The document discusses innovations in artificial intelligence and cloud computing. It describes how AWS services like Amazon Rekognition, Amazon Polly, and AWS Deep Learning AMIs can be used to build applications involving computer vision, natural language processing, and deep learning. Examples are provided of using these services for image recognition, text-to-speech, and deploying pre-trained machine learning models for inference at scale. The document advocates that these AWS AI services allow for the democratization of AI and deep learning.
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.
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
Bringing Characters to Life with Amazon Polly Text-to-Speech - AWS Online Tec...Amazon Web Services
Learning Objectives:
- Using Text-to-Speech to voice characters in game production, i.e. character speech animation
- Amazon Polly & Lumberyard integration
- Learn how to create lip-syncing avatars, karaoke style text highlighting, and integrate speech capabilities into the gaming engines
NEW LAUNCH! Feature updates for Amazon Rekognition - MCL336 - re:Invent 2017Amazon Web Services
In this session. We will provide an overview of the latest Amazon Rekognition features including real-time face recognition, Text in Image recognition, and improved face detection.
Amazon Rekognition recently added three new features: detection and recognition of text in images; real-time face recognition across tens of millions of faces; and detection of up to 100 faces in challenging crowded photos. In this session, we will cover features, benefits and use cases for these latest Rekognition features, while highlighting customer examples and a brief demo showcasing Amazon Rekognition.
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.
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
Bringing Characters to Life with Amazon Polly Text-to-Speech - AWS Online Tec...Amazon Web Services
Learning Objectives:
- Using Text-to-Speech to voice characters in game production, i.e. character speech animation
- Amazon Polly & Lumberyard integration
- Learn how to create lip-syncing avatars, karaoke style text highlighting, and integrate speech capabilities into the gaming engines
NEW LAUNCH! Feature updates for Amazon Rekognition - MCL336 - re:Invent 2017Amazon Web Services
In this session. We will provide an overview of the latest Amazon Rekognition features including real-time face recognition, Text in Image recognition, and improved face detection.
Amazon Rekognition recently added three new features: detection and recognition of text in images; real-time face recognition across tens of millions of faces; and detection of up to 100 faces in challenging crowded photos. In this session, we will cover features, benefits and use cases for these latest Rekognition features, while highlighting customer examples and a brief demo showcasing Amazon Rekognition.
Women in Big Data Forum’s mission is to strengthen the diversity in the big data field. As part of this initiative, they encourage and attract more female talent to the big data & analytics field and help them to connect, engage and grow.
NEW LAUNCH! Introducing Amazon Translate – Now in Preview - MCL209 - re:Inven...Amazon Web Services
Amazon Translate is a neural machine translation service that delivers fast, high-quality, and affordable language translation. Neural machine translation uses deep learning to deliver more accurate and more natural sounding translation than older statistical and rule-based translation algorithms. Amazon Translate enables translation at scale so that you can easily translate large volumes of text efficiently to handle tasks like localizing content for international users and facilitating real-time cross-lingual communication.
Join this session to learn more and find out how you get can started with Amazon Translate, today!
Building AI-powered Serverless Applications on AWSAdrian Hornsby
Slides from my talk at the AWSLoft in London
https://awsloft.london/session/2017/a5da881d-67f8-4af5-8ace-4f8adcf579db
"In this talk, we will show the audience how to build and deploy serverless AI-powered applications on AWS. In particular, two demos will be analysed in depths. The first demo is a simple mobile web app that allows a user to upload or take a picture with their mobile phone. The result is then spoken out loud using Amazon Polly. This demo is deployed using the AWS CLI (command line interface) with scripting techniques. The second demo is a podcast generator which connects to any RSS feed and converts that feed into a podcast. The result can then be played on iTunes or any podcast player. This demo uses AWS Lambda and Amazon Polly and is deployed using the Serverless framework. We will go through the architecture, the APIs, the code itself and the deployment of those two applications using Amazon Rekognition, Amazon Polly, AWS Lambda, Amazon S3, Amazon Route53, Elasticsearch, and more."
