The document provides an overview of artificial intelligence services available on Amazon Web Services (AWS). It discusses Amazon Polly for text-to-speech, Amazon Rekognition for image and video analysis, and Amazon Lex for conversational interfaces. It also describes Amazon Machine Learning for building machine learning models and Amazon EMR for running Spark and Hadoop clusters. Customers in various industries are using these AI services to power applications like fraud detection, personalization, targeted marketing, and more.
Make your solution see, hear and talk, leveraging artificial intelligence services based on deep learning and neural networks. We will discover three new AI tools from AWS - Lex, Polly and Rekognition; integrated with AWS IoT and a physical world device for human interaction and environmental awareness.
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.
Amazon Rekognition is a service that makes it easy to add image analysis to your applications. With Rekognition, you can detect objects, scenes, and faces in images. You can also search and compare faces. Rekognition’s API lets you easily build powerful visual search and discovery into your applications. With Amazon Rekognition, you only pay for the images you analyze and the face metadata you store. There are no minimum fees and there are no upfront commitments.
Artificial Intelligence (AI) services on the AWS cloud bring deep learning (DL) technologies like natural language understanding (NLU), automatic speech recognition (ASR), image recognition and computer vision (CV), text-to-speech (TTS), and machine learning (ML) within reach of every developer. In this session, you will be introduced to several new AI services: Amazon Lex, to build sophisticated text and voice chatbots; Amazon Rekognition, for deep learning-based image recognition; and Amazon Polly, for turning text into lifelike speech. The opportunities to apply one or more of these DL services are nearly boundless and this session will provide a number of examples and use cases to help you get started.
Make your solution see, hear and talk, leveraging artificial intelligence services based on deep learning and neural networks. We will discover three new AI tools from AWS - Lex, Polly and Rekognition; integrated with AWS IoT and a physical world device for human interaction and environmental awareness.
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.
Amazon Rekognition is a service that makes it easy to add image analysis to your applications. With Rekognition, you can detect objects, scenes, and faces in images. You can also search and compare faces. Rekognition’s API lets you easily build powerful visual search and discovery into your applications. With Amazon Rekognition, you only pay for the images you analyze and the face metadata you store. There are no minimum fees and there are no upfront commitments.
Artificial Intelligence (AI) services on the AWS cloud bring deep learning (DL) technologies like natural language understanding (NLU), automatic speech recognition (ASR), image recognition and computer vision (CV), text-to-speech (TTS), and machine learning (ML) within reach of every developer. In this session, you will be introduced to several new AI services: Amazon Lex, to build sophisticated text and voice chatbots; Amazon Rekognition, for deep learning-based image recognition; and Amazon Polly, for turning text into lifelike speech. The opportunities to apply one or more of these DL services are nearly boundless and this session will provide a number of examples and use cases to help you get started.
Make your solution see, hear and talk, leveraging artificial intelligence services based on deep learning and neural networks. We will discover three new AI tools from AWS - Lex, Polly and Rekognition; integrated with a physical world device for human interaction and environmental awareness
Speaker: Sunil Mallya, Solutions Architect, AWS Deep Learning
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
Use Amazon Rekognition to Build a Facial Recognition SystemAmazon Web Services
Amazon Rekognition makes it easy to extract meaningful metadata from visual content. In this workshop, you will work in teams to build a simple system to help track missing persons. You’ll develop a solution that leverages Amazon Rekognition and other AWS services to analyze images from various sources (e.g., social media) and provide authorities with timely reports and alerts on new leads for missing individuals. The solution will entail a repeatable and automated process that follows best practices for architecting in the cloud, such as designing for high availability and scalability.
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
Taking Complexity Out of Data Science with AWS and Zoomdata PPTAmazon Web Services
Zoomdata helps you find quick and easy solutions for even the most complicated Big Data challenges. To meet their Big Data challenges, Blue Canopy designed an AWS-based Data Science Workstation (DSWS) with Zoomdata’s blazing-fast, secure data analysis and visualization platform. Blue Canopy provides information technology and cyber security solutions to US government and commercial enterprises and needed a solution that could expand the depth and breadth of their analytics capabilities for big data projects. They built a Data Science Workstation, which is a self-provisioned tool on AWS, that delivers on-demand services for data analysts.
