AWS Rekognition: Rich Image Metadata Extraction Powered by Deep Learning
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Amazon Rekognition is an AI service that uses deep learning to extract rich metadata from images. It provides capabilities for object and scene detection, facial analysis including face detection and comparison, celebrity recognition, and image moderation to detect explicit or suggestive content. It also offers APIs and SDKs to build applications and reference architectures using these image analysis features. Pricing is based on the number of images processed and faces stored. Customers across media, retail, and public safety use Rekognition for applications like digital asset management, law enforcement, and ecommerce.
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AWS Rekognition - AWS Summit Tel Aviv 2017
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.
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AWS STARTUP DAY 2018 I Enhancing Your Startup With Amazon Machine Learning
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.
An Overview of AI on the AWS Platform - February 2017 Online Tech Talks
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
AI - State of the Union
The document outlines an agenda for a day-long event on AI and machine learning. It begins with an introductory session on the state of AI from 10:00-11:00 am. This is followed by a break and then deeper sessions on Amazon Sagemaker, Forecast, and Personalize. Lunch is from 12:30-1:30 pm. The afternoon includes sessions on machine learning production with Sagemaker and fraud detection with Sagemaker. There are additional breaks throughout the day and the event concludes with a session on reinforcement learning from 3:45-4:45 pm.
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BDA 301 An Introduction to Amazon Rekognition, for Deep Learning-based Comput...
BDA 301 An Introduction to Amazon Rekognition, for Deep Learning-based Comput...
Best Practices for Integrating Amazon Rekognition into Your Own Applications
Best Practices for Integrating Amazon Rekognition into Your Own Applications
Build Computer Vision Applications with Amazon Rekognition
Build Computer Vision Applications with Amazon Rekognition
Best practices for integrating Amazon Rekognition into your own application
Best practices for integrating Amazon Rekognition into your own application
Use Amazon Rekognition to Build a Facial Recognition System
Use Amazon Rekognition to Build a Facial Recognition System
AI & Deep Learning At Amazon - April 2017 AWS Online Tech Talks
AI & Deep Learning At Amazon - April 2017 AWS Online Tech Talks
Taking Complexity Out of Data Science with AWS and Zoomdata PPT
Taking Complexity Out of Data Science with AWS and Zoomdata PPT
Build Computer Vision Applications with Amazon Rekognition
Build Computer Vision Applications with Amazon Rekognition
Best Practices for Integrating Amazon Rekognition into Your Own Applications ...
Best Practices for Integrating Amazon Rekognition into Your Own Applications ...
Introduction to Artificial Intelligence (AI) at Amazon
Introduction to Artificial Intelligence (AI) at Amazon
AWS AI state of the union - AWS Cape Town Summit 2018
AWS AI state of the union - AWS Cape Town Summit 2018
AWS re:Invent 2016: Machine Learning State of the Union Mini Con (MAC206)
AWS re:Invent 2016: Machine Learning State of the Union Mini Con (MAC206)
AWS STARTUP DAY 2018 I Enhancing Your Startup With Amazon Machine Learning
AWS STARTUP DAY 2018 I Enhancing Your Startup With Amazon Machine Learning
An Overview of AI on the AWS Platform - February 2017 Online Tech Talks
An Overview of AI on the AWS Platform - February 2017 Online Tech Talks
Similar to AWS Rekognition: Rich Image Metadata Extraction Powered by Deep Learning
AWS Rekognition: Rich Image Metadata Extraction Powered by Deep Learning
AWS Rekognition is a deep learning service that allows customers to extract rich metadata like objects, scenes, faces, and moderation labels from images. It uses convolutional neural networks trained on large datasets to perform tasks like detecting 80+ object labels, facial analysis, comparisons, and celebrity recognition. Rekognition also offers image moderation to detect unsafe or unwanted content.
Globant - Amazon recognition workshop - 2018
Amazon Rekognition is an AWS deep learning-based image and video analysis service that can detect objects, scenes and faces; analyze facial expressions; and compare faces. It allows users to search and filter image libraries, verify identities, recognize celebrities and moderate inappropriate images. The document provides examples of how Rekognition can be used for applications like sentiment analysis in retail, targeted online advertising, and automating footage tagging and quickly identifying persons of interest for law enforcement. It also describes the Rekognition APIs and how they can be used together in advanced solutions through architectures involving AWS services like S3, Lambda, DynamoDB and more.