Amazon Polly Tips and Tricks: How to Bring Your Text-to-Speech Voices to Life...Amazon Web Services
Although there are many ways to optimize the speech generated by Amazon Polly's text-to-speech voices, you might find it challenging to apply the most effective enhancements in each situation. Learn how you can control pronunciation, intonation, and timing for text-to-speech voices. In this session, you get a comprehensive overview of the available tools and methods available for modifying Amazon Polly speech output, including SSML tags, lexicons, and punctuation. You also get recommendations for streamlining application of these techniques. Come away with insider tips on the best speech optimization techniques to provide a more natural voice experience.
NEW LAUNCH! Introducing Amazon Transcribe – Now in Preview - MCL215 - re:Inve...Amazon Web Services
Amazon Transcribe is an automatic speech recognition (ASR) service that makes it easy for developers to add speech to text capability to their applications. The ASR service can be used across a breadth of industries. For example, customer contact centers can convert call recordings into text for further analysis of what drives positive outcomes; media content producers can automate subtitling workflows for greater reach, and marketers and advertisers can enhance content discovery and display more targeted advertising based on the extracted metadata.
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.
Building Your Smart Applications with Machine Learning on AWS | AWS WebinarAmazon Web Services
Machine Learning (ML) has long been an arcane topic, accessible only to experts. In this webinar, you will learn how to easily add Amazon API-driven ML services to your education software. Image and video analysis, text-to-speech, speech-to-text, translation, natural language processing: all these are just an API call away. Through code-level demos, we'll show you how to quickly start integrating these services into your education offerings, with zero ML expertise required.
Speaker: Julien Simon, Principal Evangelist AI/ML EMEA, Amazon Web Services
Learn more: https://aws.amazon.com/education
View the video recording here: https://youtu.be/Dsj5KgER6ec
NEW LAUNCH! Amazon Rekognition Video eliminates manual cataloging of video wh...Amazon Web Services
During this session, we will provide an overview of Amazon Rekognition Video, a deep learning powered video analysis service that tracks people, detects activities, and recognizes objects, celebrities, and inappropriate content. Amazon Rekognition Video can detect and recognize faces in live streams. Rekognition Video also analyzes existing video stored in Amazon S3 and returns specific labels of activities, people and faces, and objects with time stamps so you can easily locate the scene. For people and faces, it also returns the bounding box, which is the specific location of the person or face in the frame. We will also cover different use cases for Amazon Rekognition Video in applications such as security and public safety, and media and entertainment.
Training Chatbots and Conversational Artificial Intelligence Agents with Amaz...Amazon Web Services
Building a conversational AI experience that can respond to a wide variety of inputs and situations depends on gathering high-quality, relevant training data. Dialog with humans is an important part of this training process. In this session, learn how researchers at Facebook use Amazon Mechanical Turk within the ParlAI (pronounced “parlay”) framework for training and evaluating AI models to perform data collection, human training, and human evaluation. Learn how you can use this interface to gather high-quality training data to build next-generation chatbots and conversational agents.
How to Build a Backend for an Alexa Smart Home Skill - ALX316 - re:Invent 2017Amazon Web Services
In this hands-on workshop, you learn how to build a skill and a supporting backend for Alexa that uses the APIs for Alexa Smart Home. This session involves creating a solution that implements some of the key services in supporting an Alexa Smart Home Skill including directive and event handling, device discovery, endpoint messages, and asynchronous messaging.
AWS Machine Learning Week SF: Build an Image-Based Automatic Alert System wit...Amazon Web Services
AWS Machine Learning Week at the San Francisco Loft: Build an Image-Based Automatic Alert System with Amazon Rekognition
This hands-on workshop will walk through how to build a solution that listens and captures images from Twitter, and then compares those images against a reference image to automatically notify you about a new post featuring your favorite celebrity. Additionally, we will integrate sentiment analysis into this image-based automatic alert system in order to gauge whether the determined celebrities are happy, sad, etc. in the posted image.
Speaker: Sireesha Muppala - Solutions Architect, AWS
Artificial Intelligence (AI) services on the AWS cloud bring deep learning (DL) technologies like natural language understanding (NLU), automatic speech recognition (ASR), image recognition and computer vision (CV), text-to-speech (TTS), and machine learning (ML) within reach of every developer. In this session, you will be introduced to several new AI services: Amazon Lex, to build sophisticated text and voice chatbots; Amazon Rekognition, for deep learning-based image recognition; and Amazon Polly, for turning text into lifelike speech. The opportunities to apply one or more of these DL services are nearly boundless and this session will provide a number of examples and use cases to help you get started.
