This document provides an introduction and 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 for 47 voices across 24 languages. Amazon Lex builds conversational interfaces using natural language understanding. Amazon Rekognition provides deep learning capabilities for image analysis, including object detection, facial analysis, face comparison and face recognition.
Hands-on with Rekognition, Polly & Lex - Pop-up Loft TLV 2017Amazon Web Services
This document discusses Amazon's artificial intelligence services, including Amazon Polly for text-to-speech, Amazon Lex for conversational interfaces, and Amazon Rekognition for image and video analysis. It provides overviews of the capabilities and features of each service, such as Polly's 47 text-to-speech voices across 24 languages, Lex's tools for building conversational bots, and Rekognition's face detection, analysis, and recognition tools. Examples and demos of each service are presented to illustrate their functionality.
Enhance customer experience with conversational interfacesAmazon Web Services
This document discusses conversational interfaces and Amazon's services that can help enable them. It summarizes that conversational interfaces are the third generation of user interfaces after punch cards/memory registers and pointers/sliders. It then discusses challenges in building conversational interfaces and outlines Amazon services like Transcribe, Translate, Polly, Comprehend, and Lex that provide capabilities for speech recognition, language translation and understanding, text-to-speech, and building conversational bots and interfaces. Use cases for these services include information bots, customer service, digital assistants, and more.
Building Speech Enabled Products with Amazon Polly & Amazon LexAmazon Web Services
This document provides an introduction to Amazon Polly and Amazon Lex. It discusses the features and functionality of Polly, including its wide selection of voices and languages available as well as its quality, pricing and use cases. It then introduces Amazon Lex, discussing its text and speech language understanding capabilities powered by the same technology as Alexa. It covers Lex's features such as enterprise connectors, deployment to chat services, versioning and aliases. The document concludes with examples of Lex bots and a demo of a "DevOps" chatbot integrated with Slack using Lex and AWS Lambda.
by Keith Steward, Solutions Architect, AWS
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 session combines both of these AWS services, the presenter will demonstrate how to build DevOps and Help Desk chatbots that feature spoken-voice interfaces, and explore the potential of bringing characters to life through interactive chatbots that improves customer engagement. Attendees will be provided with the foundational skills for those looking to enrich their applications with natural, conversational interfaces. Level 300
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.
Learn How to Build a Bot for Voice and Text with Amazon Lex and Amazon PollyAmazon Web Services
This document summarizes a presentation about building bots with Amazon Lex and Amazon Polly. It discusses how Amazon Lex can understand natural language inputs through intents and slots and how Amazon Polly can add life-like speech to bots. It provides examples of building a coffee ordering bot named CoffeeBot that understands orders for different coffee drinks and sizes and confirms orders using synthesized speech from Amazon Polly. The document outlines steps to code the CoffeeBot including creating intents, slots, and utterances in Amazon Lex and integrating it with a Lambda function and AWS Mobile Hub.
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
This document provides an introduction and 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 for 47 voices across 24 languages. Amazon Lex builds conversational interfaces using natural language understanding. Amazon Rekognition provides deep learning capabilities for image analysis, including object detection, facial analysis, face comparison and face recognition.
Hands-on with Rekognition, Polly & Lex - Pop-up Loft TLV 2017Amazon Web Services
This document discusses Amazon's artificial intelligence services, including Amazon Polly for text-to-speech, Amazon Lex for conversational interfaces, and Amazon Rekognition for image and video analysis. It provides overviews of the capabilities and features of each service, such as Polly's 47 text-to-speech voices across 24 languages, Lex's tools for building conversational bots, and Rekognition's face detection, analysis, and recognition tools. Examples and demos of each service are presented to illustrate their functionality.
Enhance customer experience with conversational interfacesAmazon Web Services
This document discusses conversational interfaces and Amazon's services that can help enable them. It summarizes that conversational interfaces are the third generation of user interfaces after punch cards/memory registers and pointers/sliders. It then discusses challenges in building conversational interfaces and outlines Amazon services like Transcribe, Translate, Polly, Comprehend, and Lex that provide capabilities for speech recognition, language translation and understanding, text-to-speech, and building conversational bots and interfaces. Use cases for these services include information bots, customer service, digital assistants, and more.
Building Speech Enabled Products with Amazon Polly & Amazon LexAmazon Web Services
This document provides an introduction to Amazon Polly and Amazon Lex. It discusses the features and functionality of Polly, including its wide selection of voices and languages available as well as its quality, pricing and use cases. It then introduces Amazon Lex, discussing its text and speech language understanding capabilities powered by the same technology as Alexa. It covers Lex's features such as enterprise connectors, deployment to chat services, versioning and aliases. The document concludes with examples of Lex bots and a demo of a "DevOps" chatbot integrated with Slack using Lex and AWS Lambda.
by Keith Steward, Solutions Architect, AWS
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 session combines both of these AWS services, the presenter will demonstrate how to build DevOps and Help Desk chatbots that feature spoken-voice interfaces, and explore the potential of bringing characters to life through interactive chatbots that improves customer engagement. Attendees will be provided with the foundational skills for those looking to enrich their applications with natural, conversational interfaces. Level 300
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.
Learn How to Build a Bot for Voice and Text with Amazon Lex and Amazon PollyAmazon Web Services
This document summarizes a presentation about building bots with Amazon Lex and Amazon Polly. It discusses how Amazon Lex can understand natural language inputs through intents and slots and how Amazon Polly can add life-like speech to bots. It provides examples of building a coffee ordering bot named CoffeeBot that understands orders for different coffee drinks and sizes and confirms orders using synthesized speech from Amazon Polly. The document outlines steps to code the CoffeeBot including creating intents, slots, and utterances in Amazon Lex and integrating it with a Lambda function and AWS Mobile Hub.