Exploring the Business Use Cases for Amazon Rekognition - June 2017 AWS Onlin...Amazon Web Services
Learning Objectives:
- Learn about using image analysis with Amazon Rekognition - Learn about popular use cases for Amazon Rekognition
- Learn how specific AWS customers have implemented Amazon Rekognition in different workflows
Amazon Rekognition is a service that makes it easy to add image analysis to your applications. You can detect objects, scenes, faces; search and compare faces; and identify inappropriate content in images, In this tech talk, we will introduce Amazon Rekognition and walk through use cases for media and entertainment, hospitality and public safety, where Amazon Rekognition’s computer vision capabilities create the potential to streamline existing workflows to reduce time to production subsequently reduce costs and improve service quality and delivery for customers and citizens.
AWS Summit Singapore - Get to Know Your Customers - Modern Data ArchitectureAmazon Web Services
Craig Stires, Business Development Manager - Big Data & Analytics, APAC, AWS
Your customers probably want a better experience with your brand. Your different business teams want and need better insights in their decision making. Almost certainly, your finance and operations teams require this to happen at a fraction of the cost of traditional on-premises options. Modern data architectures on AWS help many of our best customers realise all of those goals. Your business data contains critical information about customer behaviours, operational decisions, and many factors that have financial impact on your organisation. Increasingly, this data sits beyond your transactional systems, and is too big, too fast, and too complex for existing systems to handle. AWS Data and Analytics services are designed from our customers' requirements to ingest, store, analyse, and consume information at record-breaking scale. In this session you will learn how these services work together to deliver business automation, enhance customer engagement and intelligence.
Demystifying Machine Learning On AWS - AWS Summit Sydney 2018Amazon Web Services
Demystifying Machine Learning on AWS
Machine Learning is having a major impact in our society, but how can we simplify the build, train, and deploy process for all developers and data scientists? Understand how cloud-based machine learning frameworks can help turn your data into intelligence. We will introduce the general machine learning process utilising the AWS Deep Learning AMIs and hear from carsales.com.au about how they built the Cyclops, a Super Human Image Recognition Software on AWS. We will then discuss the new capabilities delivered by Amazon SageMaker and how this product will further reduce the undifferentiated heavy lifting; freeing you up to focus on your business and allow your developers to quickly and easily expand into the world of Machine Learning.
Jenny Davies, Solutions Architect, Amazon Web Services and Agustinus Nalwan, AI and Machine Learning Technical Development Manager, Carsales.com.au
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.
In this session from the London AWS Summit 2015 Tech Track Replay, AWS Technical Evangelist Ian Massingham introduces the new Amazon Machine Learning service.
Amazon Machine Learning is a service that makes it easy for developers of all skill levels to use machine learning technology. Amazon Machine Learning provides visualization tools and wizards that guide you through the process of creating machine learning (ML) models without having to learn complex ML algorithms and technology. Once your models are ready, Amazon Machine Learning makes it easy to obtain predictions for your application using simple APIs, without having to implement custom prediction generation code, or manage any infrastructure.
Distinguishing the hype from reality can be a bit confusing, especially when you consider the attention that AI gets from the media and commentators. So, how can your organisation get started and put AI to work for you? That is the question I will answer in this talk. From greater customer intimacy, increasing competitive advantage and improving efficiency, I will discuss and show how AI can be used today and help the organisation in more impactful ways.