ALX326_Applying Alexa’s Natural Language to Your ChallengesAmazon Web Services
In this session, we will give you a complete picture of all the tools and techniques required to build complex, production-quality Alexa skills. You will leave this session knowing how to use Alexa's dialog management, entity resolution, and slot elicitation capabilities as well as how to process the results through a microservice with AWS Lambda.
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.
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
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.
Workshop Build an Image-Based Automatic Alert System with Amazon Rekognition:...Amazon Web Services
Machine Learning Workshops at the San Francisco Loft
Build an Image-Based Automatic Alert System with Amazon Rekognition
This hands-on workshop will walk through how to build a solution that listens and captures images from Twitter, and then compares those images against a reference image to automatically notify you about a new post featuring your favorite celebrity. Additionally, we will integrate sentiment analysis into this image-based automatic alert system in order to gauge whether the determined celebrities are happy, sad, etc. in the posted image.
Level: 200-300
Speakers:
Wayne Davis - Solutions Architect, AWS
Tristan Li - Solutions Architect, AWS
An Overview of AI on the AWS Platform - June 2017 AWS Online Tech TalksAmazon Web Services
Learning Objectives:
- Learn about the breadth of AI services available on the AWS Cloud
- Gain insight into Amazon Lex, Amazon Polly, and Amazon Rekognition
- Learn more about why Apache MXNet is the deep learning framework of choice for AWS
Add Intelligence to Applications with AWS ML: Machine Learning Workshops SFAmazon Web Services
Machine Learning Workshops 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: Liam Morrison - Principal Solutions Architect, AWS
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.
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 get started with Artificial Intelligence on the AWS Cloud. In particular, we will explore AWS cloud-native machine learning and deep learning technologies that address a range of different use cases and needs. These include AWS Lex, which provides natural language understanding (NLU) and automatic speech recognition (ASR); Amazon Rekognition, which provides visual search and image recognition capabilities; Amazon Polly for text-to-speech (TTS) capabilities; and Amazon Machine Learning tools. The session will also cover the AWS Deep Learning AMI, which lets you run deep learning in the cloud at any scale. You can use launch instances of the AMI, pre-installed with open source deep learning engines (Apache MXNet, TensorFlow, Caffe, Theano, Torch and Keras), to run sophisticated AI models, experiment with new algorithms, and learn new deep learning skills and techniques; all backed by auto-scaling clusters of GPU-based instances.
Women in Big Data Forum’s mission is to strengthen the diversity in the big data field. As part of this initiative, they encourage and attract more female talent to the big data & analytics field and help them to connect, engage and grow.
NEW LAUNCH! Introducing Amazon Translate – Now in Preview - MCL209 - re:Inven...Amazon Web Services
Amazon Translate is a neural machine translation service that delivers fast, high-quality, and affordable language translation. Neural machine translation uses deep learning to deliver more accurate and more natural sounding translation than older statistical and rule-based translation algorithms. Amazon Translate enables translation at scale so that you can easily translate large volumes of text efficiently to handle tasks like localizing content for international users and facilitating real-time cross-lingual communication.
Join this session to learn more and find out how you get can started with Amazon Translate, today!
Building AI-powered Serverless Applications on AWSAdrian Hornsby
Slides from my talk at the AWSLoft in London
https://awsloft.london/session/2017/a5da881d-67f8-4af5-8ace-4f8adcf579db
"In this talk, we will show the audience how to build and deploy serverless AI-powered applications on AWS. In particular, two demos will be analysed in depths. The first demo is a simple mobile web app that allows a user to upload or take a picture with their mobile phone. The result is then spoken out loud using Amazon Polly. This demo is deployed using the AWS CLI (command line interface) with scripting techniques. The second demo is a podcast generator which connects to any RSS feed and converts that feed into a podcast. The result can then be played on iTunes or any podcast player. This demo uses AWS Lambda and Amazon Polly and is deployed using the Serverless framework. We will go through the architecture, the APIs, the code itself and the deployment of those two applications using Amazon Rekognition, Amazon Polly, AWS Lambda, Amazon S3, Amazon Route53, Elasticsearch, and more."