Amazon Rekognition Best Practices - DevDay Austin 2017
1) The document discusses best practices for using Amazon Rekognition, an AI service that analyzes images and video. It provides examples of how Rekognition can be used for tasks like facial recognition, object detection, and sentiment analysis.
2) Advanced techniques are described like building processing pipelines using Lambda and Step Functions to perform multiple Rekognition operations in sequence. Example use cases include detecting when persons of interest are near celebrities.
3) The document emphasizes optimizing input data and requests for performance, and following security best practices like access control and encryption when using Rekognition.
Build Computer Vision Applications with Amazon Rekognition: Machine Learning ...
Machine Learning Week at the San Francisco Loft: Build Computer Vision Applications with Amazon Rekognition
Build Computer Vision Applications 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
Speaker: Niranjan Hira - Solutions Architect, Amazon Lex
Build Computer Vision Applications with Amazon Rekognition
Amazon Rekognition is a deep learning-based image and video analysis service that enables developers to integrate easy-to-use APIs into their applications. This session will walk through the Amazon Rekognition features, including object and scene detection, text-in-image extraction, celebrity recognition, content moderation, and more. Developers can quickly get started with this fully-managed service and start building computer vision applications such as a searchable media library, automated content moderation, an image-based alert system, and more.
Level: 200-300
Speaker: Binoy Das - Partner Solutions Architect, AWS
An Introduction to the AI services at AWS - AWS Summit Tel Aviv 2017
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.
Adding image and video analysis to your app
Prakash Palanisamy presented on adding image and video analysis capabilities to applications using Amazon Rekognition. Rekognition provides deep learning-based computer vision APIs for facial analysis, object detection, celebrity recognition and other features. Use cases discussed included building searchable image libraries, facial recognition for verification, and analyzing video streams. The presentation provided an overview of Rekognition's capabilities and APIs as well as code examples and customer references.
Best practices for integrating amazon rekognition into your own applications
1. The document discusses Amazon Rekognition, an AWS deep learning service for image and video analysis. It describes Rekognition's capabilities for object detection, facial analysis, face comparison, and other tasks.
2. Best practices are provided for integrating Rekognition into applications through SDKs and APIs, and extending it using other AWS services and third-party software.
3. Advanced techniques are demonstrated like building processing pipelines to detect persons of interest near celebrities in images.
AI as a Service: the future has never been so simple with cloud
Do you remember the futuristic movie called Minority Report, taken from the famous novel by Philip K. Dick, where a system automatically recognizes customers entering the store? The future is now, thanks to the AI services offered by Amazon. In this talk we will show you how easy it is to create a person's automatic recognition system by simply invoking services on the cloud and using cheap device like a RaspberryPi. We will show how to set up a complete serverless architecture with AWS Lambda to execute your code and interact with AWS services like S3, Rekognition and Polly. We will show to you how is simple to integrate the AWS world with a RaspberryPi (or any other IoT devie) through the AWS API Gateway service. We will show you all these thing with a real demo with real device.. because the future is now!
For this demo we are used a simple Raspberry Pi 3 Model B with the Camera borad V2 and Bright PI board v1, on which we have installed the linux distro Raspbian. With Python and OpenCV framework we are written a simple script that uses camera to detect faces, take the frames and send these to the Cloud. An API Rest, realized with AWS Api Gateway, is the entry point for the AWS world. After that an AWS Lambda function takes in charge the request and implements all the logic to save the photo on a S3 bucket and call AWS Rekognition to analyse and detect the face. The function try to associates metadata saved on a DynamoDB table with the data returned from AWS Rekognition service. With this data the Lambda creates a simple phrase with name and surname of the recognized person and a special charateristic revealed on the photo. Finally a call to AWS Polly, a text-to-speech service, is made to get the mp3 from the text. Then the audio file is returned to the API Rest call that was made by the RaspberryPi. Finally the device closes the loop with the play of this file.