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
Learn How to Build a Bot for Voice and Text with Amazon Lex and Amazon Polly ...Amazon Web Services
This document summarizes a presentation about building bots with Amazon Lex and Amazon Polly. It introduces Amazon Lex for building conversational bots and Amazon Polly for converting text to lifelike speech. It provides an example of a "Coffee Bot" that takes voice or text orders for coffee drinks and confirms the order using synthesized speech. The presentation encourages attendees to build their own Coffee Bot by setting up intents and slots in Lex, integrating it with a Lambda function for fulfillment, and using Mobile Hub and CloudWatch for analytics.
Learn How to Build a Bot for Voice and Text with Amazon Lex and Amazon Polly ...Amazon Web Services
Learn How to Build a Bot for Voice and Text with Amazon Lex and Amazon Polly.
- Amazon Polly - life-like speech service
- Amazon Lex - enables developer to build conversational chatbots quickly and easily.
This document provides an agenda and objectives for an Amazon Polly and Amazon Lex workshop. The agenda includes quick overviews of Polly and Lex, hands-on exercises for both Polly and Lex, and a summary. The objectives are to get hands-on experience with Polly and Lex and build a Lex bot. The overviews explain that Polly converts text to speech, Lex builds conversational interfaces, and the exercises provide example bots to build and links to follow.
The document discusses Amazon's artificial intelligence capabilities and services. It describes how Amazon uses artificial intelligence and deep learning across its business from fulfillment to new product development. It also introduces three new AI services from Amazon: Polly for text-to-speech, Rekognition for computer vision and image analysis, and Lex for building conversational interfaces. Each service is described as using deep learning techniques to provide high quality, easy to use and low cost functionality for applications.
This document provides an overview of Amazon's artificial intelligence capabilities including:
- Amazon uses AI across many parts of its business including discovery, search, fulfillment, and enhancing existing and defining new products.
- It discusses several Amazon AI services including Lex for conversational interfaces, Polly for text-to-speech, and Rekognition for image and video analysis.
- The services are powered by deep learning and aimed at applications like voice and chat bots, image labeling, facial recognition and more.
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
Nikko Strom presented on Amazon's Alexa technologies at the AWS Stockholm Summit on May 3, 2017. He discussed Alexa Voice Service which allows developers to integrate Alexa directly into their devices, the Alexa Skills Kit which allows developers to extend Alexa's capabilities, and Amazon's growing catalog of over 10,000 skills. He also covered the Alexa Smart Home Skill API and Amazon Polly text-to-speech service. Strom concluded by discussing Amazon's investments in deep learning for speech recognition and natural language processing through programs like the Alexa Prize and Alexa Fund.
This document summarizes Amazon AI services Amazon Polly and Amazon Rekognition. Amazon Polly is a text-to-speech service that converts text into natural sounding speech using deep learning. It has 47 voices across 24 languages. Amazon Rekognition is an image and video analysis service that can detect objects, scenes, and faces in images and videos. It also has capabilities for facial analysis, facial search, and visual similarity search. Both services are powered by deep learning and are easily accessible and low cost.
This document discusses building AI-powered apps on AWS using services like Polly, Rekognition, and Cognito. It provides demos of text-to-speech with Polly and image analysis with Rekognition. It also discusses using Cognito for user identity management and CodeStar for developing mobile apps.
AWS User Group Singapore / Amazon Lex -- JAWSDAYS 2017Alex Smith
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
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
Building Serverless AI-powered Apps on AWSAdrian Hornsby
This document summarizes a presentation about building serverless AI applications using Amazon Web Services (AWS). It discusses Amazon Polly for text-to-speech, Amazon Rekognition for image analysis, and provides examples of how companies like Duolingo and Bynder use these services. It also demonstrates a sample app called Poliko that uses Polly and Rekognition to take an image, detect faces and labels, and synthesize speech describing the image.
This document provides an overview of Amazon's artificial intelligence and machine learning services, including Polly, Rekognition, and Lex. Polly converts text to lifelike speech in various languages and voices. Rekognition provides image and video analysis capabilities like object detection and facial analysis. Lex builds natural language conversational interfaces. The services integrate with AWS and are aimed at reducing costs and improving capabilities for customers in media, education, and other industries.
Automate for Efficiency with Amazon Transcribe & Amazon Translate: Machine Le...Amazon Web Services
Machine Learning Workshops at the San Francisco Loft
Automate for Efficiency with Amazon Transcribe and Amazon Translate
Teaching a computer how to understand human language is one of the most challenging problems in computer science. However, significant progress has been made in automatic speech recognition (ASR) and machine translation (MT) to create highly accurate and fluent transcriptions and translations. Amazon Transcribe is an ASR service that makes it easy for developers to add speech to text capability to their applications, and Amazon Translate is a MT service that delivers fast, high-quality, and affordable language translation. In this session, you’ll learn how to weave machine translation and transcription into your workflows, to increase the efficiency and reach of your operations.
Level: 200-300
Speaker: Martin Schade - R&D Engineer, AWS Solutions Architecture
On 30 April at European School IV in Brussels, 250 girls from thirty-three schools across Belgium celebrated International Girls in ICT Day 2016 by participating in Belgium’s first-ever Digital Muse “Girl Tech Fest,” an all-day event promoting digital and creative skills…
https://ec.europa.eu/digital-single-market/en/node/87018
The Alexa skills hands-on workshop teaching 11-16 years old about coding in JSON and how to create an alexa skill.
