The document discusses machine learning and artificial intelligence services provided by Amazon Web Services (AWS). It begins with an overview of AWS's global infrastructure and machine learning capabilities. It then describes several AWS application services for machine learning like Amazon Rekognition (image analysis), Amazon Polly (text-to-speech), Amazon Translate (machine translation), and Amazon SageMaker (machine learning platform). Finally, it discusses machine learning frameworks and infrastructure supported by AWS and provides examples of customers using AWS machine learning services.
The document discusses Amazon's AI services for building machine learning models including application services, platform services, and frameworks/infrastructure. It describes several Amazon AI services such as Amazon Rekognition for computer vision, Amazon Polly for text-to-speech, Amazon Lex for conversational interfaces, and Amazon SageMaker for training and deploying models. The services provide APIs, tools, and capabilities to developers and data scientists to incorporate AI into their applications and analyze large datasets.
Introduction to AWS Travel by Massimo MorinSameer Kenkare
The document discusses how AWS is helping companies in the travel industry innovate through leveraging data, machine learning, and personalization. It highlights trends in travel like connected customer experiences and operational efficiency. Examples are given of airlines like Qantas using AWS to gain customer insights and Ryanair rebuilding applications on AWS to personalize travel experiences. The conclusion encourages travel companies to focus on differentiating through customers and having an ambitious innovation plan using AWS's 13+ years of experience.
The document discusses Amazon Web Services' artificial intelligence services. It provides an overview of Amazon Rekognition for image and video analysis, Amazon Polly for text-to-speech, Amazon Transcribe for speech recognition, Amazon Translate for language translation, and Amazon Lex for conversational interfaces. The document highlights key features and capabilities of each service, including examples of real-world customers using the services. It emphasizes that the services provide high-quality AI through best-in-class deep learning models, with easy-to-use and production-ready interfaces at low cost.
The document discusses Amazon Web Services machine learning services including Amazon Rekognition (image and video analysis), Amazon Polly (text-to-speech), Amazon Transcribe (speech recognition), Amazon Comprehend (natural language processing), and Amazon Translate (machine translation). It provides examples of how developers can use these services to build applications that see, hear, speak, understand and translate content. The services are part of AWS's aim to put machine learning in the hands of every developer.
Automate for Efficiency with Amazon Transcribe & Amazon TranslateAmazon Web Services
by Pratap Ramamurthy, Partner Solutions Architect, AWS
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.
Getting Started with AWS AI Managed Services and SagemakerAmazon Web Services
Chan Sze-Lok, Startup Business Development Manager, AWS
Amazon.com uses Artificial Intelligence to improve customer experience, grow its business and optimize its operations. AWS AI managed services make this powerful AI technology available to every business in the form of simple-to-use services. Attendees will learn how their business can start using powerful AI such as facial recognition, chatbots and sentiment analysis. AWS AI managed services allow customers to get started without any data science expertise, benefiting from technology first tested in the scale and mission critical environment of Amazon.com.
AWS STARTUP DAY 2018 I Enhancing Your Startup With Amazon Machine LearningAWS Germany
Learn how to easily add Amazon AI services to your own applications. Find out how to access image and video analysis, text to speech, speech to text, translation, natural language processing: all of which are just an API call away. You'll learn about Amazon SageMaker, Amazon Translate, Amazon Polly, Amazon Transcribe, Amazon Comprehend, Amazon Rekognition, we'll show you how to quickly get started with these services, with zero AI expertise required.
The document discusses Amazon's artificial intelligence services for machine learning including computer vision, natural language processing, speech recognition, and text-to-speech. It provides examples of Amazon Rekognition for image and video analysis, Amazon Polly for text-to-speech, Amazon Transcribe for speech recognition, Amazon Translate for language translation, Amazon Lex for conversational interfaces, and Amazon Comprehend for natural language processing. The services are designed to be high quality, easy to use, integrated, and low cost for production machine learning applications.
The document discusses Amazon's AI services for building machine learning models including application services, platform services, and frameworks/infrastructure. It describes several Amazon AI services such as Amazon Rekognition for computer vision, Amazon Polly for text-to-speech, Amazon Lex for conversational interfaces, and Amazon SageMaker for training and deploying models. The services provide APIs, tools, and capabilities to developers and data scientists to incorporate AI into their applications and analyze large datasets.
Introduction to AWS Travel by Massimo MorinSameer Kenkare
The document discusses how AWS is helping companies in the travel industry innovate through leveraging data, machine learning, and personalization. It highlights trends in travel like connected customer experiences and operational efficiency. Examples are given of airlines like Qantas using AWS to gain customer insights and Ryanair rebuilding applications on AWS to personalize travel experiences. The conclusion encourages travel companies to focus on differentiating through customers and having an ambitious innovation plan using AWS's 13+ years of experience.
The document discusses Amazon Web Services' artificial intelligence services. It provides an overview of Amazon Rekognition for image and video analysis, Amazon Polly for text-to-speech, Amazon Transcribe for speech recognition, Amazon Translate for language translation, and Amazon Lex for conversational interfaces. The document highlights key features and capabilities of each service, including examples of real-world customers using the services. It emphasizes that the services provide high-quality AI through best-in-class deep learning models, with easy-to-use and production-ready interfaces at low cost.
The document discusses Amazon Web Services machine learning services including Amazon Rekognition (image and video analysis), Amazon Polly (text-to-speech), Amazon Transcribe (speech recognition), Amazon Comprehend (natural language processing), and Amazon Translate (machine translation). It provides examples of how developers can use these services to build applications that see, hear, speak, understand and translate content. The services are part of AWS's aim to put machine learning in the hands of every developer.
Automate for Efficiency with Amazon Transcribe & Amazon TranslateAmazon Web Services
by Pratap Ramamurthy, Partner Solutions Architect, AWS
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.