Make your solution see, hear and talk, leveraging artificial intelligence services based on deep learning and neural networks. We will discover three new AI tools from AWS - Lex, Polly and Rekognition; integrated with a physical world device for human interaction and environmental awareness
Speaker: Sunil Mallya, Solutions Architect, AWS Deep Learning
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
Use Amazon Rekognition to Build a Facial Recognition SystemAmazon Web Services
Amazon Rekognition makes it easy to extract meaningful metadata from visual content. In this workshop, you will work in teams to build a simple system to help track missing persons. You’ll develop a solution that leverages Amazon Rekognition and other AWS services to analyze images from various sources (e.g., social media) and provide authorities with timely reports and alerts on new leads for missing individuals. The solution will entail a repeatable and automated process that follows best practices for architecting in the cloud, such as designing for high availability and scalability.
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
Taking Complexity Out of Data Science with AWS and Zoomdata PPTAmazon Web Services
Zoomdata helps you find quick and easy solutions for even the most complicated Big Data challenges. To meet their Big Data challenges, Blue Canopy designed an AWS-based Data Science Workstation (DSWS) with Zoomdata’s blazing-fast, secure data analysis and visualization platform. Blue Canopy provides information technology and cyber security solutions to US government and commercial enterprises and needed a solution that could expand the depth and breadth of their analytics capabilities for big data projects. They built a Data Science Workstation, which is a self-provisioned tool on AWS, that delivers on-demand services for data analysts.
Exploring the Business Use Cases for Amazon Rekognition - June 2017 AWS Onlin...Amazon Web Services
Learning Objectives:
- Learn about using image analysis with Amazon Rekognition - Learn about popular use cases for Amazon Rekognition
- Learn how specific AWS customers have implemented Amazon Rekognition in different workflows
Amazon Rekognition is a service that makes it easy to add image analysis to your applications. You can detect objects, scenes, faces; search and compare faces; and identify inappropriate content in images, In this tech talk, we will introduce Amazon Rekognition and walk through use cases for media and entertainment, hospitality and public safety, where Amazon Rekognition’s computer vision capabilities create the potential to streamline existing workflows to reduce time to production subsequently reduce costs and improve service quality and delivery for customers and citizens.
AWS Summit Singapore - Get to Know Your Customers - Modern Data ArchitectureAmazon Web Services
Craig Stires, Business Development Manager - Big Data & Analytics, APAC, AWS
Your customers probably want a better experience with your brand. Your different business teams want and need better insights in their decision making. Almost certainly, your finance and operations teams require this to happen at a fraction of the cost of traditional on-premises options. Modern data architectures on AWS help many of our best customers realise all of those goals. Your business data contains critical information about customer behaviours, operational decisions, and many factors that have financial impact on your organisation. Increasingly, this data sits beyond your transactional systems, and is too big, too fast, and too complex for existing systems to handle. AWS Data and Analytics services are designed from our customers' requirements to ingest, store, analyse, and consume information at record-breaking scale. In this session you will learn how these services work together to deliver business automation, enhance customer engagement and intelligence.
Demystifying Machine Learning On AWS - AWS Summit Sydney 2018Amazon Web Services
Demystifying Machine Learning on AWS
Machine Learning is having a major impact in our society, but how can we simplify the build, train, and deploy process for all developers and data scientists? Understand how cloud-based machine learning frameworks can help turn your data into intelligence. We will introduce the general machine learning process utilising the AWS Deep Learning AMIs and hear from carsales.com.au about how they built the Cyclops, a Super Human Image Recognition Software on AWS. We will then discuss the new capabilities delivered by Amazon SageMaker and how this product will further reduce the undifferentiated heavy lifting; freeing you up to focus on your business and allow your developers to quickly and easily expand into the world of Machine Learning.
Jenny Davies, Solutions Architect, Amazon Web Services and Agustinus Nalwan, AI and Machine Learning Technical Development Manager, Carsales.com.au
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.
In this session from the London AWS Summit 2015 Tech Track Replay, AWS Technical Evangelist Ian Massingham introduces the new Amazon Machine Learning service.
Amazon Machine Learning is a service that makes it easy for developers of all skill levels to use machine learning technology. Amazon Machine Learning provides visualization tools and wizards that guide you through the process of creating machine learning (ML) models without having to learn complex ML algorithms and technology. Once your models are ready, Amazon Machine Learning makes it easy to obtain predictions for your application using simple APIs, without having to implement custom prediction generation code, or manage any infrastructure.