Build Computer Vision Applications with Amazon Rekognition and SageMaker
Agenda
• Amazon Rekognition
• Amazon Rekognition Custom Labels
• Amazon SageMaker Ground Truth
• Amazon SageMaker Studio Notebooks, Training Job, Endpoints
Building and Operating AI Services at Scale by Randall Hunt, Amazon Web Services
Randall Hunt, Senior Technical Evangelist at Amazon Web Services talks about Building and Operating AI Services at Scale at TriNimbus' 2017 Canadian Executive Cloud & DevOps summit in Vancouver
Deep Learning-based Image Recognition: Intro to Amazon Rekognition
by Mikhail Prudnikov, Solutions Architect, AWS
This session will introduce you to Amazon Rekognition, 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. To get started with Rekognition, simply log in to the Rekognition console to try the service with sample photos or your own photos. Join this session and learn more about Amazon Rekognition!
BDA301 An Introduction to Amazon Rekognition
This session will introduce you to Amazon Rekognition, a service that makes it easy to add deep learning image analysis to your applications. Amazon Rekognition is based on the same proven, highly scalable, deep learning technology developed by Amazon’s
AWS AI Media & Entertainment Seminar - NYC, August 15, 2017
Presentations from the AWS Media & Entertainment Seminar on Artificial Intelligence in NYC on August 15, 2017. Attendees spent the afternoon with AWS and a few of our Media and Entertainment customers exploring how M&E organizations can derive higher productivity and new business insight using AI services, platforms, tools and infrastructure on the AWS Cloud.
Introduction to Artificial Intelligence on AWS
by Dario Rivera, 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.
Moving forward with AI
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_HK_StartupDay_Building Interactive websites while automating for efficien...
This document discusses Amazon's machine learning services for building conversational interfaces and extracting insights from unstructured text and audio. It describes Amazon Lex for creating chatbots, Amazon Comprehend for natural language processing tasks like entity extraction and sentiment analysis, and how they can be used together for applications like intelligent call centers and content analysis. Pre-trained APIs simplify adding machine learning to apps without requiring ML expertise.
BDA304 Build Deep Learning Applications with TensorFlow and Amazon SageMaker
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, you learn how to get started with the TensorFlow deep learning framework using Amazon SageMaker, a platform to easily build, train, and use to deploy models at scale. You learn how to build a model using TensorFlow by setting up a Jupyter Notebook to get started with image and object recognition. You also learn how to quickly train and deploy a model through Amazon SageMaker.
Amazon AI (October 2017)
This document discusses Amazon's artificial intelligence services including Polly, Rekognition, and Lex. Polly provides text-to-speech conversion in multiple languages. Rekognition allows image analysis including object detection, facial detection and analysis. Lex builds conversational interactions through voice and text using natural language understanding. The document demonstrates how these services work through examples and emphasizes their ease of use, quality, functionality and integration capabilities. It positions Amazon Web Services as the center of gravity for artificial intelligence.
Deep dive on amazon rekognition architectures for image analysis - MCL318 - ...
Join us for a deep dive on how to use Amazon Rekognition for real world image analysis. Learn how to integrate Amazon Rekognition with other AWS services to make your image libraries searchable. Also learn how to verify user identities by comparing their live image with a reference image, and estimate the satisfaction and sentiment of your customers. We also share best practices around fine-tuning and optimizing your Amazon Rekognition usage and refer to AWS CloudFormation templates.
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Amazon Rekognition Best Practices - DevDay Austin 2017
Amazon Rekognition Best Practices - DevDay Austin 2017
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Build Computer Vision Applications with Amazon Rekognition: Machine Learning ...
Build Computer Vision Applications with Amazon Rekognition
Build Computer Vision Applications with Amazon Rekognition
An Introduction to the AI services at AWS - AWS Summit Tel Aviv 2017
An Introduction to the AI services at AWS - AWS Summit Tel Aviv 2017
Best practices for integrating amazon rekognition into your own applications
Best practices for integrating amazon rekognition into your own applications
AI as a Service: the future has never been so simple with cloud
AI as a Service: the future has never been so simple with cloud
Build Computer Vision Applications with Amazon Rekognition and SageMaker
Build Computer Vision Applications with Amazon Rekognition and SageMaker
Building and Operating AI Services at Scale by Randall Hunt, Amazon Web Services
Building and Operating AI Services at Scale by Randall Hunt, Amazon Web Services
Deep Learning-based Image Recognition: Intro to Amazon Rekognition
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AWS AI Media & Entertainment Seminar - NYC, August 15, 2017
AWS AI Media & Entertainment Seminar - NYC, August 15, 2017
AWS_HK_StartupDay_Building Interactive websites while automating for efficien...