Getting Started with AWS AI Managed Services and SagemakerAmazon Web Services
Chan Sze-Lok, Startup Business Development Manager, AWS
Amazon.com uses Artificial Intelligence to improve customer experience, grow its business and optimize its operations. AWS AI managed services make this powerful AI technology available to every business in the form of simple-to-use services. Attendees will learn how their business can start using powerful AI such as facial recognition, chatbots and sentiment analysis. AWS AI managed services allow customers to get started without any data science expertise, benefiting from technology first tested in the scale and mission critical environment of Amazon.com.
AWS STARTUP DAY 2018 I Enhancing Your Startup With Amazon Machine LearningAWS Germany
Learn how to easily add Amazon AI services to your own applications. Find out how to access image and video analysis, text to speech, speech to text, translation, natural language processing: all of which are just an API call away. You'll learn about Amazon SageMaker, Amazon Translate, Amazon Polly, Amazon Transcribe, Amazon Comprehend, Amazon Rekognition, we'll show you how to quickly get started with these services, with zero AI expertise required.
The document discusses Amazon's artificial intelligence services for machine learning including computer vision, natural language processing, speech recognition, and text-to-speech. It provides examples of Amazon Rekognition for image and video analysis, Amazon Polly for text-to-speech, Amazon Transcribe for speech recognition, Amazon Translate for language translation, Amazon Lex for conversational interfaces, and Amazon Comprehend for natural language processing. The services are designed to be high quality, easy to use, integrated, and low cost for production machine learning applications.
Artificial Intelligence for Developers - OOP MunichBoaz Ziniman
Artificial Intelligence (AI) services on the AWS cloud bring the experience of Amazon and power of deep learning within reach of every developer, allowing us to develop new tools and enrich our systems with new capabilities. In this session, we will look into the history of AI at Amazon and explore the opportunities to apply one or more of the AI services, provide a number of examples and use cases to help you get started.
Introduction to Amazon Go and Amazon Go Tour by Humphrey ChanSameer Kenkare
Humphrey Chen is a senior manager at Amazon Rekognition. The document discusses Amazon's machine learning services including Rekognition, which provides image and video analysis features like facial recognition and analysis, celebrity recognition, label detection, moderation, and text detection. It also discusses Amazon Textract, which simplifies extracting text, tables, and forms from documents without needing code or templates.
Build Text Analytics Solutions with Amazon Comprehend and Amazon TranslateAmazon Web Services
by Pratap Ramamurthy, Partner Solutions Architect, AWS
Natural language holds a wealth of information like user sentiment and conversational intent. In this session, we'll demonstrate the capabilities of Amazon Comprehend, a natural language processing (NLP) service that uses machine learning to find insights and relationships in text. We'll show you how to build a VOC (Voice of the Customer) application and integrate it with other AWS services including AWS Lambda, Amazon S3, Amazon Athena, Amazon QuickSight, and Amazon Translate. We’ll also show you additional methods for NLP available through Amazon Sagemaker.
Introduction to AI on AWS - AL/ML Hebrew WebinarBoaz Ziniman
Artificial Intelligence (AI) services on the AWS cloud bring the power of deep learning within reach of every developer, allowing us to develop new tools and enrich our systems with new capabilities. In this session, we will look into the opportunities to apply one or more of these services provide a number of examples and use cases to help you get started.
AWS Machine Learning Week SF: Build Intelligent Applications with AWS ML Serv...Amazon Web Services
AWS Machine Learning Week at the San Francisco Loft
Add Intelligence to Applications with AWS ML Services: Organizations are increasingly turning to machine learning to build intelligent applications and get more insights out of their data in real-time. In this session, you’ll learn about AWS Machine Learning APIs for computer vision and language, and how to get started with these pre-trained services: Amazon Rekognition, Amazon Comprehend, Amazon Transcribe, Amazon Translate, Amazon Polly, and Amazon Lex. We’ll also show how these services connect to AWS’s comprehensive data platform and services to drive the success of your machine learning projects.
Speaker: Randall Hunt - Technical Evangelist, AWS
Add Intelligence to Applications with AWS ML Services: Machine Learning Week ...Amazon Web Services
Machine Learning Week at the San Francisco Loft: Add Intelligence to Applications with AWS ML Services
Organizations are increasingly turning to machine learning to build intelligent applications and get more insights out of their data in real-time. In this session, you’ll learn about AWS Machine Learning APIs for computer vision and language, and how to get started with these pre-trained services: Amazon Rekognition, Amazon Comprehend, Amazon Transcribe, Amazon Translate, Amazon Polly, and Amazon Lex. We’ll also show how these services connect to AWS’s comprehensive data platform and services to drive the success of your machine learning projects.
Level: 200
Speaker: Anjana Kandalam - Solutions Architect, AWS
The document discusses the top errors that startups make and how to avoid them. It lists 8 major errors: 1) developing a product that no one needs, 2) losing focus, 3) choosing the wrong technology, 4) overcomplicating solutions, 5) not defining metrics to guide decisions, 6) not iterating quickly enough, 7) not delegating tasks, and 8) lacking determination. It also provides an overview of MG Group's architecture when building applications, which involves using services like AWS ECS, Fargate, API Gateway, Lambda, RDS, and deploying in a DevOps process between different VPC environments.
Unique engine recommendations give customers a shopping experience in which the most relevant products are displayed real time. By enhancing your online store's user experience with personalised recommendations, you’ll need to select an algorithm that will help you with product discovery and to enable larger order sizes that can lead to increased sales.
This document summarizes Amazon's Alexa Voice Service which allows device makers and skill builders to incorporate voice capabilities into their products and services. It notes that voice interactions are growing fast driven by devices with Alexa built-in, and that Alexa skills and devices that work with Alexa are expanding to over 70,000 products across 150 countries. The document outlines Amazon's vision for Alexa to be everywhere through built-in devices, skills, and connectivity to other products and services in the home, on the go and at work.
Using Machine Learning on AWS for Continuous Sentiment Analysis from Labeling...Amazon Web Services
by Zignal Labs
Today, machine learning solves a range of everyday business challenges. Companies are leveraging machine learning to understand how their brands are perceived in the marketplace across key stakeholder segments. How does the brand resonate with customers and the media? What product feedback and enhancements can be learned?