Distinguishing the hype from reality can be a bit confusing, especially when you consider the attention that AI gets from the media and commentators. So, how can your organisation get started and put AI to work for you? That is the question I will answer in this talk. From greater customer intimacy, increasing competitive advantage and improving efficiency, I will discuss and show how AI can be used today and help the organisation in more impactful ways.
Amazon Web Services (AWS) es una plataforma de servicios de nube que ofrece potencia de cómputo, almacenamiento de bases de datos, entrega de contenido y otra funcionalidad para ayudar a las empresas a escalar y crecer. Explore cómo millones de clientes aprovechan los productos y soluciones de la nube de AWS para crear aplicaciones sofisticadas y cada vez más flexibles, escalables y fiables.
Self-Driving cars. Commercial drones. Smart cameras. Movie and music creation. Powerful & intelligent robots. Over the past few years, a new revolution has brought AI almost to the level of science-fiction. However, most companies are not worried about far-off futuristic applications of AI, they want to know what AI can do - today - for their organisations. Distinguishing the hype from reality can be a bit confusing, especially when you consider the attention that AI gets from the media and commentators. So, how can your organisation get started and put AI to work for you? That is the question I will answer in this talk. From greater customer intimacy, increasing competitive advantage and improving efficiency, I will discuss and show how AI can be used today and help the organisation in more impactful ways.
AWS Data-Driven Insights Learning Series_ANZ Sep 2019 Part 2Amazon Web Services
AWS has been supporting companies across Australia and New Zealand to put their most innovative tools and technologies to work to achieve their business needs and goals. AWS and our ecosystem of partners has helped the likes of CP Mining, IntelliHQ, WesCEF, Oz Minerals, Woodside and many more to modernise their analytics and data architecture in order to successfully generate business value from their data.
This event series aimed to educate customers with a broader understanding of how to build next-gen data lakes and analytics platforms and make connections with AWS.
Big Data Meets AI - Driving Insights and Adding Intelligence to Your SolutionsAmazon Web Services
Over 90% of today’s data has been generated in the last two years, and growth rates continue to climb. In this session, we’ll step through challenges and best practices with data capturing, how to derive meaningful insights to help predict the future, and common pitfalls in data analysis.
Come discover how integrated solutions involving Amazon S3, AWS Glue, Amazon Redshift, Amazon Athena, Amazon EMR, Amazon Kinesis, and Amazon Machine Learning, and Deep Learning on AWS result in effective data systems for data scientists and business users, alike.
Ben Snively, Principal Solutions Architect, AWS; Kate Werling, Solutions Architect, AWS
Amazon AI services bring natural language understanding (NLU), automatic speech recognition (ASR), visual search and image recognition, text-to-speech (TTS), and ML technologies within the reach of every developer. In this session, we will dive deep into 2 specific AWS services: Amazon Lex and Amazon Polly. Amazon Lex uses the same technology as Amazon Alexa to provide advanced deep learning functionalities of automatic speech recognition (ASR) and natural language understanding (NLU) to enable you to build applications with conversational interfaces, commonly called chatbots. Amazon Polly is a service that turns text into lifelike speech. Polly lets you create applications that speak in over two dozen languages with a wide variety of natural sounding male and female voices to enable you to build entirely new categories of speech-enabled products.
AWS Summit Singapore - Building Tomorrow’s Bank with AI/ MLAmazon Web Services
Olivier Klein, Head of Emerging Technologies, APAC, AWS. AWS Customer Speakers:
Kyle McNamara, Executive General Manager, Program Management Office, National Australia Bank
Chye Kit Chionh, CEO, Cynopsis Solutions.