The Girl Tech Fest was featured in the Saturday evening news on BX1 television: http://bx1.be/news/une-journee-pour-promouvoir-la-presence-des-femmes-dans-les-metiers-de-la-technologie/
Advocate for STEM content that relates to girls & work hard to recognize.
Build Intelligent Apps with Amazon ML - Language Services - BDA302 - Chicago ...Amazon Web Services
Amazon brings natural language processing, automatic speech recognition, text-to-speech, and neural machine translation technologies within reach of every developer. In this session, learn how you can easily add intelligence to any application with solution-oriented machine learning (ML) services that provide speech, language, and chatbot functionalities. We also share real-world examples of ML in action. See how others are defining and building the next generation of apps that can hear, speak, understand, and interact with the world around us.
This document provides an introduction to Amazon's artificial intelligence services, including Amazon Polly for text-to-speech, Amazon Rekognition for image and video analysis, and Amazon Lex for conversational interactions. It discusses how these services are powered by deep learning and machine learning techniques and are built to be high quality, easy to use, and cost effective.
AWS Security Best Practices (March 2017)Julien SIMON
The document outlines best practices for AWS security including understanding the shared security model, encrypting data, managing users and permissions, logging activities, and automating security checks. Some key recommendations are to encrypt everything using services like KMS, apply least privilege and rotate credentials for IAM users, enable CloudTrail logging across all regions, and use tools like Inspector and Config to automate security checks.
Deep Dive: Amazon Redshift (March 2017)Julien SIMON
This document provides an overview of optimizing performance in Amazon Redshift. It discusses the architecture of Redshift including columnar storage and compression. It also covers best practices for schema design such as choosing distribution styles, sort keys and column widths. Additional topics include ingestion strategies, regular maintenance of statistics and vacuuming, and workload management using queues.
Learn How to Build a Bot for Voice and Text with Amazon Lex and Amazon Polly ...Amazon Web Services
This document summarizes a presentation about building bots with Amazon Lex and Amazon Polly. It introduces Amazon Lex for building conversational bots and Amazon Polly for converting text to lifelike speech. It provides an example of a "Coffee Bot" that takes voice or text orders for coffee drinks and confirms the order using synthesized speech. The presentation encourages attendees to build their own Coffee Bot by setting up intents and slots in Lex, integrating it with a Lambda function for fulfillment, and using Mobile Hub and CloudWatch for analytics.
Learn How to Build a Bot for Voice and Text with Amazon Lex and Amazon Polly ...Amazon Web Services
Learn How to Build a Bot for Voice and Text with Amazon Lex and Amazon Polly.
- Amazon Polly - life-like speech service
- Amazon Lex - enables developer to build conversational chatbots quickly and easily.
This document provides an agenda and objectives for an Amazon Polly and Amazon Lex workshop. The agenda includes quick overviews of Polly and Lex, hands-on exercises for both Polly and Lex, and a summary. The objectives are to get hands-on experience with Polly and Lex and build a Lex bot. The overviews explain that Polly converts text to speech, Lex builds conversational interfaces, and the exercises provide example bots to build and links to follow.
The document discusses Amazon's artificial intelligence capabilities and services. It describes how Amazon uses artificial intelligence and deep learning across its business from fulfillment to new product development. It also introduces three new AI services from Amazon: Polly for text-to-speech, Rekognition for computer vision and image analysis, and Lex for building conversational interfaces. Each service is described as using deep learning techniques to provide high quality, easy to use and low cost functionality for applications.
This document provides an overview of Amazon's artificial intelligence capabilities including:
- Amazon uses AI across many parts of its business including discovery, search, fulfillment, and enhancing existing and defining new products.
- It discusses several Amazon AI services including Lex for conversational interfaces, Polly for text-to-speech, and Rekognition for image and video analysis.
- The services are powered by deep learning and aimed at applications like voice and chat bots, image labeling, facial recognition and more.
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
Nikko Strom presented on Amazon's Alexa technologies at the AWS Stockholm Summit on May 3, 2017. He discussed Alexa Voice Service which allows developers to integrate Alexa directly into their devices, the Alexa Skills Kit which allows developers to extend Alexa's capabilities, and Amazon's growing catalog of over 10,000 skills. He also covered the Alexa Smart Home Skill API and Amazon Polly text-to-speech service. Strom concluded by discussing Amazon's investments in deep learning for speech recognition and natural language processing through programs like the Alexa Prize and Alexa Fund.
This document summarizes Amazon AI services Amazon Polly and Amazon Rekognition. Amazon Polly is a text-to-speech service that converts text into natural sounding speech using deep learning. It has 47 voices across 24 languages. Amazon Rekognition is an image and video analysis service that can detect objects, scenes, and faces in images and videos. It also has capabilities for facial analysis, facial search, and visual similarity search. Both services are powered by deep learning and are easily accessible and low cost.
This document discusses building AI-powered apps on AWS using services like Polly, Rekognition, and Cognito. It provides demos of text-to-speech with Polly and image analysis with Rekognition. It also discusses using Cognito for user identity management and CodeStar for developing mobile apps.