Amazon Polly Tips and Tricks: How to Bring Your Text-to-Speech Voices to Life...Amazon Web Services
Although there are many ways to optimize the speech generated by Amazon Polly's text-to-speech voices, you might find it challenging to apply the most effective enhancements in each situation. Learn how you can control pronunciation, intonation, and timing for text-to-speech voices. In this session, you get a comprehensive overview of the available tools and methods available for modifying Amazon Polly speech output, including SSML tags, lexicons, and punctuation. You also get recommendations for streamlining application of these techniques. Come away with insider tips on the best speech optimization techniques to provide a more natural voice experience.
NEW LAUNCH! Introducing Amazon Transcribe – Now in Preview - MCL215 - re:Inve...Amazon Web Services
Amazon Transcribe is an automatic speech recognition (ASR) service that makes it easy for developers to add speech to text capability to their applications. The ASR service can be used across a breadth of industries. For example, customer contact centers can convert call recordings into text for further analysis of what drives positive outcomes; media content producers can automate subtitling workflows for greater reach, and marketers and advertisers can enhance content discovery and display more targeted advertising based on the extracted metadata.
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.
Building Your Smart Applications with Machine Learning on AWS | AWS WebinarAmazon Web Services
Machine Learning (ML) has long been an arcane topic, accessible only to experts. In this webinar, you will learn how to easily add Amazon API-driven ML services to your education software. Image and video analysis, text-to-speech, speech-to-text, translation, natural language processing: all these are just an API call away. Through code-level demos, we'll show you how to quickly start integrating these services into your education offerings, with zero ML expertise required.
Speaker: Julien Simon, Principal Evangelist AI/ML EMEA, Amazon Web Services
Learn more: https://aws.amazon.com/education
View the video recording here: https://youtu.be/Dsj5KgER6ec
NEW LAUNCH! Amazon Rekognition Video eliminates manual cataloging of video wh...Amazon Web Services
During this session, we will provide an overview of Amazon Rekognition Video, a deep learning powered video analysis service that tracks people, detects activities, and recognizes objects, celebrities, and inappropriate content. Amazon Rekognition Video can detect and recognize faces in live streams. Rekognition Video also analyzes existing video stored in Amazon S3 and returns specific labels of activities, people and faces, and objects with time stamps so you can easily locate the scene. For people and faces, it also returns the bounding box, which is the specific location of the person or face in the frame. We will also cover different use cases for Amazon Rekognition Video in applications such as security and public safety, and media and entertainment.
Training Chatbots and Conversational Artificial Intelligence Agents with Amaz...Amazon Web Services
Building a conversational AI experience that can respond to a wide variety of inputs and situations depends on gathering high-quality, relevant training data. Dialog with humans is an important part of this training process. In this session, learn how researchers at Facebook use Amazon Mechanical Turk within the ParlAI (pronounced “parlay”) framework for training and evaluating AI models to perform data collection, human training, and human evaluation. Learn how you can use this interface to gather high-quality training data to build next-generation chatbots and conversational agents.
How to Build a Backend for an Alexa Smart Home Skill - ALX316 - re:Invent 2017Amazon Web Services
In this hands-on workshop, you learn how to build a skill and a supporting backend for Alexa that uses the APIs for Alexa Smart Home. This session involves creating a solution that implements some of the key services in supporting an Alexa Smart Home Skill including directive and event handling, device discovery, endpoint messages, and asynchronous messaging.
AWS Machine Learning Week SF: Build an Image-Based Automatic Alert System wit...Amazon Web Services
AWS Machine Learning Week at the San Francisco Loft: Build an Image-Based Automatic Alert System with Amazon Rekognition
This hands-on workshop will walk through how to build a solution that listens and captures images from Twitter, and then compares those images against a reference image to automatically notify you about a new post featuring your favorite celebrity. Additionally, we will integrate sentiment analysis into this image-based automatic alert system in order to gauge whether the determined celebrities are happy, sad, etc. in the posted image.