Machine learning is transforming how we interact with our customers and helps us make faster and better decisions within our business. Intelligent and continuously improving neural networks help to streamline written and spoken customer conversations, help improve customer service centers or ensure compliance within your organization. Deep Learning machine models are trained to automatically detect fraudulent activities or make best next action decisions. Attend this session to learn how machine learning is used in the financial service industry to improve operational excellence, compliance and improve customer experience. Furthermore learn from our customers NAB and Cynopsis Solutions how they drive digital insights, improve customer satisfaction across all digital channels and consolidate their data lakes to drive continuous ML efforts.
Artificial Intelligence (AI) services on the AWS cloud bring deep learning (DL) technologies like natural language understanding (NLU), automatic speech recognition (ASR), image recognition and computer vision (CV), text-to-speech (TTS), and machine learning (ML) within reach of every developer. In this session, you will be introduced to several new AI services: Amazon Lex, to build sophisticated text and voice chatbots; Amazon Rekognition, for deep learning-based image recognition; and Amazon Polly, for turning text into lifelike speech. The opportunities to apply one or more of these DL services are nearly boundless and this session will provide a number of examples and use cases to help you get started.
AWS offers a family of intelligent services that provide cloud-native machine learning and deep learning technologies to address different use cases and needs. This deck will help you to gain insight into practical use cases for Amazon Lex, Amazon Polly, and Amazon Rekognition, and learn about newly announced services Amazon Rekognition Video, Amazon Comprehend, Amazon Translate, and Amazon Transcribe. This presentation took place in Australia and New Zealand as part of the AWS Learning Series in 2018.
The Machine Learning Factory: Automation of the ML Lifecycle
Speaker:
Jason Barto, AWS Solutions Architect, AWS
The lifecycle of a machine learning model, and more importantly the business insights it offers, is an iterative and ever evolving process. From feature discovery and engineering, to model training and selection, even through to production hosting and drift detection, AWS services can support and automate the events that lead to change in a customer’s model.
Join us to see a demonstration of how AWS services can be used to transform raw data into an engineered feature set that then triggers the training and evaluation of an updated model. This session will address topics such as context drift, secure hosting of trained models as a RESTful API, and automation for retraining models when data or code changes.
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
Unlocking New Todays - Artificial Intelligence and Data Platforms on AWSAmazon Web Services
熱門創新服務專題
Unlocking New Todays: Artificial Intelligence and Data Platforms on AWS (Level 200)
Speaker: Paul Yung, Head of Territory Development HKT, AWS
Similar to Artificial Intelligence on the AWS Platform (20)
How can your business benefit from going serverless?Adrian Hornsby
Serverless architectures let you build and deploy applications and services with infrastructure resources that require zero administration. In the past, you had to provision and scale servers to run your application code to install and operate distributed databases and build and run custom software to handle API requests. Now AWS provides a stack of scalable fully-managed services that eliminates these operational complexities. In this session, you will learn about the basics of serverless and especially how your business can benefit from it.
Moving Forward with AI - as presented at the Prosessipäivät 2018Adrian Hornsby
https://www.oppia.fi/prosessipaivat/
-
Self-Driving cars. Commercial drones. Smart cameras. Movie and music creation. Powerful & intelligent robots. Over the past few years, a new revolution has brought AI almost to the level of science-fiction. However, most companies are not worried about far-off futuristic applications of AI, they want to know what AI can do - today - for their organisations. Distinguishing the hype from reality can be a bit confusing, especially when you consider the attention that AI gets from the media and commentators. So, how can your organisation get started and put AI to work for you? That is the question I will answer in this talk. From greater customer intimacy, increasing competitive advantage and improving efficiency, I will discuss and show how AI can be used today and help the organisation in more impactful ways.
Chaos Engineering: Why Breaking Things Should Be Practised.Adrian Hornsby
As presented at the AWS London Summit 2018
With the rise of micro-services and large-scale distributed architectures, software systems have grown increasingly complex and hard to understand. Adding to that complexity, the velocity of software delivery has also dramatically increased, resulting in failures being harder to predict and contain.
While the cloud allows for high availability, redundancy and fault-tolerance, no single component can guarantee 100% uptime. Therefore, we have to understand availability but especially learn how to design architectures with failure in mind.