AWS User Group Singapore / Amazon Lex -- JAWSDAYS 2017Alex Smith
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
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
Building Serverless AI-powered Apps on AWSAdrian Hornsby
This document summarizes a presentation about building serverless AI applications using Amazon Web Services (AWS). It discusses Amazon Polly for text-to-speech, Amazon Rekognition for image analysis, and provides examples of how companies like Duolingo and Bynder use these services. It also demonstrates a sample app called Poliko that uses Polly and Rekognition to take an image, detect faces and labels, and synthesize speech describing the image.
This document provides an overview of Amazon's artificial intelligence and machine learning services, including Polly, Rekognition, and Lex. Polly converts text to lifelike speech in various languages and voices. Rekognition provides image and video analysis capabilities like object detection and facial analysis. Lex builds natural language conversational interfaces. The services integrate with AWS and are aimed at reducing costs and improving capabilities for customers in media, education, and other industries.
Automate for Efficiency with Amazon Transcribe & Amazon Translate: Machine Le...Amazon Web Services
Machine Learning Workshops at the San Francisco Loft
Automate for Efficiency with Amazon Transcribe and Amazon Translate
Teaching a computer how to understand human language is one of the most challenging problems in computer science. However, significant progress has been made in automatic speech recognition (ASR) and machine translation (MT) to create highly accurate and fluent transcriptions and translations. Amazon Transcribe is an ASR service that makes it easy for developers to add speech to text capability to their applications, and Amazon Translate is a MT service that delivers fast, high-quality, and affordable language translation. In this session, you’ll learn how to weave machine translation and transcription into your workflows, to increase the efficiency and reach of your operations.
Level: 200-300
Speaker: Martin Schade - R&D Engineer, AWS Solutions Architecture
On 30 April at European School IV in Brussels, 250 girls from thirty-three schools across Belgium celebrated International Girls in ICT Day 2016 by participating in Belgium’s first-ever Digital Muse “Girl Tech Fest,” an all-day event promoting digital and creative skills…
https://ec.europa.eu/digital-single-market/en/node/87018
The Alexa skills hands-on workshop teaching 11-16 years old about coding in JSON and how to create an alexa skill.
The Girl Tech Fest was featured in the Saturday evening news on BX1 television: http://bx1.be/news/une-journee-pour-promouvoir-la-presence-des-femmes-dans-les-metiers-de-la-technologie/
Advocate for STEM content that relates to girls & work hard to recognize.
Build Intelligent Apps with Amazon ML - Language Services - BDA302 - Chicago ...Amazon Web Services
Amazon brings natural language processing, automatic speech recognition, text-to-speech, and neural machine translation technologies within reach of every developer. In this session, learn how you can easily add intelligence to any application with solution-oriented machine learning (ML) services that provide speech, language, and chatbot functionalities. We also share real-world examples of ML in action. See how others are defining and building the next generation of apps that can hear, speak, understand, and interact with the world around us.
This document provides an introduction to Amazon's artificial intelligence services, including Amazon Polly for text-to-speech, Amazon Rekognition for image and video analysis, and Amazon Lex for conversational interactions. It discusses how these services are powered by deep learning and machine learning techniques and are built to be high quality, easy to use, and cost effective.
AWS Security Best Practices (March 2017)Julien SIMON
The document outlines best practices for AWS security including understanding the shared security model, encrypting data, managing users and permissions, logging activities, and automating security checks. Some key recommendations are to encrypt everything using services like KMS, apply least privilege and rotate credentials for IAM users, enable CloudTrail logging across all regions, and use tools like Inspector and Config to automate security checks.
Deep Dive: Amazon Redshift (March 2017)Julien SIMON
This document provides an overview of optimizing performance in Amazon Redshift. It discusses the architecture of Redshift including columnar storage and compression. It also covers best practices for schema design such as choosing distribution styles, sort keys and column widths. Additional topics include ingestion strategies, regular maintenance of statistics and vacuuming, and workload management using queues.
Deep Dive: Amazon Virtual Private Cloud (March 2017)Julien SIMON
- The document discusses VPC configurations, networking services like ENIs, routing tables, security groups, and network ACLs.
- It provides examples of building hybrid architectures with on-premises networks by creating VPCs, VPN/Direct Connect connections, and routing configurations.
- VPC peering and endpoints are also covered, allowing communication and service access between VPCs in the same or different AWS accounts without an internet gateway.
Advanced Task Scheduling with Amazon ECSJulien SIMON
- Amazon EC2 Container Service (ECS) is a container management service that supports Docker containers and allows scheduling of application containers across compute resources.
- ECS provides two options for task scheduling - using services which let ECS handle scheduling, and implementing a custom scheduler using the ECS API.
- The ECS placement engine allows developers more control over task placement using placement constraints and strategies to target attributes like instance types, availability zones, or custom attributes.
The Marketer's Guide To Customer InterviewsGood Funnel
A step-by-step guide on how to doing customer interviews that reveal revenue-boosting insights. This deck is made exclusively for marketers & copywriters.
The document discusses connecting devices to the Internet of Things (IoT) using AWS IoT. It describes how AWS IoT provides services for connecting devices, securely transmitting and storing device data, and building applications that integrate with connected devices and AWS services. These services include device SDKs, rules engines, device shadowing, authentication/authorization, and a device registry.
Big Data answers in seconds with Amazon AthenaJulien SIMON
This document discusses Amazon Athena, a new serverless query service that allows users to run SQL queries directly on data stored in Amazon S3 without having to load the data into databases or manage servers. With Athena, users can analyze exabyte-scale data using standard SQL and get results within seconds without having to learn complex big data technologies. The document provides an overview of how Athena works, the types of data formats it supports, how to query and analyze data with it, and examples using the GDELT data set to demonstrate its capabilities.