By harnessing the power of machine learning, Zignal monitors and analyzes – in real-time – brand conversations across social, broadcast, digital and traditional media channels. In this session, learn how Zignal leverages Amazon SageMaker, Amazon Mechanical Turk, AWS Code Pipeline and AWS Lambda to accurately measure the brand health of major enterprises such as NVIDIA and Airbnb. Zignal will dive deep into how Amazon SageMaker and these services work together on machine learning models in a real-time media environment.
Artificial Intelligence (AI) services on the AWS cloud bring the power of deep learning within reach of every developer, allowing us to develop new tools and enrich our systems with new capabilities. In this session, we will look into the opportunities to apply one or more of these services provide a number of examples and use cases to help you get started.
This document provides an overview of Amazon Web Services' machine learning capabilities, including:
- AI services like Rekognition, Polly, Transcribe, Translate, and Comprehend that perform tasks like image recognition, speech synthesis, speech-to-text, language translation, and natural language processing without requiring ML expertise.
- The Amazon SageMaker service for building, training, and deploying machine learning models at scale using Amazon Web Services products and infrastructure.
- Amazon's machine learning frameworks and infrastructure for training models, including EC2 instances optimized for ML workloads and elastic inference acceleration.
Using Amazon ML Services for Video Transcription & Translation: Machine Learn...Amazon Web Services
Machine Learning Workshops at the San Francisco Loft
Using Amazon ML Services for Video Transcription and Translation
In this hands-on workshop, participants will use AWS ML services to generate transcripts from audio files, use NLP to analyze those transcripts, and produce subtitles in multiple languages. Using ML, you can keep pace with the proliferation of audio/video content across businesses. Asset managers can unlock hidden value in existing media libraries by finding precise moments when particular keywords or phrases are spoken; video publishers can benefit from subtitle and localized files for reaching global audiences; and IT organizations can utilize transcription data to improve organizational governance.
Level: 200-300
The document discusses how various organizations are applying artificial intelligence and machine learning techniques. It provides examples of cancer research centers using AI to extract key data from patient records to help clinical research. It also lists some Amazon Web Services customers that are using AI/ML for applications like computer vision, natural language processing, forecasting and recommendations. The document emphasizes that AI can be used to gain insights from large and complex data in areas like healthcare.
Artificial Intelligence (Machine Learning) on AWS: How to StartVladimir Simek
Amazon has been investing deeply in artificial intelligence (AI) for over 20 years. Machine learning (ML) algorithms drive many of its internal systems. It is also core to the capabilities Amazon's customers experience – from the path optimization in the fulfillment centers, and Amazon.com’s recommendations engine, to Echo powered by Alexa, drone initiative Prime Air, and the new retail experience Amazon Go. This is just the beginning. Amazon's mission is to share learnings and ML capabilities as fully managed services, and put them into the hands of every developer and data scientist.
If you are interested, how can you develop ML-based smart applications on the AWS platform, and want to see a couple of cool demos, join us for the next AWS meetup. AWS Solutions Architect, Vladimir Simek, will be presenting the full AWS portfolio for AI and ML - from virtual servers enabled for training Deep Learning models up to a fully managed API-based services.
Breaking Language Barriers with AI: AWS Developer Workshop - Web Summit 2018Amazon Web Services
Breaking Language Barriers with AI: AWS Developer Workshop - Web Summit 2018
AI and Machine learning allow developers to introduce new language capabilities in their apps and use Natural Language Processing and Natural Language Understanding to break language barriers, add new functionality and expand their target audience. This session will focus on several AWS AI services for developers, that allow you to add such functionality to your code with minimal effort. We will build an automatic translator, interact with text to speech and try to extract sentiments from live text coming from different feeds.
Speaker: Boaz Ziniman - Technical Evangelist, AWS
The document discusses preparing an organization to operate at scale on AWS. It recommends starting with a single AWS account and then expanding to multiple accounts as needs grow. Key steps include setting operational priorities, designing with operations in mind, and ensuring operational readiness. Specific account types are proposed like core accounts, security accounts, and developer/sandbox accounts. The goal is to evolve the architecture incrementally as the organization and usage of AWS grows over time.
Mike Gillespie - Automate for Efficiency with Amazon Transcribe & Amazon Tran...Amazon Web Services
The document discusses Amazon Transcribe and Amazon Translate services. It provides an overview of how Transcribe can automatically transcribe audio and video files into text. It also describes how Translate can instantly translate text between many different languages. Use cases for both services include automating tasks like video captioning, localization of online content, and facilitating global communications.
The document discusses deep learning and neural networks. It begins with an overview of machine learning and defines deep learning as using neural networks with multiple layers to learn from complex data without explicitly defining features. It then discusses building and deploying a custom image classifier in 21 lines of code using Amazon SageMaker. It concludes by providing resources for learning more about deep learning.
Machine Learning on AWS (December 2018)Julien SIMON
The document discusses machine learning services available on Amazon Web Services (AWS). It describes several AWS machine learning application services like Amazon Rekognition for image and video analysis, Amazon Translate for language translation, and Amazon Transcribe for speech to text. It also covers AWS machine learning platform services, including Amazon SageMaker for building, training and deploying models, and Amazon Comprehend for natural language processing. Many companies are using these AWS machine learning services for applications like facial recognition, translation, speech transcription and analyzing text.
This document provides an overview of Amazon's machine learning services, including Amazon Rekognition (image and video analysis), Amazon Polly (text-to-speech), Amazon Translate (language translation), Amazon Transcribe (speech recognition), Amazon Comprehend (natural language processing), and Amazon Lex (conversational interfaces). It highlights the capabilities of each service and provides examples of their uses. The document also discusses Amazon Web Services' machine learning infrastructure and frameworks for building and deploying machine learning models at scale.