And since failures have become more and more chaotic in nature, we must turn to chaos engineering in order to identify failures before they become outages.
In this talk, I will deep dive into availability, reliability and large-scale architectures and make an introduction to chaos engineering, a discipline that promotes breaking things on purpose in order to learn how to build more resilient systems.
Chaos Engineering: Why Breaking Things Should Be Practised.Adrian Hornsby
With the rise of micro-services and large-scale distributed architectures, software systems have grown increasingly complex and hard to understand. Adding to that complexity, the velocity of software delivery has also dramatically increased, resulting in failures being harder to predict and contain.
While the cloud allows for high availability, redundancy and fault-tolerance, no single component can guarantee 100% uptime. Therefore, we have to understand availability but especially learn how to design architectures with failure in mind.
And since failures have become more and more chaotic in nature, we must turn to chaos engineering in order to identify failures before they become outages.
In this talk, I will deep dive into availability, reliability and large-scale architectures and make an introduction to chaos engineering, a discipline that promotes breaking things on purpose in order to learn how to build more resilient systems.
Slides from my talk at the Data Innovations Summit on MXNet Model Server.
https://www.datainnovationsummit.com/
Apache MXNet Model Server (MMS) is a flexible and easy to use tool for serving deep learning models exported from MXNet or the Open Neural Network Exchange (ONNX).
https://github.com/awslabs/mxnet-model-server
Building a Multi-Region, Active-Active Serverless Backends.Adrian Hornsby
From understanding reliability and availability, this talks walks you through the why and the how of building multi-region, active-active applications, and especially why serverless is a great fit.
The slides from my talk at the AWS DevDays in the Nordics.
https://aws.amazon.com/events/Devdays-Nordics/agenda/
Objectives:
- Understand Serverless Key Concepts.
- Understand Event Processing Architecture.
- Understand Operation Automation Architecture.
- Understand Web Application Architecture.
- Understand Data Processing Architecture.
* Kinesis-based apps.
* IoT-based apps.
re:Invent re:Cap - An overview of Artificial Intelligence and Machine Learnin...Adrian Hornsby
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.
re:Invent re:Cap - Big Data & IoT at Any ScaleAdrian Hornsby
This session covers the most recent Big Data & IoT announcements at re:Invent. Learn about trends and use cases for understanding your data and implementing an Internet of Things (IoT) project. Hear about how AWS customers are using AWS IoT to connect their devices to the cloud and solve business challenges with IoT.
Devoxx: Building AI-powered applications on AWSAdrian Hornsby
Slides from my talk at devoxx2018
The video: https://www.youtube.com/watch?v=-izfBVlHkSc
https://cfp.devoxx.be/2017/talk/XEO-9942/Building_Serverless_AI-powered_Applications_on_AWS
Slides from my talk at the first AWS Community Day in Bangalore
https://www.meetup.com/awsugblr/events/243819403/
Speaker notes: https://medium.com/@adhorn/10-lessons-from-10-years-of-aws-part-1-258b56703fcf
and https://medium.com/@adhorn/10-lessons-from-10-years-of-aws-part-2-5dd92b533870
The list is not in any particular order :)
Developing Sophisticated Serverless Applications with AIAdrian Hornsby
The slides from my talk at the Serverless Summit in India http://inserverless.com
Developing advanced AI enabled applications with serverless technology on AWS
Journey Towards Scaling Your API to 10 Million UsersAdrian Hornsby
The slides from my talk at the NordicAPI summit 2017:
https://nordicapis.com/sessions/journey-towards-scaling-application-10-million-users/
A collection of thoughts and ideas that I experienced during my 10 years working with AWS Cloud.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
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.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
21. Text In, Life-like Speech Out
Amazon Polly
“Today in Seattle, WA
it’s 11°F”
“Today in Seattle Washington
it’s 11 degrees Fahrenheit”
22. “Today in Seattle, WA, it’s 11°F”
‘"We live for the music" live from the Madison Square Garden.’