This document summarizes serverless computing using AWS Lambda. It discusses how AWS Lambda allows developers to deploy code without managing infrastructure. Various frameworks are presented that simplify development and deployment of serverless applications using AWS Lambda, including the Serverless framework, Gordon, and the new AWS Serverless Application Model (SAM). Serverless architectures with AWS Lambda provide scalability and pay-per-use pricing for event-driven applications.
This document summarizes diversity data from HubSpot in 2016. It shows the breakdown of employees by gender, age, ethnicity, and management level across different departments. While diversity is still lacking, especially in technical roles and leadership, progress was made in 2016 with increases in female representation and hiring of underrepresented ethnic groups. Continued efforts are needed to create a more inclusive workforce.
Why People Block Ads (And What It Means for Marketers and Advertisers) [New R...HubSpot
HubSpot Research shares new data on why people use ad blockers and what marketers and advertisers need to do to keep people from blocking out ads completely. Hint: it's stop using interruptive and annoying ads.
The lack of visible female role models is pervasive in the tech industry, particularly on Wikipedia, where just under 17% of Wikipedia biographies were on women. That's why HubSpot wrote fourteen Wikipedia entries for remarkable women in tech to help inspire young women to reach positions at the highest levels of STEM.
FrenchWeb 500, le classement des entreprises de la tech françaiseFrenchWeb.fr
Opérateurs télécoms, e-commerçants, acteurs de la publicité: qui sont réellement les acteurs les plus dynamiques de l'écosystème numérique en 2017? C'est la question à laquelle a souhaité répondre FrenchWeb, avec la deuxième édition de son classement FrenchWeb 500.
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.
re:Invent Recap keynote - An introduction to the latest AWS servicesAmazon Web 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.
Building Speech Enabled Products with Amazon Polly & Amazon LexAmazon Web Services
by Dario Rivera, Solutions Architect, AWS
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. Join this session to learn more and find out how you get can started with Amazon Polly, today!
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.
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.
Building Chatbots with Amazon Lex I AWS Dev Day 2018AWS Germany
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.
Building speech enabled products with Amazon Polly & Amazon LexAmazon Web Services
Amazon Lex and Amazon Polly are services for building conversational interfaces and converting text to speech. Lex allows developers to build bots that understand natural language and integrate with back-end systems. It features tools for building conversations using text or speech and deploying bots to messaging platforms. Polly is a text-to-speech service that converts text into high-quality speech for 47 voices and 24 languages. It offers features like SSML and lexicons to customize output. Both services aim to make building conversational applications easier and more cost-effective for developers.
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!
The document provides an overview of artificial intelligence capabilities on AWS, including text-to-speech with Amazon Polly, computer vision with Amazon Rekognition, and conversational interactions with Amazon Lex. It describes several deep learning frameworks and services that can be used to build AI solutions, such as Apache MXNet and Amazon's AI offerings.
An Overview to Artificial Intelligence Services at AWSKristana Kane
The document provides an overview of artificial intelligence capabilities on AWS, including Amazon Polly for text-to-speech, Amazon Rekognition for computer vision, Apache MXNet as a deep learning framework, and Amazon Lex for building conversational bots. It describes their features and common use cases, such as using computer vision for facial analysis and recognition, building conversational interfaces for devices with Amazon Lex, and more.
AWS offers a suite of AI and machine learning services including:
- Rekognition for image and video analysis including object detection, facial recognition and analysis, and image moderation.
- Polly for text-to-speech conversion with many voices and languages.
- Lex for building conversational bots using voice and text across channels like Alexa, Slack, and Facebook Messenger.
- Comprehend for natural language processing including keyword extraction, sentiment analysis, and topic modeling from text.
- SageMaker as a fully managed platform for building, training, and deploying machine learning models at scale.
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
BDA306 NEW LAUNCH! An Introduction to Amazon Lex, your service for building v...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!
This document provides an agenda and overview of Amazon Web Services' artificial intelligence capabilities. It begins with a discussion of the advent of deep learning and how data, GPUs, programming models, and AWS have contributed. It then introduces several AWS AI services - Apache MXNet for deep learning, Polly for text-to-speech, Rekognition for image and video analysis, and Lex for conversational interfaces. It provides demonstrations and examples of each service, and discusses how customers are using them.
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
Raleigh DevDay 2017: Distributed Deep Learning on AWS with Apache MXNetAmazon Web Services
This document provides an overview of artificial intelligence capabilities on AWS, including text-to-speech with Amazon Polly, computer vision with Amazon Rekognition, deep learning with Apache MXNet, and conversational interfaces with Amazon Lex. It discusses common use cases for each service and highlights their key features such as life-like speech synthesis, facial analysis and comparison, scalable deep learning, and building chatbots and voice interfaces.
Unlocking New Todays: Artificial Intelligence and Data Platforms on AWSAmazon Web Services
In this session, you will learn about our strategy for driving machine learning innovation for our customers and learn what’s new from AWS in the machine learning space. We will discuss and demonstrate the latest new services for ML on AWS: Amazon SageMaker, AWS DeepLens, Amazon Rekogntion Video, Amazon Translate, Amazon Transcribe, and Amazon Comprehend. Attend this session to understand how to make the most of machine learning in the cloud.
This document discusses Amazon's artificial intelligence and deep learning capabilities. It summarizes Amazon's AI services including Amazon Lex for building conversational bots, Amazon Polly for text-to-speech, and Amazon Rekognition for computer vision tasks like image moderation, facial analysis, and celebrity recognition. It also discusses Amazon's deep learning framework MXNet and partnerships with Intel for high performance and low cost AI and machine learning.