Speaker: Sireesha Muppala - Solutions Architect, AWS
Artificial Intelligence (AI) services on the AWS cloud bring deep learning (DL) technologies like natural language understanding (NLU), automatic speech recognition (ASR), image recognition and computer vision (CV), text-to-speech (TTS), and machine learning (ML) within reach of every developer. In this session, you will be introduced to several new AI services: Amazon Lex, to build sophisticated text and voice chatbots; Amazon Rekognition, for deep learning-based image recognition; and Amazon Polly, for turning text into lifelike speech. The opportunities to apply one or more of these DL services are nearly boundless and this session will provide a number of examples and use cases to help you get started.
ALX326_Applying Alexa’s Natural Language to Your ChallengesAmazon Web Services
In this session, we will give you a complete picture of all the tools and techniques required to build complex, production-quality Alexa skills. You will leave this session knowing how to use Alexa's dialog management, entity resolution, and slot elicitation capabilities as well as how to process the results through a microservice with AWS Lambda.
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.
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
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.
Workshop Build an Image-Based Automatic Alert System with Amazon Rekognition:...Amazon Web Services
Machine Learning Workshops at the San Francisco Loft
Build an Image-Based Automatic Alert System with Amazon Rekognition
This hands-on workshop will walk through how to build a solution that listens and captures images from Twitter, and then compares those images against a reference image to automatically notify you about a new post featuring your favorite celebrity. Additionally, we will integrate sentiment analysis into this image-based automatic alert system in order to gauge whether the determined celebrities are happy, sad, etc. in the posted image.
Level: 200-300
Speakers:
Wayne Davis - Solutions Architect, AWS
Tristan Li - Solutions Architect, AWS
An Overview of AI on the AWS Platform - June 2017 AWS Online Tech TalksAmazon Web Services
Learning Objectives:
- Learn about the breadth of AI services available on the AWS Cloud
- Gain insight into Amazon Lex, Amazon Polly, and Amazon Rekognition
- Learn more about why Apache MXNet is the deep learning framework of choice for AWS
Add Intelligence to Applications with AWS ML: Machine Learning Workshops SFAmazon Web Services
Machine Learning Workshops 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: Liam Morrison - Principal Solutions Architect, AWS
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.
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 get started with Artificial Intelligence on the AWS Cloud. In particular, we will explore AWS cloud-native machine learning and deep learning technologies that address a range of different use cases and needs. These include AWS Lex, which provides natural language understanding (NLU) and automatic speech recognition (ASR); Amazon Rekognition, which provides visual search and image recognition capabilities; Amazon Polly for text-to-speech (TTS) capabilities; and Amazon Machine Learning tools. The session will also cover the AWS Deep Learning AMI, which lets you run deep learning in the cloud at any scale. You can use launch instances of the AMI, pre-installed with open source deep learning engines (Apache MXNet, TensorFlow, Caffe, Theano, Torch and Keras), to run sophisticated AI models, experiment with new algorithms, and learn new deep learning skills and techniques; all backed by auto-scaling clusters of GPU-based instances.
This session walks you through some of the more advanced features offered in Alexa Skill Builder, like Dialog Management, Entity Resolution, state management, session persistence, and maintaining context. Using Dialog Management, you can engage skill users in a multi-turn dialog to elicit and confirm slots for an intent. Using Entity Resolution, you can greatly simplify slot management by mapping multiple synonyms of your slot to a unique ID. We couple these conversational techniques with the management of session state and persistence to enable memory and personalization.
ALX401-Advanced Alexa Skill Building Conversation and MemoryAmazon Web Services
This session walks you through some of the more advanced features offered in Alexa Skill Builder, like Dialog Management, Entity Resolution, state management, session persistence, and maintaining context. Using Dialog Management, you can engage skill users in a multi-turn dialog to elicit and confirm slots for an intent. Using Entity Resolution, you can greatly simplify slot management by mapping multiple synonyms of your slot to a unique ID. We couple these conversational techniques with the management of session state and persistence to enable memory and personalization.
NEW LAUNCH! Introducing Amazon Sumerian – Build VR/AR and 3D Applications - M...Amazon Web Services
AWS has launched Amazon Sumerian. Sumerian lets you create and run virtual reality (VR), augmented reality (AR), and 3D applications quickly and easily without requiring any specialized programming or 3D graphics expertise. In this session, we will introduce you to Sumerian, and how you can build highly immersive and interactive scenes for the enterprise that run on popular hardware such as Oculus Rift, HTC Vive, and iOS mobile devices.