1. Automatic, Accurate Text Processing
A Focus On Voice Quality & Pronunciation
23. 2. Intelligible and Easy to Understand
1. Automatic, Accurate Text Processing
A Focus On Voice Quality & Pronunciation
25. 2. Intelligible and Easy to Understand
3. Add Semantic Meaning to Text
“Richard’s number is 2122341237“
“Richard’s number is 2122341237“
Telephone Number
1. Automatic, Accurate Text Processing
A Focus On Voice Quality & Pronunciation
26. 2. Intelligible and Easy to Understand
3. Add Semantic Meaning to Text
4. Customized Pronunciation
“My daughter’s name is Kaja.”
“My daughter’s name is Kaja.”
1. Automatic, Accurate Text Processing
A Focus On Voice Quality & Pronunciation
27. Duolingo voices its language learning service Using Polly
Duolingo is a free language learning service where
users help translate the web and rate translations.
With Amazon Polly our users
benefit from the most lifelike
Text-to-Speech voices
available on the market.
Severin Hacker
CTO, Duolingo
”
“
• Spoken language crucial for language
learning
• Accurate pronunciation matters
• Faster iteration thanks to TTS
• As good as natural human speech
29. Amazon
Rekognition
Object and scene detection
Facial analysis
Face comparison
Celebrity recognition
Image moderation
Amazon Rekognition: Images In, Rich Metadata
Out
34. Image moderation
Explicit Nudity
Nudity
Graphic Male Nudity
Graphic Female Nudity
Sexual Activity
Partial Nudity
Suggestive
Female Swimwear or Underwear
Male Swimwear or Underwear
Revealing Clothes
35. Amazon Rekognition
Customers
• Digital Asset Management
• Media and Entertainment
• Travel and Hospitality
• Influencer Marketing
• Systems Integration
• Digital Advertising
• Consumer Storage
• Law Enforcement
• Public Safety
• eCommerce
• Education
37. Speech Recognition & Natural
Language Understanding
Amazon Lex
Automatic Speech Recognition
Natural Language Understanding
“What’s the weather
forecast?”
Weather
Forecast
38. Speech Recognition & Natural
Language Understanding
Amazon Lex
Automatic Speech Recognition
Natural Language Understanding
“What’s the weather
forecast?”
“It will be sunny
and 25°C”
Weather
Forecast
45. And a few more examples…
Fraud detection Detecting fraudulent transactions, filtering spam emails,
flagging suspicious reviews, …
Personalization Recommending content, predictive content loading,
improving user experience, …
Targeted marketing Matching customers and offers, choosing marketing
campaigns, cross-selling and up-selling, …
Content classification Categorizing documents, matching hiring managers and
resumes, …
Churn prediction Finding customers who are likely to stop using the
service, free-tier upgrade targeting, …
Customer support Predictive routing of customer emails, social media
listening, …
46. Amazon Machine Learning helps us
reduce complexity and make sense of
emerging fraud patterns. We can see
correlations we wouldn’t have been able to
see otherwise and answer questions it
would have taken us way too long to
answer ourselves.
Oliver ClarkCTO
Fraud.net Case Study
Fraud.net is the world’s leading
crowdsourced fraud prevention platform,
aggregating and analyzing large amounts
of fraud data from thousands of online
merchants in real time. A collaborative
program, Fraud.net is currently the largest
merchant-led effort to combat online
payment fraud, which costs U.S.
merchants an estimated $20 billion
annually. The platform protects more than
2 percent of all U.S. e-commerce, and its
client base and data requirements are
growing at a pace of more than 1,000
percent per year.
47.