Steve Shirkey, Solutions Architect, ASEAN, AWS
With the launch of several new Machine Learning (ML) services on AWS, now is your chance to learn how to quickly apply ML to solve real-world business problems, no prior ML experience necessary. During this session, you will learn about vision services to analyze your images and video for facial comparison, object detection and detecting text (Amazon Rekognition and Amazon Rekognition Video), building conversational interfaces for chatbots (Amazon Lex), and core language services for converting audio to text (Amazon Transcribe), converting text to speech (Amazon Polly), identifying topics and themes in text (Amazon Comprehend) and translating between two languages (Amazon Translate).
New Artificial Intelligence and IoT Services (Lex, Polly, Rekognition, Greeng...Amazon Web Services
This document provides a summary of new artificial intelligence and internet of things services from Amazon Web Services that were announced in January 2017. It discusses Amazon Rekognition for image and facial analysis, Amazon Lex for building conversational interfaces, and Amazon Polly for converting text to speech. It also covers AWS Greengrass for running AWS Lambda functions and messaging on devices and the AWS IoT Button Enterprise Program.
The document discusses Amazon Polly, a text-to-speech service that converts text into natural sounding speech in various languages and voices. It describes how Polly offers features like Speech Synthesis Markup Language to control various speech properties and outputs high quality speech. The document also discusses Amazon Rekognition, a deep learning image recognition service that can perform tasks like facial analysis, facial recognition, and object detection on images. Finally, it provides an example use case of using Rekognition and other AWS services to build a smart assistant application that can detect faces, understand speech commands and respond using text-to-speech.
An introduction to computer vision with Hugging FaceJulien SIMON
In this code-level talk, Julien will show you how to quickly build and deploy computer vision applications based on Transformer models. Along the way, you'll learn about the portfolio of open source and commercial Hugging Face solutions, and how they can help you deliver high-quality solutions faster than ever before.
Reinventing Deep Learning with Hugging Face TransformersJulien SIMON
The document discusses how transformers have become a general-purpose architecture for machine learning, with various transformer models like BERT and GPT-3 seeing widespread adoption. It introduces Hugging Face as a company working to make transformers more accessible through tools and libraries. Hugging Face has seen rapid growth, with its hub hosting over 73,000 models and 10,000 datasets that are downloaded over 1 million times daily. The document outlines Hugging Face's vision of facilitating the entire machine learning process from data to production through tools that support tasks like transfer learning, hardware acceleration, and collaborative model development.
Building NLP applications with TransformersJulien SIMON
The document discusses how transformer models and transfer learning (Deep Learning 2.0) have improved natural language processing by allowing researchers to easily apply pre-trained models to new tasks with limited data. It presents examples of how HuggingFace has used transformer models for tasks like translation and part-of-speech tagging. The document also discusses tools from HuggingFace that make it easier to train models on hardware accelerators and deploy them to production.
Building Machine Learning Models Automatically (June 2020)Julien SIMON
This document discusses automating machine learning model building. It introduces AutoML and describes scenarios where it can help build models without expertise, empower more people, and experiment at scale. It discusses the importance of transparency and control. The agenda covers using Amazon SageMaker Studio for zero-code AutoML, Amazon SageMaker Autopilot and SDK for AutoML, and open source AutoGluon. SageMaker Autopilot automates all model building steps and provides a transparent notebook. AutoGluon is an open source AutoML toolkit that can automate tasks for tabular, text, and image data in just a few lines of code.
Starting your AI/ML project right (May 2020)Julien SIMON
In this talk, we’ll see how you can put your AI/ML project on the right track from the get-go. Applying common sense and proven best practices, we’ll discuss skills, tools, methods, and more. We’ll also look at several real-life projects built by AWS customers in different industries and startups.
Scale Machine Learning from zero to millions of users (April 2020)Julien SIMON
This document discusses scaling machine learning models from initial development to production deployment for millions of users. It outlines several options for scaling models from a single instance to large distributed systems, including using Amazon EC2 instances with automation, Docker clusters on ECS/EKS, or the fully managed SageMaker service. SageMaker is recommended for ease of scaling training and inference with minimal infrastructure management required.
An Introduction to Generative Adversarial Networks (April 2020)Julien SIMON
Generative adversarial networks (GANs) use two neural networks, a generator and discriminator, that compete against each other. The generator creates synthetic samples and the discriminator evaluates them as real or fake. This training process allows the generator to produce highly realistic samples. GANs have been used to generate new images like faces, as well as music, dance motions, and design concepts. Resources for learning more about GANs include online courses, books, and example notebooks.
AIM410R1 Deep learning applications with TensorFlow, featuring Fannie Mae (De...Julien SIMON
Fannie Mae leverages Amazon SageMaker for machine learning applications to more accurately value properties and reduce mortgage risk. Amazon SageMaker provides a fully managed service that enables Fannie Mae to focus on modeling while ensuring data security, self-service access, and end-to-end governance through techniques like private subnets, encryption, IAM policies, and operating zones. The presentation demonstrates how to get started with TensorFlow on Amazon SageMaker.
AIM410R Deep Learning Applications with TensorFlow, featuring Mobileye (Decem...Julien SIMON
Mobileye adopted Amazon SageMaker to accelerate its deep learning model development, reducing time from months to under a week. Pipe Mode enabled training on Mobileye's large datasets without copying data to instances. Challenges like data format conversion and shuffling were addressed using SageMaker features and TensorFlow APIs. Adopting SageMaker provided Mobileye unlimited compute and helped simplify and scale its neural network training.