Using Access Advisor to Strike the Balance Between Security and Usability - S...Amazon Web Services
AWS provides a killer feature for security operations teams: Access Advisor. In this session, we discuss how Access Advisor shows the services to which an IAM policy grants access and provides a timestamp for the last time that the role authenticated against that service. At Netflix, we use this valuable data to automatically remove permissions that are no longer used. By continually removing excess permissions, we can achieve a balance of empowering developers and maintaining a best-practice, secure environment.
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 :)
Deep Learning continues to push the state of the art in domains such as computer vision, natural language understanding, and recommendation engines. In this session, we provide an overview of Deep Learning focusing on relevant application domains. We introduce popular Deep Learning frameworks such as TensorFlow and Apache MXNet, and we discuss how to select the right fit for your targeted use cases. We also walk you through other key considerations for optimizing Deep Learning training and inference, including setting up and scaling your infrastructure on AWS.
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
Keith Steward - SageMaker Algorithms Infinitely Scalable Machine Learning_VK.pdfAmazon Web Services
Amazon SageMaker is a fully-managed service that enables developers and data scientists to quickly and easily build, train, and deploy machine learning (ML) models, at any scale. Amazon SageMaker provides high-performance, machine learning algorithms optimized for speed, scale, and accuracy, to perform training on petabyte-scale data sets. This webinar will introduce you to the collection of distributed streaming ML algorithms that come with Amazon SageMaker. You will learn about the difference between streaming and batch ML algorithms, and how SageMaker has been architected to run these algorithms at scale. We will demo Neural Topic Modeling of text documents using a sample SageMaker Notebook, which will be made available to attendees.
Moving Forward with AI - as presented at the Prosessipäivät 2018Adrian Hornsby
https://www.oppia.fi/prosessipaivat/
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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.
Building the Organisation of the Future: Leveraging Artificial Intelligence a...Amazon Web Services
Artificial intelligence and machine learning are no longer the stuff of science fiction. Organisations of all sizes are using these tools to create innovative artificial intelligences applications – namely, Amazon.com's own retail experience. Join us for an inside look at how Amazon thinks about this technology, and hear from Skinvision on how they’re using machine learning for early skin-cancer detection. Through these stories, gain insight into a range of new machine learning services on AWS for use in your own business.
Breght Boschker, CTO, Skinvision
Miguel Rojo Rossi, Solutions Architect Lead, AWS
SageMaker Algorithms Infinitely Scalable Machine LearningAmazon Web Services
Amazon SageMaker is a fully-managed service that enables developers and data scientists to quickly and easily build, train, and deploy machine learning (ML) models, at any scale. Amazon SageMaker provides high-performance, machine learning algorithms optimized for speed, scale, and accuracy, to perform training on petabyte-scale data sets. This webinar will introduce you to the collection of distributed streaming ML algorithms that come with Amazon SageMaker. You will learn about the difference between streaming and batch ML algorithms, and how SageMaker has been architected to run these algorithms at scale. We will demo Neural Topic Modeling of text documents using a sample SageMaker Notebook, which will be made available to attendees.
Level: 300-400
Speaker: Binoy Das - Partner Solutions Architect, AWS
Working with Amazon SageMaker Algorithms for Faster Model TrainingAmazon Web Services
Amazon SageMaker is a fully-managed service that enables developers and data scientists to quickly and easily build, train, and deploy machine learning (ML) models, at any scale. Amazon SageMaker provides high-performance, machine learning algorithms optimized for speed, scale, and accuracy, to perform training on petabyte-scale data sets. This webinar will introduce you to the collection of distributed streaming ML algorithms that come with Amazon SageMaker. You will learn about the difference between streaming and batch ML algorithms, and how SageMaker has been architected to run these algorithms at scale. We will demo Neural Topic Modeling of text documents using a sample SageMaker Notebook, which will be made available to attendees.
