模式能還您學習到如何在 Lex 上構建預處理層或將信息送到到多個特定的聊天機器人。
Building a Chatbot with Amazon Lex and AWS Lambda Workshop
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.
Building ChatBots with Amazon Lex - AWS Summit Tel Aviv 2017
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.
Announcing Amazon Lex - January 2017 AWS Online Tech Talks
Amazon Lex is a service for building conversational interfaces into any application using voice and text. 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.
Learning Objectives:
• Learn about the capabilities and features of Amazon Lex
• Learn about the benefits of Amazon Lex
• Learn about the different use cases
• Learn how to get started using Amazon Lex
AWS Artificial Intelligence Day - Toronto
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.
Amazon Web Services - Strategy and Current Offering
This document provides an overview of Amazon's AI and machine learning strategy and current offerings. It begins with an introduction from Guy Ernest of Amazon AI and discusses why companies talk about AI and machine learning. It then provides an overview of Amazon's machine learning services and platforms, including Amazon Machine Learning, Amazon AI, Deep Learning AMI, and services like EC2, ECS, EMR, Redshift, and Athena that can be used for machine learning workloads. It discusses the machine learning workflow and how different Amazon services fit into different parts of that workflow. It also discusses Amazon's approach to pricing for machine learning services.
Integrate Your Amazon Lex Chatbot with Any Messaging Service
Amazon Lex offers built-in integrations with Slack, Twilio, Marketo, Salesforce, Quickbooks, Microsoft Dynamics, Zendesk, and Hubspot. But, you can also integrate with any other application by combining the Lex API and AWS API Gateway to extend your chatbots into virtually any use case with minimal effort.
This session will show you how. The design pattern shown will be interesting to folks who want to build a pre-processing layer in front of Lex or want to route messages to multiple specialized bots.
Getting Started with Amazon Lex - AWS Summit Cape Town 2017
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
Introducing Amazon Lex
Randall Hunt introduced Amazon Lex, a new service for building conversational interfaces using voice and text. Amazon Lex provides features like text and speech language understanding powered by the same technology as Alexa, deployment to chat services like Facebook Messenger, and integration with other AWS services. It is designed to make building conversational interfaces more efficient for developers. Amazon Lex aims to help developers build powerful applications like informational, application, and enterprise productivity bots as well as bots for internet of things devices.
re:Invent Recap keynote - An introduction to the latest AWS services
The document provides an overview of Amazon's AI services including Amazon Polly, Amazon Lex, and Amazon Rekognition. Amazon Polly is a text-to-speech service that converts text into natural-sounding speech in multiple languages. Amazon Lex builds conversational interfaces into any application using voice and text. Amazon Rekognition provides image and video analysis using deep learning techniques including object detection, scene interpretation, facial analysis and comparison.
Integrate Your Amazon Lex Chatbot with Any Messaging Service - AWS Online Tec...
Learning Objectives:
- Learn about Amazon Lex and associated concepts such as intents and utterances
- Learn how to setup and configure Amazon API Gateway
- How to leverage AWS Lambda as the compute layer in front of Amazon Lex
Integrate Your Amazon Lex Chatbot with Any Messaging Service - May 2017 AWS O...
Learning Objectives:
- Learn how to setup and configure AWS API Gateway
- Learn how to leverage AWS Lambda as the compute layer in front of Amazon Lex
- Learn about Multi-bot architecture
Amazon Lex offers built-in integrations with Slack, Twilio, Marketo, Salesforce, Quickbooks, Microsoft Dynamics, Zendesk, and Hubspot. But, you can also integrate with any other application by combining the Lex API and AWS API Gateway to extend your chatbots into virtually any use case with minimal effort. This session will show you how. The design pattern shown will be interesting to folks who want to build a pre-processing layer in front of Lex or want to route messages to multiple specialized bots.
Building a Better .NET Bot with AWS Services - WIN205 - re:Invent 2017
With the recent introduction of AWS Tools for Visual Studio Team Services, .NET developers have more ways than ever to easily use AWS services for their .NET applications. In this workshop, we run through building a .NET chatbot as we take advantage of AWS Lambda and Amazon Lex. The best part? You can build and deploy the chatbot directly to AWS without ever leaving Visual Studio.