Building smart applications with AWS AI services (October 2019)Julien SIMON
This document discusses Amazon Web Services (AWS) AI and machine learning services. It notes that 40% of digital transformation initiatives in 2019 will involve AI. It then highlights key aspects of AWS AI services, including that they have over 10,000 active customers, that 90% of the roadmap is defined by customer needs, and that there were over 200 new launches or updates in the previous year. It provides examples of various AI services available on AWS.
Build, train and deploy ML models with SageMaker (October 2019)Julien SIMON
The document discusses Amazon SageMaker, a fully managed machine learning platform. It describes how SageMaker allows users to build, train, and deploy machine learning models using various options like built-in algorithms and frameworks. The document provides an overview of key SageMaker capabilities like notebook instances, APIs, training options, and frameworks. It also includes a demo of image classification using Keras/TensorFlow with SageMaker Script Mode and managed spot training.
The document discusses best practices for AI/ML projects based on past failures to understand disruptive technologies. It recommends (1) setting clear expectations and metrics, (2) assessing skills needed, (3) choosing the right tools based on cost, time and accuracy tradeoffs, (4) using best practices like iterative development, and (5) repeating until gains become irrelevant before moving to the next project.
Building Machine Learning Inference Pipelines at Scale (July 2019)Julien SIMON
Talk at OSCON, Portland, 18/07/2019
Real-life Machine Learning applications require more than a single model. Data may need pre-processing: normalization, feature engineering, dimensionality reduction, etc. Predictions may need post-processing: filtering, sorting, combining, etc.
Our goal: build scalable ML pipelines with open source (Spark, Scikit-learn, XGBoost) and managed services (Amazon EMR, AWS Glue, Amazon SageMaker)
Train and Deploy Machine Learning Workloads with AWS Container Services (July...Julien SIMON
The document discusses different options for deploying machine learning workloads, including using EC2 instances, ECS/EKS clusters, Fargate, and Amazon SageMaker. It provides pros and cons for each option based on infrastructure effort, machine learning setup effort, CI/CD integration, ability to build, train and deploy models at scale, optimize costs, and security. The conclusion recommends choosing based on current business needs, mixing and matching options, and focusing on machine learning rather than infrastructure. SageMaker is presented as requiring the least infrastructure work to get started with machine learning.
Optimize your Machine Learning Workloads on AWS (July 2019)Julien SIMON
Talk at Floor 28, Tel Aviv.
Infrastructure, tips to speed up training, hyperparameter optimization, model compilation, Amazon SageMaker Neo, cost optimization, Amazon Elastic Inference
Build, train and deploy ML models with Amazon SageMaker (May 2019)Julien SIMON
The document discusses HID Global's use of Amazon SageMaker to develop machine learning models for gesture recognition in access control. HID Global collected data on user gestures and used SageMaker to build, train, and deploy tree-based ensemble models to reduce false positives and provide a better user experience. The models were deployed on mobile devices using techniques like Neo to accelerate inference. Overall, SageMaker helped HID Global develop more accurate predictive models for physical access control.
Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.
CAKE: Sharing Slices of Confidential Data on BlockchainClaudio Di Ciccio
Presented at the CAiSE 2024 Forum, Intelligent Information Systems, June 6th, Limassol, Cyprus.
Synopsis: Cooperative information systems typically involve various entities in a collaborative process within a distributed environment. Blockchain technology offers a mechanism for automating such processes, even when only partial trust exists among participants. The data stored on the blockchain is replicated across all nodes in the network, ensuring accessibility to all participants. While this aspect facilitates traceability, integrity, and persistence, it poses challenges for adopting public blockchains in enterprise settings due to confidentiality issues. In this paper, we present a software tool named Control Access via Key Encryption (CAKE), designed to ensure data confidentiality in scenarios involving public blockchains. After outlining its core components and functionalities, we showcase the application of CAKE in the context of a real-world cyber-security project within the logistics domain.
Paper: https://doi.org/10.1007/978-3-031-61000-4_16
OpenID AuthZEN Interop Read Out - AuthorizationDavid Brossard
During Identiverse 2024 and EIC 2024, members of the OpenID AuthZEN WG got together and demoed their authorization endpoints conforming to the AuthZEN API
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/building-and-scaling-ai-applications-with-the-nx-ai-manager-a-presentation-from-network-optix/
Robin van Emden, Senior Director of Data Science at Network Optix, presents the “Building and Scaling AI Applications with the Nx AI Manager,” tutorial at the May 2024 Embedded Vision Summit.
In this presentation, van Emden covers the basics of scaling edge AI solutions using the Nx tool kit. He emphasizes the process of developing AI models and deploying them globally. He also showcases the conversion of AI models and the creation of effective edge AI pipelines, with a focus on pre-processing, model conversion, selecting the appropriate inference engine for the target hardware and post-processing.
van Emden shows how Nx can simplify the developer’s life and facilitate a rapid transition from concept to production-ready applications.He provides valuable insights into developing scalable and efficient edge AI solutions, with a strong focus on practical implementation.
Generating privacy-protected synthetic data using Secludy and MilvusZilliz
During this demo, the founders of Secludy will demonstrate how their system utilizes Milvus to store and manipulate embeddings for generating privacy-protected synthetic data. Their approach not only maintains the confidentiality of the original data but also enhances the utility and scalability of LLMs under privacy constraints. Attendees, including machine learning engineers, data scientists, and data managers, will witness first-hand how Secludy's integration with Milvus empowers organizations to harness the power of LLMs securely and efficiently.