Artificial Intelligence for Developers - OOP MunichBoaz Ziniman
Artificial Intelligence (AI) services on the AWS cloud bring the experience of Amazon and power of deep learning within reach of every developer, allowing us to develop new tools and enrich our systems with new capabilities. In this session, we will look into the history of AI at Amazon and explore the opportunities to apply one or more of the AI services, provide a number of examples and use cases to help you get started.
Introduction to Amazon Go and Amazon Go Tour by Humphrey ChanSameer Kenkare
Humphrey Chen is a senior manager at Amazon Rekognition. The document discusses Amazon's machine learning services including Rekognition, which provides image and video analysis features like facial recognition and analysis, celebrity recognition, label detection, moderation, and text detection. It also discusses Amazon Textract, which simplifies extracting text, tables, and forms from documents without needing code or templates.
Build Text Analytics Solutions with Amazon Comprehend and Amazon TranslateAmazon Web Services
by Pratap Ramamurthy, Partner Solutions Architect, AWS
Natural language holds a wealth of information like user sentiment and conversational intent. In this session, we'll demonstrate the capabilities of Amazon Comprehend, a natural language processing (NLP) service that uses machine learning to find insights and relationships in text. We'll show you how to build a VOC (Voice of the Customer) application and integrate it with other AWS services including AWS Lambda, Amazon S3, Amazon Athena, Amazon QuickSight, and Amazon Translate. We’ll also show you additional methods for NLP available through Amazon Sagemaker.
Introduction to AI on AWS - AL/ML Hebrew WebinarBoaz Ziniman
Artificial Intelligence (AI) services on the AWS cloud bring the power of deep learning within reach of every developer, allowing us to develop new tools and enrich our systems with new capabilities. In this session, we will look into the opportunities to apply one or more of these services provide a number of examples and use cases to help you get started.
AWS Machine Learning Week SF: Build Intelligent Applications with AWS ML Serv...Amazon Web Services
AWS Machine Learning Week at the San Francisco Loft
Add Intelligence to Applications with AWS ML Services: Organizations are increasingly turning to machine learning to build intelligent applications and get more insights out of their data in real-time. In this session, you’ll learn about AWS Machine Learning APIs for computer vision and language, and how to get started with these pre-trained services: Amazon Rekognition, Amazon Comprehend, Amazon Transcribe, Amazon Translate, Amazon Polly, and Amazon Lex. We’ll also show how these services connect to AWS’s comprehensive data platform and services to drive the success of your machine learning projects.
Speaker: Randall Hunt - Technical Evangelist, AWS
Add Intelligence to Applications with AWS ML Services: Machine Learning Week ...Amazon Web Services
Machine Learning Week at the San Francisco Loft: Add Intelligence to Applications with AWS ML Services
Organizations are increasingly turning to machine learning to build intelligent applications and get more insights out of their data in real-time. In this session, you’ll learn about AWS Machine Learning APIs for computer vision and language, and how to get started with these pre-trained services: Amazon Rekognition, Amazon Comprehend, Amazon Transcribe, Amazon Translate, Amazon Polly, and Amazon Lex. We’ll also show how these services connect to AWS’s comprehensive data platform and services to drive the success of your machine learning projects.
Level: 200
Speaker: Anjana Kandalam - Solutions Architect, AWS
The document discusses the top errors that startups make and how to avoid them. It lists 8 major errors: 1) developing a product that no one needs, 2) losing focus, 3) choosing the wrong technology, 4) overcomplicating solutions, 5) not defining metrics to guide decisions, 6) not iterating quickly enough, 7) not delegating tasks, and 8) lacking determination. It also provides an overview of MG Group's architecture when building applications, which involves using services like AWS ECS, Fargate, API Gateway, Lambda, RDS, and deploying in a DevOps process between different VPC environments.
Unique engine recommendations give customers a shopping experience in which the most relevant products are displayed real time. By enhancing your online store's user experience with personalised recommendations, you’ll need to select an algorithm that will help you with product discovery and to enable larger order sizes that can lead to increased sales.
This document summarizes Amazon's Alexa Voice Service which allows device makers and skill builders to incorporate voice capabilities into their products and services. It notes that voice interactions are growing fast driven by devices with Alexa built-in, and that Alexa skills and devices that work with Alexa are expanding to over 70,000 products across 150 countries. The document outlines Amazon's vision for Alexa to be everywhere through built-in devices, skills, and connectivity to other products and services in the home, on the go and at work.
Using Machine Learning on AWS for Continuous Sentiment Analysis from Labeling...Amazon Web Services
by Zignal Labs
Today, machine learning solves a range of everyday business challenges. Companies are leveraging machine learning to understand how their brands are perceived in the marketplace across key stakeholder segments. How does the brand resonate with customers and the media? What product feedback and enhancements can be learned?
By harnessing the power of machine learning, Zignal monitors and analyzes – in real-time – brand conversations across social, broadcast, digital and traditional media channels. In this session, learn how Zignal leverages Amazon SageMaker, Amazon Mechanical Turk, AWS Code Pipeline and AWS Lambda to accurately measure the brand health of major enterprises such as NVIDIA and Airbnb. Zignal will dive deep into how Amazon SageMaker and these services work together on machine learning models in a real-time media environment.
Artificial Intelligence (AI) services on the AWS cloud bring the power of deep learning within reach of every developer, allowing us to develop new tools and enrich our systems with new capabilities. In this session, we will look into the opportunities to apply one or more of these services provide a number of examples and use cases to help you get started.
This document provides an overview of Amazon Web Services' machine learning capabilities, including:
- AI services like Rekognition, Polly, Transcribe, Translate, and Comprehend that perform tasks like image recognition, speech synthesis, speech-to-text, language translation, and natural language processing without requiring ML expertise.
- The Amazon SageMaker service for building, training, and deploying machine learning models at scale using Amazon Web Services products and infrastructure.
- Amazon's machine learning frameworks and infrastructure for training models, including EC2 instances optimized for ML workloads and elastic inference acceleration.