WIN205-Building a Better .NET Bot with AWS Services
With the recent introduction of AWS Tools for Visual Studio Team Services, .NET developers have more ways than ever to easily use AWS services for their .NET applications. In this workshop, we run through building a .NET chatbot as we take advantage of AWS Lambda and Amazon Lex. The best part? You can build and deploy the chatbot directly to AWS without ever leaving Visual Studio.
Building Chatbots with Amazon Lex I AWS Dev Day 2018
Amazon Lex is a service that allows developers to build conversational interfaces into any application using voice and text. It uses the same deep learning technology as Amazon Alexa to understand user intent and respond conversationally. Developers can build bots using Lex that can be deployed to messaging platforms and mobile apps. Lex provides tools to efficiently create conversations through utterances, intents, slots and fulfillment modeling while automatically scaling.
AWS User Group Singapore / Amazon Lex -- JAWSDAYS 2017
In this presentation, we cover the growth and experience of the AWS User Group Singapore. The second half covers the use of Amazon Lex to augment User Group activities
This was originally delivered at JAWSDAYS 2017 Tokyo:- http://jawsdays2017.jaws-ug.jp/session/1337/
Engage your users with a natural language conversational interface using voice and text.
You will learn how to:
– Create a chat bot to understand your users’ intentions and fulfil their requests.
– Engage in a conversation to extract key pieces of data from the user
– Fulfil the users’ intentions with AWS Lambda functions
– Integrate with Facebook Messenger
Chatbots with Serverless
An introductory presentation on Chatbots with Serverless (AWS Lambda). It covers AWS Lez, its terminologies and AWS Lambda in detail. It also showcases on how to connect your Lex bot to Facebook Messenger.
AWS re:Invent 2016: bots + serverless = ❤ (SVR304)
Chatbots are the new apps. Businesses of all sizes, from startups to enterprises, are looking for new ways to connect with their users through natural, conversational interfaces. Developers have started using chatbots to improve the productivity and efficiency of their operations. In this session, we show how to use AWS Lambda and other serverless offerings from AWS to build chatbots quickly and efficiently. We share examples from our recently concluded AWS Slack Hackathon with a full walkthrough of building a conversational chatbot in an easy, fast, and fun way, along with helpful tools, tips, and techniques.
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Building ChatBots with Amazon Lex - AWS Summit Tel Aviv 2017
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Announcing Amazon Lex - January 2017 AWS Online Tech Talks
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Amazon Web Services - Strategy and Current Offering
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Integrate Your Amazon Lex Chatbot with Any Messaging Service
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re:Invent Recap keynote - An introduction to the latest AWS services
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Building a Better .NET Bot with AWS Services - WIN205 - re:Invent 2017
WIN205-Building a Better .NET Bot with AWS Services
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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.
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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.
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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
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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.
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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!
Open banking as a service
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
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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.
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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.
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Vuoi conoscere le opzioni per eseguire Microsoft Active Directory su AWS? Quando si spostano carichi di lavoro Microsoft in AWS, è importante considerare come distribuire Microsoft Active Directory per supportare la gestione, l'autenticazione e l'autorizzazione dei criteri di gruppo. In questa sessione, discuteremo le opzioni per la distribuzione di Microsoft Active Directory su AWS, incluso AWS Directory Service per Microsoft Active Directory e la distribuzione di Active Directory su Windows su Amazon Elastic Compute Cloud (Amazon EC2). Trattiamo argomenti quali l'integrazione del tuo ambiente Microsoft Active Directory locale nel cloud e l'utilizzo di applicazioni SaaS, come Office 365, con AWS Single Sign-On.
Computer Vision con AWS
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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.
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Amazon QLDB elimina la necessità di costruire sistemi personalizzati e complessi fornendo un database ledger serverless completamente gestito.
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5th Power Grid Model Meet-up
It is with great pleasure that we extend to you an invitation to the 5th Power Grid Model Meet-up, scheduled for 6th June 2024. This event will adopt a hybrid format, allowing participants to join us either through an online Mircosoft Teams session or in person at TU/e located at Den Dolech 2, Eindhoven, Netherlands. The meet-up will be hosted by Eindhoven University of Technology (TU/e), a research university specializing in engineering science & technology.