48. Howard Hughes Corp
Creating an enterprise data lake on Amazon S3 by The
Howard Hughes Corporation and 47Lining. Their
business analytics built a lead-scoring model using
Amazon Machine Learning (Amazon ML) to predict
propensity to purchase high-end real estate. Some pretty
impressive numbers – 400% increase in the number of
identified qualified leads in their pipeline and more than
10x reduction is lead acquisition cost.
https://www.youtube.com/watch?v=o7atIw2ntgw
53. Zillow Provides Near-Real-Time Home-Value
Estimates Using Amazon Kinesis
Zillow Group increases machine-learning calculation
performance and scalability and delivers near-real-time
home-valuation data to customers using AWS. The
company houses a portfolio of the largest online real-
estate and home-related brands. Zillow Group runs the
Zestimate, its machine learning–based home-valuation
tool, on Amazon Kinesis and Apache Spark on Amazon
EMR.
Zestimate
54.
55. FINRA Anomaly detection, sequence matching, regression
analysis, network/tribe analysis
https://aws.amazon.com/blogs/big-data/low-latency-access-on-trillions-of-records-finras-
architecture-using-apache-hbase-on-amazon-emr-with-amazon-s3/
The Financial Industry Regulatory Authority (FINRA) is a
private sector regulator responsible for analyzing 99% of
the equities and 65% of the option activity in the US. In
order to look for fraud, market manipulation, insider
trading, and abuse, FINRA’s technology group has
developed a robust set of big data tools in the AWS
Cloud to support these activities.
Early in the 2 ½ year migration of FINRA’s Market
Regulation Portfolio to the AWS Cloud, FINRA developed
a system on AWS to replace an on-premises solution
that allowed analysts to query this trade activity. This
solution provided fast random access across trillions of
trade records, which would quickly grow to over 700 TB
of data.
Most critical system!
56. FINRA
FINRA, the—the Financial Industry Regulatory Authority—is all in on AWS and has gained massive
performance improvements in its stock-market surveillance system, including 400 percent faster response
times, by using the AWS cloud. FINRA, one of the largest independent securities regulators in the United
States, was established to monitor and regulate financial trading practices. The organization is moving all of its
IT infrastructure to AWS and closing data centers in the process. FINRA is moving its databases that ingest and
store billions of financial transaction records daily from Oracle to Amazon RDS and Amazon Aurora,
leveraging AWS database technologies for capturing and storing a daily influx of more than 75 billion financial
records. Steve Randich, EVP and Chief Information Officer, spoke onstage at re:Invent 2016.
60. One-Click
Deep Learning
AWS Deep Learning AMIs
Amazon Linux & Ubuntu
Up to~40k CUDA cores
Apache MXNet
TensorFlow
Theano
Keras
Caffe
CNTK
Torch
Pre-configured CUDA drivers
Anaconda, Python3
Out-of-the-box Tutorials
+ CloudFormation template
+ Container Image
Available in the AWS Marketplace
66. Create your own Basquiat with Deep Learning
https://becominghuman.ai/create-your-own-basquiat-with-deep-learning-for-much-less-than-110-million-314aa07c9ba8
72. Up to
40 thousand parallel processing cores
70 teraflops (single precision)
over 23 teraflops (double precision)
Instance Size GPUs GPU Peer
to Peer
vCPUs Memory
(GiB)
Network
Bandwidth*
p2.xlarge 1 - 4 61 1.25Gbps
p2.8xlarge 8 Y 32 488 10Gbps
p2.16xlarge 16 Y 64 732 20Gbps
*In a placement group
Amazon EC2 P2 Instances
73.
74.
75.
76.
77.
78.
79. NVIDIA TESLA V100
The Most Advanced Data Center GPU Ever Built.
640 Tensor Cores
120 teraflops
Pre-optimized for Apache MXNet
80. FPGA Images Available In AWS Marketplace
F 1 I n s t a n c e
W i t h y o u r c u s t o m l o g i c
r u n n i n g o n a n F P G A
D e v e l o p , s i m u l a t e , d e b u g
& c o m p i l e y o u r c o d e
P a c k a g e a s F P G A
I m a g e s
F1 Instances:
Bringing Hardware Acceleration To All
81. “FPGAs may become the platform of
choice for accelerating next-
generation DNNs.”
E Nurvitadhi - 2017