Using Amazon ML Services for Video Transcription & Translation: Machine Learn...Amazon Web Services
Machine Learning Workshops at the San Francisco Loft
Using Amazon ML Services for Video Transcription and Translation
In this hands-on workshop, participants will use AWS ML services to generate transcripts from audio files, use NLP to analyze those transcripts, and produce subtitles in multiple languages. Using ML, you can keep pace with the proliferation of audio/video content across businesses. Asset managers can unlock hidden value in existing media libraries by finding precise moments when particular keywords or phrases are spoken; video publishers can benefit from subtitle and localized files for reaching global audiences; and IT organizations can utilize transcription data to improve organizational governance.
Level: 200-300
The document discusses how various organizations are applying artificial intelligence and machine learning techniques. It provides examples of cancer research centers using AI to extract key data from patient records to help clinical research. It also lists some Amazon Web Services customers that are using AI/ML for applications like computer vision, natural language processing, forecasting and recommendations. The document emphasizes that AI can be used to gain insights from large and complex data in areas like healthcare.
Artificial Intelligence (Machine Learning) on AWS: How to StartVladimir Simek
Amazon has been investing deeply in artificial intelligence (AI) for over 20 years. Machine learning (ML) algorithms drive many of its internal systems. It is also core to the capabilities Amazon's customers experience – from the path optimization in the fulfillment centers, and Amazon.com’s recommendations engine, to Echo powered by Alexa, drone initiative Prime Air, and the new retail experience Amazon Go. This is just the beginning. Amazon's mission is to share learnings and ML capabilities as fully managed services, and put them into the hands of every developer and data scientist.
If you are interested, how can you develop ML-based smart applications on the AWS platform, and want to see a couple of cool demos, join us for the next AWS meetup. AWS Solutions Architect, Vladimir Simek, will be presenting the full AWS portfolio for AI and ML - from virtual servers enabled for training Deep Learning models up to a fully managed API-based services.
Breaking Language Barriers with AI: AWS Developer Workshop - Web Summit 2018Amazon Web Services
Breaking Language Barriers with AI: AWS Developer Workshop - Web Summit 2018
AI and Machine learning allow developers to introduce new language capabilities in their apps and use Natural Language Processing and Natural Language Understanding to break language barriers, add new functionality and expand their target audience. This session will focus on several AWS AI services for developers, that allow you to add such functionality to your code with minimal effort. We will build an automatic translator, interact with text to speech and try to extract sentiments from live text coming from different feeds.
Speaker: Boaz Ziniman - Technical Evangelist, AWS
The document discusses preparing an organization to operate at scale on AWS. It recommends starting with a single AWS account and then expanding to multiple accounts as needs grow. Key steps include setting operational priorities, designing with operations in mind, and ensuring operational readiness. Specific account types are proposed like core accounts, security accounts, and developer/sandbox accounts. The goal is to evolve the architecture incrementally as the organization and usage of AWS grows over time.
Mike Gillespie - Automate for Efficiency with Amazon Transcribe & Amazon Tran...Amazon Web Services
The document discusses Amazon Transcribe and Amazon Translate services. It provides an overview of how Transcribe can automatically transcribe audio and video files into text. It also describes how Translate can instantly translate text between many different languages. Use cases for both services include automating tasks like video captioning, localization of online content, and facilitating global communications.
The document discusses deep learning and neural networks. It begins with an overview of machine learning and defines deep learning as using neural networks with multiple layers to learn from complex data without explicitly defining features. It then discusses building and deploying a custom image classifier in 21 lines of code using Amazon SageMaker. It concludes by providing resources for learning more about deep learning.
Machine Learning on AWS (December 2018)Julien SIMON
The document discusses machine learning services available on Amazon Web Services (AWS). It describes several AWS machine learning application services like Amazon Rekognition for image and video analysis, Amazon Translate for language translation, and Amazon Transcribe for speech to text. It also covers AWS machine learning platform services, including Amazon SageMaker for building, training and deploying models, and Amazon Comprehend for natural language processing. Many companies are using these AWS machine learning services for applications like facial recognition, translation, speech transcription and analyzing text.
This document provides an overview of Amazon's machine learning services, including Amazon Rekognition (image and video analysis), Amazon Polly (text-to-speech), Amazon Translate (language translation), Amazon Transcribe (speech recognition), Amazon Comprehend (natural language processing), and Amazon Lex (conversational interfaces). It highlights the capabilities of each service and provides examples of their uses. The document also discusses Amazon Web Services' machine learning infrastructure and frameworks for building and deploying machine learning models at scale.
Amazon has been developing and applying machine learning and AI technologies across its business for over 20 years. It now offers a full suite of AI and ML services through AWS, including high-level application services, lower-level platform services, and infrastructure. Some key services highlighted include Amazon Rekognition for computer vision, Amazon Lex for conversational interfaces, Amazon Translate for neural machine translation, and Amazon SageMaker for building, training and deploying models at scale.
Introduction to AWS ML Application Services - BDA202 - Toronto AWS SummitAmazon Web Services
Amazon brings computer vision, natural language processing, speech recognition, text-to-speech, and machine translation within the reach of every developer. API-driven application services enable developers to easily plug in pre-built AI functionality into their applications and automate manual workflows. Join us to learn more about new language capabilities and text-in-image extraction. We also share how others are building the next generation of intelligent apps that can see, hear, speak, understand, and interact with the world around us.
by Pratap Ramamurthy, Partner Solutions Architect
Organizations are increasingly turning to machine learning to build intelligent applications and get more insights out of their data in real-time. In this session, you’ll learn about AWS Machine Learning APIs for computer vision and language, and how to get started with these pre-trained services: Amazon Rekognition, Amazon Comprehend, Amazon Transcribe, Amazon Translate, Amazon Polly, and Amazon Lex. We’ll also show how these services connect to AWS’s comprehensive data platform and services to drive the success of your machine learning projects.
The Future of AI - AllCloud Best of reInventBoaz Ziniman
The document discusses Amazon's artificial intelligence services. It provides an overview of Amazon's vision, language, and application AI services including Amazon Rekognition, Amazon Polly, Amazon Lex, Amazon Transcribe, Amazon Translate, and Amazon Comprehend. It also discusses Amazon SageMaker for building, training and deploying machine learning models and AWS DeepLens for developing custom computer vision applications.