NEW LAUNCH! Deep dive on Amazon Neptune - DAT318 - re:Invent 2017Amazon Web Services
Amazon Neptune is a fully managed graph database service which has been built ground up for handling rich highly connected data. Graph databases have diverse use cases across multiple industries; examples include recommendation engines, knowledge graphs, fraud detection, social networks, network management and life sciences. Amazon Neptune is open and flexible with support for Apache TinkerPop and RDF/SPARQL standards. Under the hood Neptune uses the same foundational building blocks as Amazon Aurora which gives it high performance, availability and durability. In this session, we will do a deep dive into capabilities, performance and key innovations in Amazon Neptune.
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.
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.
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.
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 - 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.
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.
Slides from my talk at the IP Expo Nordic 2017:
https://www.ipexponordic.com/Speakers-2017/Adrian-Hornsby
Speed and agility are essential for today’s businesses. The quicker you can get from an idea to first results, the more you can experiment and innovate with your data, perform ad-hoc analysis, and drive answers to new business questions. During this talk, Adrian will take in key features of the AWS IoT platform, latest developments and live demos
AWS Batch: Simplifying batch computing in the cloudAdrian Hornsby
Docker enables you to create highly customized images that are used to execute your jobs. These images allow you to easily share complex applications between teams and even organizations. However, sometimes you might just need to run a script! This talk walk you through the steps to create and run a simple “fetch & run” job in AWS Batch. AWS Batch executes jobs as Docker containers using Amazon ECS. You build a simple Docker image containing a helper application that can download your script or even a zip file from Amazon S3. AWS Batch then launches an instance of your container image to retrieve your script and run your job.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
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.
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.
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
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
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.
In fact, it is worth noting that Thomas Edison did not invent the incandescent light bulb. Twenty three different light bulbs were developed before Edison's.
Though he didn’t come up with the whole concept, his light bulb was the first that proved practical and affordable for home illumination.
The trick had been choosing a filament that would be durable but inexpensive, and they tested more than 6,000 possible materials before finding one that fit the bill: carbonized bamboo.
But the strike of Genius of Edison wasn’t the lightbulb itself using carbonized bamboo, but instead it came with the lighting of the Pearl Street 2 years later.
Building on the work of other inventors in some cases and drawing on his own vision and ingenuity, Edison and his associates had to invent a long lasting lightbulbs yes, but they also had to have a reliable source of electric current, a system for distributing that current around the street, a mechanism for connecting lightbulbs to the grid and a meter to measure how much electricity each household was using.
The lightbulb in itself was more of a curiosity piece rather than a life changing technology.
In fact, the lightbulb was a product of networked innovations, all linked together to create the magic of electric light.
To build that, no genius was involved .. but like Edison, we stitched innovations together to create a Wow experience for our customers. (security, scalable, reliable..)
And this is exactly what the cloud is about!
Just few years ago, it would have taken months or even years to build a scalable, reliable and secure application like that from scratch.
Catalyst for innovations to flourish, because you can easy abstract complex problems into easy to use, secure and scalable units, that you can use to easily build complex applications
From storage, compute, databases, analytics tools and datawarehouses to Mobile applications, Devops tools, Containers, Serverless, IoT and AI just to name a few.
One of the most fascinating complex problem but also one that has the potential to shape the future of technology is AI…
When did it all start? Well, that was approx 60 years ago!
By the 2000s, 4 things made large-scale neural networks possible.
Algorithms: 1998 Convolutional Neural Networks, a new type of multi-layered networks ( “Deep Learning”) - dramatically reduces the computing cost of network training.
Large data sets became widely available. Text, pictures, movies, music: everything was suddenly digital and could be used to train neural networks.
Graphics Processing Units (GPUs) parallel processing power to train large neural networks.
Cloud computing brought elasticity and scalability to developers and researchers, allowing them to use as much infrastructure as needed for training… without having to build, run or pay for it long term.
AWS is an AI enabler .. For all the reason mentioned here –
At Amazon, we’ve been investing deeply in artificial intelligence for over 20 years, and many of the capabilities customers experience are driven by machine learning. We have thousands of engineers at Amazon committed to machine learning and deep learning, and it’s a big part of our heritage.
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.
So the 20 years and thousands of engineers we’ve had working on Amazon have helped drive operation efficiencies and better experiences for our customers.. And that experience and the tools we have developed, we offer them for you to build and innovate with.