Power Grid Model
The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
Power Grid Model is an open source project from Linux Foundation Energy and provides a calculation engine that is increasingly essential for DSOs. It offers a standards-based foundation enabling real-time power systems analysis, simulations of electrical power grids, and sophisticated what-if analysis. In addition, it enables in-depth studies and analysis of the electrical power grid’s behavior and performance. This comprehensive model incorporates essential factors such as power generation capacity, electrical losses, voltage levels, power flows, and system stability.
Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.
What to expect
For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
-Insightful presentations covering two practical applications of the Power Grid Model.
-An update on the latest advancements in Power Grid -Model technology during the first and second quarters of 2024.
-An interactive brainstorming session to discuss and propose new feature requests.
-An opportunity to connect with fellow Power Grid Model enthusiasts and users.
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For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/temporal-event-neural-networks-a-more-efficient-alternative-to-the-transformer-a-presentation-from-brainchip/
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Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
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During the hour, we’ll take you through:
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How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
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With Skybuffer AI, various AI models can be integrated into a single communication channel such as Microsoft Teams. This integration empowers business users with insights drawn from SAP backend systems, enterprise documents, and the expansive knowledge of Generative AI. And the best part of it is that it is all managed through our intuitive no-code Action Server interface, requiring no extensive coding knowledge and making the advanced AI accessible to more users.
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AWS re:Invent 2016: NEW LAUNCH! Workshop: Hands on with Amazon Lex, Amazon Polly, and Amazon Rekognition (MAC308)
- 1. © 2016, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon AI November 30, 2016 NEW LAUNCH! Workshop: Hands on with Amazon Lex, Amazon Polly, and Amazon Rekognition MAC308
- 2. Amazon Polly • A service that converts text into lifelike speech • Offers 47 lifelike voices across 24 languages • Low latency responses enable developers to build real-time systems • Developers can store, replay and distribute generated speech
- 4. AWS Blog
- 5. Architecture RSS Feed Amazon Polly Amazon CloudWatch Amazon S3AWS Lambda 1. Trigger 2. Check 3. Content 4. Text 5. Audio 6. Audio
- 7. Source from boto3 import Session, resource from contextlib import closing polly = Session().client(”polly") response = polly.synthesize_speech( Text="Sample content", OutputFormat="mp3", VoiceId="Joanna") with closing(response["AudioStream"]) as stream: bucket = resource("s3").Bucket("podcasts") bucket.put_object(Key="output.mp3", Body=stream.read())
- 8. Summary RSS Feed Amazon Polly Amazon CloudWatch Amazon S3AWS Lambda 1. Trigger 2. Check 3. Content 4. Text 5. Audio 6. Audio
- 10. Other use cases
- 11. Thank you!
- 12. What to Expect from the Session Introduction to Amazon Lex Create a movie bot to book tickets Build Lex bot Configure Lambda Functions Wire Lex bot to Lambda Functions Create Cognito Federated Identity Pool Create Android App
- 13. Amazon Lex: A new service for building conversational interfaces using voice and text
- 14. 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 ….
- 16. Architecture Mobile App Amazon Lex Speech Recognition & Natural Language understanding AWS Lambda: Business Logic Amazon CloudWatch Monitoring User input Polly TTS Speech Text ….. ….. DynamoDB SNS 1:”Where is Trolls playing tomorrow?” 2: Amazon Lex recognizes that the user wants to look up movie show times 4: Movie times are retrieved via searchMovies () API 5: “Trolls is playing at Movie Palace 16” 3: Amazon Lex engages in multi-turn conversation to get additional information such as city, time, etc
- 18. Let’s build!
- 19. Amazon Lex Benefits High quality text and speech language understanding Built-in integration with the AWS platform Seamlessly deploy and scale Easy to use Cost effective
- 20. How Do I Get Started Using Amazon Lex?
- 21. Register for the Preview @ aws.amazon.com/lex Sign-up & whitelist Build your first bot! ✔ ✔ ✔
- 22. Thank you!
- 24. Q & A
- 25. Thank you!
- 26. Remember to complete your evaluations!
- 27. Related Sessions DCS205 - Workshop: Building Serverless Bots on AWS – Botathon