Speaker: Herbert-John Kelly, AWS
Customer Speaker: Data Prophet
Level: 200
Join us to hear about our strategy for driving machine learning (ML) 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.
Add Intelligence to Applications with AWS ML Services
Organizations are increasingly turning to machine learning to build intelligent applications and get more insights out of their data in real-time. In this session, you’ll learn about AWS Machine Learning APIs for computer vision and language, and how to get started with these pre-trained services: Amazon Rekognition, Amazon Comprehend, Amazon Transcribe, Amazon Translate, Amazon Polly, and Amazon Lex. We’ll also show how these services connect to AWS’s comprehensive data platform and services to drive the success of your machine learning projects.
Level: 200
Speaker: Yash Pant - Enterprise Solutions Architect, AWS
Transform the Modern Contact Center Using Machine Learning and Analytics (AIM...Amazon Web Services
Analyzing customer service interactions across channels provides a complete 360-degree view of customers. By capturing all interactions, you can better identify the root cause of issues and improve first-call resolution and customer satisfaction. In this session, learn how to integrate Amazon Connect and AWS machine learning services, such Amazon Lex, Amazon Transcribe, and Amazon Comprehend, to quickly process and analyze thousands of customer conversations and gain valuable insights. With speech and text analytics, you can pick up on emerging service-related trends before they get escalated or identify and address a potential widespread problem at its inception.
Mike Gillespie - Build Intelligent Applications with AWS ML Services (200).pdfAmazon Web Services
Organizations are increasingly turning to machine learning to build intelligent applications and get more insights out of their data in real-time. In this session, you’ll learn about AWS Machine Learning APIs for computer vision and language, and how to get started with these pre-trained services: Amazon Rekognition, Amazon Comprehend, Amazon Transcribe, Amazon Translate, Amazon Polly, and Amazon Lex. We’ll also show how these services connect to AWS’s comprehensive data platform and services to drive the success of your machine learning projects.
The document discusses Amazon Web Services' (AWS) machine learning and artificial intelligence services. It provides an overview of AWS' application services like Amazon Rekognition, Amazon Polly, and Amazon Translate. It also discusses AWS' platform services like Amazon SageMaker, Amazon EMR, and the AWS Deep Learning AMI. The document emphasizes that more AI/ML is built on AWS than anywhere else and highlights several customer examples using AWS machine learning services.
Machine learning state of the union - Tel Aviv Summit 2018Amazon Web Services
Join us to hear about our strategy for driving machine learning innovation for our customers and learn what's new from AWS in the machine learning space. We will discuss and demonstrate the latest new services for ML on AWS: Amazon SageMaker, AWS DeepLens, Amazon Rekogntion Video, Amazon Translate, Amazon Transcribe and Amazon Comprehend.
Attend this session to understand how to make the most of machine learning in the cloud.
AWS re:Invent 2018 - Machine Learning recap (December 2018)Julien SIMON
AWS is improving machine learning services in three key areas: cost, data preparation, and ease of use. New services like Amazon SageMaker GroundTruth and Amazon Personalize aim to reduce the cost and complexity of obtaining labeled data and building models. AWS is also optimizing frameworks like TensorFlow for faster, more efficient training and lowering inference costs with Elastic Inference. The goal is to continue driving down barriers to ML for all developers.
Optimizing Healthcare Call Centers with Natural Language Understanding (HLC30...Amazon Web Services
Large call volumes into customer service call centers can lead to frustrated customers, delayed responses, and overburdened staff, particularly when a large number of queries could have been resolved with simple yes or no answers, or formulaic responses. In this session hear from the National Health Service (NHS) Business Services Authority, the support body for multiple NHS organizations in England, how it is using machine learning to manage the large volumes of calls coming to their call center (five million calls each year). Learn how these services have been used to reduce call response times, increase staff morale, and maximize staff utilization for value-add activities. See how to develop and implement Amazon Connect, Amazon Lex, and Amazon Polly to automate call centers, reduce labor costs, and provide a consistent experience for customers.
The document outlines an agenda for a day-long event on AI and machine learning. It begins with an introductory session on the state of AI from 10:00-11:00 am. This is followed by a break and then deeper sessions on Amazon Sagemaker, Forecast, and Personalize. Lunch is from 12:30-1:30 pm. The afternoon includes sessions on machine learning production with Sagemaker and fraud detection with Sagemaker. There are additional breaks throughout the day and the event concludes with a session on reinforcement learning from 3:45-4:45 pm.
Improving Customer Experience: Enhanced Customer Insights Using Natural Langu...Amazon Web Services
The document discusses using natural language processing (NLP) techniques to gain customer insights from unstructured text data. It describes several Amazon NLP services like Amazon Comprehend, Amazon Transcribe, Amazon Translate, and Amazon Polly that can be used to extract entities, key phrases, sentiment and topics from text. It also discusses how these services can be combined with Amazon SageMaker and Amazon ML services to build custom classifiers and analyze customer calls to improve customer experience.
Artificial Intelligence (AI) services on the AWS cloud bring the power of deep learning within reach of every developer, allowing us to develop new tools and enrich our systems with new capabilities. In this session, we will look into the opportunities to apply one or more of these services provide a number of examples and use cases to help you get started.
Artificial Intelligence (AI) services on the AWS cloud bring the experience of Amazon and power of deep learning within reach of every developer, allowing us to develop new tools and enrich our systems with new capabilities. In this session, we will look into the history of AI at Amazon and explore the opportunities to apply one or more of the AI services provide a number of examples and use cases to help you get started.
Artificial Intelligence (AI) services on the AWS cloud bring the experience of Amazon and power of deep learning within reach of every developer, allowing us to develop new tools and enrich our systems with new capabilities. In this session, we will look into the history of AI at Amazon and explore the opportunities to apply one or more of the AI services provide a number of examples and use cases to help you get started.