Amazon Rekognition currently supports the JPEG and PNG image formats. You can submit images either as an S3 object or as a byte array.Amazon Rekognition supports image file sizes up to 15MB when passed as an S3 object, and up to 5MB when submitted as an image byte array.Amazon Rekognition is currently available in US East (Northern Virginia), US West (Oregon) and EU (Ireland) regions.
Mxnet convolutional deep neural networks (CNNs),
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Polly’s basics are pretty simple, but the service has deep functionality.
You can send the service a simple string of text, and it will generate the life like voice in your choice of 47 different voices.
But it’s not naive of the context of the text. For example, the text here - ‘WA’ and ‘degree F’, that would sound strange if it were spoken out loud.
Instead, Polly will automatically expand the text strings ‘WA’ and ‘degree F’, to ‘Washington’ and ‘degrees fahrenheit’, to create more life like speech. The developer doesn’t have to do anything - just send the text, and get life like voice back.
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.
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Spoken language crucial for language learning
Accurate pronunciation matters
Faster iteration thanks to TTS
As good as natural human speech
When teaching a foreign language, accurate pronunciation matters. If exposed to incorrect pronunciation, learners develop their listening and speaking skills poorly, which compromises their ability to communicate effectively. Duolingo uses text-to-speech (TTS) to provide high-quality language education. To some, this approach might seem counterintuitive: shouldn’t people learn by listening to a native speaker?
Find a company that records audio in the language: The company must find a voice actor who not only speaks the language, but also who speaks with good pronunciation and clarity.
Find someone to evaluate the quality of pronunciation: We need an independent party from the recording company to create a small sample of sentences, which this party uses to evaluate pronunciation quality of the recordings.
Record and evaluate the quality of the sample sentences.
Set up a contract with the recording company.
Record all sentences.
Evaluate recordings, providing a data quality assurance check. For example, we need to check if all files are in the proper format and correctly separated. This step is necessary because the industry standard is to record all sentences in a single session and separate them later.
On click – Marketplace
AMI supported and maintained by Amazon Web Services for use on EC2.
Designed to provide a stable, secure, and high performance execution environment for deep learning applications running on Amazon EC2.
Popular deep learning frameworks, including MXNet, Caffe, Tensorflow, Theano, CNTK and Torch
as Packages that enable easy integration with AWS, including launch configuration tools and many popular AWS libraries and tools.
It also includes the Anaconda Data Science Platform for Python2 and Python3.
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The AWS Deep Learning AMIs equip data scientists, machine learning practitioners, and research scientists with the infrastructure and tools to accelerate work in deep learning, in the cloud, at any scale. You can quickly launch Amazon EC2 instances on Amazon Linux or Ubuntu, pre-installed with popular deep learning frameworks to train sophisticated, custom AI models, experiment with new algorithms, or to learn new skills and techniques. 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, using Apache MXNet, TensorFlow, the Microsoft Cognitive Toolkit (CNTK), Caffe, Caffe2, Theano, Torch and Keras.
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Call out mxnet and AI at the edge.
We compiled and built the MXNet libraries to demonstrate how Lambda scales the prediction pipeline to provide this ease and flexibility for machine learning or deep learning model prediction. We built a sample application that predicts image labels using an 18-layer deep residual network. The model architecture is based on the winning model in the ImageNet competition called ResidualNet. The application produces state-of-the-art results for problems like image classification.
I often get asked about the future..
Democratization of technology –
When we started AWS – our goal was to put technology that fortune 500 companies have in the hands of every developers
Remember the lightbulb was a product of networked innovations, all linked together to create the magic of electric light. Well, When we started AWS – our goal was to put technology that fortune 500 companies have in the hands of every developers – today you have access to technology that was still science fiction few years ago – technology that you can stitch together to create that woo experience for your customers - and we are not going to stop here.
The truth is, the future is always in the making. It’s engineers and wild thinkers that are helping to create the world of tomorrow.
It’s customers like tusimple, revolutionizing the commerical trucking industry, arterys saving lives through 4d medical imaging, duolingo teaching language around the world.. It’s customers like you that are helping to build this.
Together, we can tackle some of the worlds most challenging problems, and together we can continue to build a smater future.