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Things to Consider When Choosing a Website Developer for your Website | FODUUFODUU
Choosing the right website developer is crucial for your business. This article covers essential factors to consider, including experience, portfolio, technical skills, communication, pricing, reputation & reviews, cost and budget considerations and post-launch support. Make an informed decision to ensure your website meets your business goals.
Best 20 SEO Techniques To Improve Website Visibility In SERPPixlogix Infotech
Boost your website's visibility with proven SEO techniques! Our latest blog dives into essential strategies to enhance your online presence, increase traffic, and rank higher on search engines. From keyword optimization to quality content creation, learn how to make your site stand out in the crowded digital landscape. Discover actionable tips and expert insights to elevate your SEO game.
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdfMalak Abu Hammad
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
Amazon AI (March 2017)
1. Hands-on with Amazon AI
Julien Simon"
Principal Technical Evangelist
julsimon@amazon.fr
@julsimon
2. Artificial Intelligence At Amazon
Thousands Of Employees Across The Company Focused on AI
Discovery &
Search
Fulfilment &
Logistics
Enhance
Existing Products
Define New
Categories Of
Products
Bring Machine
Learning To All
3.
4. Amazon AI: Three New Deep Learning Services
Polly
Rekognition
Lex
Life-like Speech
Image Analysis
Conversational
Engine
6. What is 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
7. Amazon Polly: Quality
Natural sounding speech
A subjective measure of how close TTS output is to human speech.
Accurate text processing
Ability of the system to interpret common text formats such as abbreviations, numerical
sequences, homographs etc.
Today in Las Vegas, NV it's 54°F.
"We live for the music", live from the Madison Square Garden.
Highly intelligibile
A measure of how comprehensible speech is.
”Peter Piper picked a peck of pickled peppers.”
8. Amazon Polly: Language Portfolio
Americas:
• Brazilian Portuguese
• Canadian French
• English (US)
• Spanish (US)
A-PAC:
• Australian English
• Indian English
• Japanese
EMEA:
• British English
• Danish
• Dutch
• French
• German
• Icelandic
• Italian
• Norwegian
• Polish
• Portuguese
• Romanian
• Russian
• Spanish
• Swedish
• Turkish
• Welsh
• Welsh English
9. Amazon Polly features: SSML
Speech Synthesis Markup Language
is a W3C recommendation, an XML-based markup language for speech
synthesis applications
<speak>
My name is Kuklinski. It is spelled
<prosody rate='x-slow'>
<say-as interpret-as="characters">Kuklinski</say-as>
</prosody>
</speak>
10. Amazon Polly features: Lexicons
Enables developers to customize the pronunciation of words or
phrases
My daughter’s name is Kaja.
<lexeme>
<grapheme>Kaja</grapheme>
<grapheme>kaja</grapheme>
<grapheme>KAJA</grapheme>
<phoneme>"kaI.@</phoneme>
</lexeme>
11. TEXT
Market grew by > 20%.
WORDS
PHONEMES
{
{
{
{
{
ˈtwɛn.ti
pɚ.ˈsɛnt
ˈmɑɹ.kət
ˈgɹu
baɪ
ˈmoʊɹ
ˈðæn
PROSODY CONTOUR
UNIT SELECTION AND ADAPTATION
TEXT PROCESSING
PROSODY MODIFICATION
STREAMING
Market
grew
by
more
than
twenty
percent
Speech units
inventory
17. Lex Bot Structure
Utterances
Spoken or typed phrases that invoke
your intent
BookHotel
Intents
An Intent performs an action in
response to natural language user input
Slots
Slots are input data required to fulfill the
intent
Fulfillment
Fulfillment mechanism for your intent
18. Utterances
I’d like to book a hotel
I want to make my hotel reservations
I want to book a hotel in New York City
Can you help me book my hotel?
19. Slots
Destination
City
New York City, Seattle, London, …
Slot
Type
Values
Check In
Date
Valid dates
Check Out
Date
Valid dates
20. Slot Elicitation
I’d like to book a hotel
What date do you check in?
New York City
Sure what city do you want to book?
Nov 30th
Check In
11/30/2016
City
New York City
21. Fulfillment
AWS Lambda Integration
Return to Client
User input parsed to derive
intents and slot values. Output
returned to client for further
processing.
Intents and slots passed to
AWS Lambda function for
business logic
implementation.
22. “Book a Hotel”
Book
Hotel
NYC
“Book a Hotel in
NYC”
Automatic Speech
Recognition
Hotel Booking
New York City
Natural Language
Understanding
Intent/Slot
Model
Utterances
Hotel Booking
City New York City
Check In Nov 30th
Check Out Dec 2nd
“Your hotel is booked for Nov
30th”
Polly
Confirmation: “Your hotel is
booked for Nov 30th”
a
in
“Can I go ahead with
the booking?
23. Amazon Lex - Technology
Amazon Lex
Automatic Speech
Recognition (ASR)
Natural Language
Understanding (NLU)
Same technology that powers Alexa
Cognito
CloudTrail
CloudWatch
AWS Services
Action
AWS Lambda
Authentication
& Visibility
Speech
API
Language
API
Fulfillment
End-Users
Developers
Console
SDK
Intents,
Slots,
Prompts,
Utterances
Input:
Speech
or Text
Multi-Platform Clients:
Mobile, IoT, Web,
Chat
API
Response:
Speech (via Polly TTS)
or Text
26. 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