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.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Maruthi Prithivirajan, Head of ASEAN & IN Solution Architecture, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
20 Comprehensive Checklist of Designing and Developing a WebsitePixlogix Infotech
Dive into the world of Website Designing and Developing with Pixlogix! Looking to create a stunning online presence? Look no further! Our comprehensive checklist covers everything you need to know to craft a website that stands out. From user-friendly design to seamless functionality, we've got you covered. Don't miss out on this invaluable resource! Check out our checklist now at Pixlogix and start your journey towards a captivating online presence today.
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
“An Outlook of the Ongoing and Future Relationship between Blockchain Technologies and Process-aware Information Systems.” Invited talk at the joint workshop on Blockchain for Information Systems (BC4IS) and Blockchain for Trusted Data Sharing (B4TDS), co-located with with the 36th International Conference on Advanced Information Systems Engineering (CAiSE), 3 June 2024, Limassol, Cyprus.
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!SOFTTECHHUB
As the digital landscape continually evolves, operating systems play a critical role in shaping user experiences and productivity. The launch of Nitrux Linux 3.5.0 marks a significant milestone, offering a robust alternative to traditional systems such as Windows 11. This article delves into the essence of Nitrux Linux 3.5.0, exploring its unique features, advantages, and how it stands as a compelling choice for both casual users and tech enthusiasts.
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.
Full-RAG: A modern architecture for hyper-personalizationZilliz
Mike Del Balso, CEO & Co-Founder at Tecton, presents "Full RAG," a novel approach to AI recommendation systems, aiming to push beyond the limitations of traditional models through a deep integration of contextual insights and real-time data, leveraging the Retrieval-Augmented Generation architecture. This talk will outline Full RAG's potential to significantly enhance personalization, address engineering challenges such as data management and model training, and introduce data enrichment with reranking as a key solution. Attendees will gain crucial insights into the importance of hyperpersonalization in AI, the capabilities of Full RAG for advanced personalization, and strategies for managing complex data integrations for deploying cutting-edge AI solutions.
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.
How to Get CNIC Information System with Paksim Ga.pptxdanishmna97
Pakdata Cf is a groundbreaking system designed to streamline and facilitate access to CNIC information. This innovative platform leverages advanced technology to provide users with efficient and secure access to their CNIC details.
10. Machine Learning at Amazon.com
R E TA I L
Demand Forecasting
Vendor Lead Time Prediction
Pricing
Packaging
Substitute Prediction
C U S TO M E R S
Recommendation
Product Search
Product Ads
Shopping Advice
Customer Problem
Detection
S E L L E R S
Fraud Detection
Predictive Help
Seller Search & Crawling
C ATA LO G U E
Browse-Node Classification
Meta-data Validation
Review Analysis
Product Matching
T E X T
In-Book Search
Named-entity Extraction
Summarization/X-ray
Plagiarism Detection
I M A G E S
Visual Search
Product Image
Enhancement
Brand Tracking
32. 137 Language Pairs
• English
• Spanish
• Portuguese
• German
• French
• Arabic
• Simplified Chinese
• Japanese
• Russian
• Italian
• Traditional Chinese
• Turkish
• Czech
.Coming soon : Danish, Dutch, Finnish, Hebrew, Polish, and Swedish
53. Julien Simon
Principal Evangelist, Artificial Intelligence & Machine Learning
@julsimon
https://ml.aws
https://aws.amazon.com/blogs/machine-learning
https://medium.com/@julsimon
https://youtube.com/juliensimonfr
Editor's Notes
18 Regions, 55 Azs
5 Regions coming: Bahrain, Cape Town, Hong Kong, Stockholm, and a second GovCloud Region in the US.
Helping recommend what might interest you, by learning from other customers who have purchased this item have also liked.
Amazon Echo is a hands-free speaker you control with your voice. Echo connects to the Alexa Voice Service to play music, make calls, send and receive messages, provide information, news, sports scores, weather, and more—instantly. All you have to do is ask.
Amazon Robotics was founded in 2003 on the notion that in order to meet consumer demands in eCommerce, a better approach to order fulfillment solutions was necessary. Amazon Robotics empowers a smarter, faster, more consistent customer experience through automation
automates fulfilment center operations using various methods of robotic technology including autonomous mobile robots, sophisticated control software, language perception, power management, computer vision, depth sensing, machine learning, object recognition, and semantic understanding of commands.
Amazon Prime Air is a service that will deliver packages up to 2.5 kg in 30 minutes or less using small drones and relies extensively on visual object recognition.
We have Prime Air development centers in the United States, the United Kingdom, Austria, France and Israel.
Amazon Go is a new kind of store with no checkout required. We created the world’s most advanced shopping technology so you never have to wait in line. With our Just Walk Out Shopping experience, simply use the Amazon Go app to enter the store, take the products you want, and go! No lines, no checkout. (No, seriously.)
No lines, no checkout
Our checkout-free shopping experience is made possible by the same types of technologies used in self-driving cars: computer vision, sensor fusion, and deep learning. Our Just Walk Out Technology automatically detects when products are taken from or returned to the shelves and keeps track of them in a virtual cart. When you’re done shopping, you can just leave the store. Shortly after, we’ll charge your Amazon account and send you a receipt.
This is just a sample of the range of AI-related services that we use across Amazon.com to help build better experiences for our customers. Mandy of which you don’t ever *SEE* as a customer. Our order fulfillment services, how we pack our trucks, and all of the logistics from the time you place your order until it shows up on your doorstep is completely directed by our AI advancements.
Up to 100 faces
Recognizing clients
User Generated Content
You can use the ‘MinConfidence’ parameter in your API requests to balance detection of content (recall) vs the accuracy of detection (precision).
You can use the ‘MinConfidence’ parameter in your API requests to balance detection of content (recall) vs the accuracy of detection (precision).
Polly also support Speech Synthesis Markup Language (SSML) Version 1.0
The Voice Browser Working Group has sought to develop standards to enable access to the Web using spoken interaction.
…Amazon Comprehend, a Natural Language Processing service that enables customers to discover insights from text.
1/ Without provisioning a server, Comprehend can understand documents, social network posts, articles, and any other data in AWS
2/ Simply provide text stored in data lake in S3 via Comprehend API, and Comprehend uses NLP to give you highly accurate info about what it contains in 4 categories:
a/ entities (people, places, dates, brands, qtys)
b/ key phrases that provide significance to the text
c/ language being used
d/ sentiment
First, you need to collect and prepare your training data to discover which elements of your data set are important. Then, you need to select which algorithm and framework you’ll use. After deciding on your approach, you need to teach the model how to make predictions by training, which requires a lot of compute. Then, you need to tune the model so it delivers the best possible predictions, which is often a tedious and manual effort. After you’ve developed a fully trained model, you need to integrate the model with your application and deploy this application on infrastructure that will scale. All of this takes a lot of specialized expertise, access to large amounts of compute and storage, and a lot of time to experiment and optimize every part of the process. In the end, it's not a surprise that the whole thing feels out of reach for most developers.
SageMaker makes it easy to build ML models and get them ready for training by providing everything you need to quickly connect to your training data, and to select and optimize the best algorithm and framework for your application. Amazon SageMaker includes hosted Jupyter notebooks that make it is easy to explore and visualize your training data stored in Amazon S3. You can connect directly to data in S3, or use AWS Glue to move data from Amazon RDS, Amazon DynamoDB, and Amazon Redshift into S3 for analysis in your notebook.
To help you select your algorithm, Amazon SageMaker includes the 10 most common machine learning algorithms which have been pre-installed and optimized to deliver up to 10 times the performance you’ll find running these algorithms anywhere else. Amazon SageMaker also comes pre-configured to run TensorFlow and Apache MXNet, two of the most popular open source frameworks, or you have the option of using your own framework.
You can begin training your model with a single click in the Amazon SageMaker console. The service manages all of the underlying infrastructure for you and can easily scale to train models at petabyte scale. To make the training process even faster and easier, Amazon SageMaker can automatically tune your model to achieve the highest possible accuracy.
Once your model is trained and tuned, SageMaker makes it easy to deploy in production so you can start generating predictions on new data (a process called inference). Amazon SageMaker deploys your model on an auto-scaling cluster of Amazon EC2 instances that are spread across multiple availability zones to deliver both high performance and high availability. It also includes built-in A/B testing capabilities to help you test your model and experiment with different versions to achieve the best results.
For maximum versatility, we designed Amazon SageMaker in three modules – Build, Train, and Deploy – that can be used together or independently as part of any existing ML workflow you might already have in place.
Assume a guest, Jessica Yu, already has a reservation. Prior to her arrival, she gets a pre-arrival notification with opportunities for her to upgrade her room and/ or select amenities she might like. The data on her reservation and her broader profile info is in the CRM – Revinate in this case. Room rates come from Duetto, the Revenue Management System. This integration is already live but one place where in the future it can become even more powerful is through targeted upgrades. Leveraging machine learning, we can predict which room upgrades and which amenities are most likely to resonate with her. This makes life better for her because she doesn’t have to sort through what at some fancier hotels and resorts might be dozens of options. And it’s also great for the hotel because revenue is optimized through both higher conversion (based on showing Jessica the right thing) and better rate (dynamic based on season, availability, and many other possible factors).
SageMaker is going to make it much easier for everyday developers to build machine-learning models. But, people and developers are still really interested in learning more about how they can use machine learning. They want to do it, so they're reading all kinds of literature, and there are some code samples they can play around with. But, for any of us who've had to learn something new that has any kind of complexity, there's no substitute for hands-on training and application.
And so we thought about: What can we do that would allow our builders and our developers to get this hands-on training? Our teams worked on this problem and developed AWS DeepLens, which is the world's first wireless deep-learning-enabled video-camera for developers.
AWS DeepLens is a high-definition camera with on-board compute that is optimized for deep learning. It comes with computer-vision models that we've already built that you can use right on the camera, or you can build your own in SageMaker and import them over the air via the console with a few clicks to DeepLens.
It has Greengrass in it. So in addition to writing the models, you can program Greengrass to run various Lambda triggers.
There's lots of tutorials and prebuilt models for you, so you can get started right away. In fact, we believe that you'll be able to get started running your first deep-learning computer-vision model in 10 minutes from the time that you unbox the camera. You can provide and you program this thing to do almost anything you can imagine. So for instance, you could imagine programming the camera with computer-vision models where, if you recognize a license plate coming into your driveway, it will open the garage door. Or you could program it to send you an alert when your dog gets on the couch.
Really, you can do almost anything. And it's going to give you an opportunity to get learning very quickly in a way that you haven't been able to do before.
EACH OF THESE ARE AVAILBLE - TODAY WE WILL DIVE INTO OBJECT DETECTION AS A WAY TO GET YOU STARTED… AFTER THIS WORKSHOP YOU THEN EXPLORE THESE OTHER SAMPLES, CREATE CUSTOM FUNCTIONALITY OR START YOUR OWN PROEHCT FROM SCRATCH
Learn the basics of machine learning through hands on examples and sample projects
sample projects of varying difficulty available for use: object detection, artistic style transfer, face recognition, hot dog Not hot dog, cat vs dog, license plate detection
Use existing sample projects or extend the sample project with your own custom functionality (example detect when your dog is sitting on the couch and send an sms) or create your own project
Go deeper through integrations with Sage Maker, Greengrass, and other AWS services
So far, we've discussed the bottom and middle layers of the machine learning stack – first we talked about the frameworks and the deep learning AMI for expert practitioners. Then, SageMaker and DeepLens in the middle layer to bring ML capabilities to all developers. Now, at the top of the stack, we serve developers and companies who want to add solution-oriented intelligence to their applications through an API call rather than developing and training their own models. These are services that exhibit artificial intelligence that emulates a human’s cognitive skills. Last year, we announced three services in this area: Amazon Rekognition (image analysis), Amazon Polly (text-to-speech), and Amazon Lex (conversational applications).