AWS_HK_StartupDay_Building Interactive websites while automating for efficien...
BDA304 Build Deep Learning Applications with TensorFlow and Amazon SageMaker
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More from Adrian Hornsby
How can your business benefit from going serverless?
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.
Can Automotive be as agile as Unicorns?
The slides from my talk at the Vehicle Electronics & Connected Services
http://insightevents.se/vehicle-electronics-connected-services/
Moving Forward with AI - as presented at the Prosessipäivät 2018
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.
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.
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.
Model Serving for Deep Learning
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
AI in Finance: Moving forward!
The slides from my talk about AI in Finance that I gave to one of our customer, a large bank in the Nordics.
Building a Multi-Region, Active-Active Serverless Backends.
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.
Moving Forward with AI
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.
AI: State of the Union
Slides from my talk at the AWS DevDays in the Nordics
https://aws.amazon.com/events/Devdays-Nordics/
Review of AI @ AWS as of early 2018
Serverless Architectural Patterns
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...
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 Scale
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.
Innovations and the Cloud
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.
Serverless in Action on AWS
The slides from my talk on Serverless - more geared towards developers and architects, or innovators. Well actually to anyone interested really :)
Innovations and The Cloud
The document discusses innovations in artificial intelligence and cloud computing. It describes how AI has advanced from early neural networks in the 1950s to today's deep learning algorithms powered by large datasets, GPUs, cloud computing, and AWS services. The document outlines several AWS AI services including Amazon Rekognition for image analysis, Amazon Polly for text-to-speech, and Amazon Lex for conversational interfaces. It emphasizes that AWS aims to put AI in the hands of every developer.
Devoxx: Building AI-powered applications on AWS
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
10 Lessons from 10 Years of 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 AI
The slides from my talk at the Serverless Summit in India http://inserverless.com
Developing advanced AI enabled applications with serverless technology on AWS
AWS Startup Day Bangalore: Being Well-Architected in the Cloud
The document discusses the AWS Well-Architected Framework which provides guidance to help build secure, high-performing, resilient, and efficient infrastructure for applications. It covers the five pillars of the framework - security, reliability, performance efficiency, cost optimization, and operational excellence. For each pillar, it discusses design principles, best practices, services, and examples to evaluate architectures against AWS recommendations.
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How can your business benefit from going serverless?
How can your business benefit from going serverless?
Moving Forward with AI - as presented at the Prosessipäivät 2018
Moving Forward with AI - as presented at the Prosessipäivät 2018
Chaos Engineering: Why Breaking Things Should Be Practised.
Chaos Engineering: Why Breaking Things Should Be Practised.
Chaos Engineering: Why Breaking Things Should Be Practised.
Chaos Engineering: Why Breaking Things Should Be Practised.
Building a Multi-Region, Active-Active Serverless Backends.
Building a Multi-Region, Active-Active Serverless Backends.
re:Invent re:Cap - An overview of Artificial Intelligence and Machine Learnin...
re:Invent re:Cap - An overview of Artificial Intelligence and Machine Learnin...
Developing Sophisticated Serverless Applications with AI
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Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
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Encryption in Microsoft 365 - ExpertsLive Netherlands 2024
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Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdf
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While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
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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.
A tale of scale & speed: How the US Navy is enabling software delivery from l...
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
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GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024
Neha Bajwa, Vice President of Product Marketing, Neo4j
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Artificial Intelligence for XMLDevelopment
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
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Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
UiPath Test Automation using UiPath Test Suite series, part 6
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Video Streaming: Then, Now, and in the Future
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
National Security Agency - NSA mobile device best practices
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AI
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AIVladimir Iglovikov, Ph.D.
Presented by Vladimir Iglovikov:
- https://www.linkedin.com/in/iglovikov/
- https://x.com/viglovikov
- https://www.instagram.com/ternaus/
This presentation delves into the journey of Albumentations.ai, a highly successful open-source library for data augmentation.
Created out of a necessity for superior performance in Kaggle competitions, Albumentations has grown to become a widely used tool among data scientists and machine learning practitioners.
This case study covers various aspects, including:
People: The contributors and community that have supported Albumentations.
Metrics: The success indicators such as downloads, daily active users, GitHub stars, and financial contributions.
Challenges: The hurdles in monetizing open-source projects and measuring user engagement.
Development Practices: Best practices for creating, maintaining, and scaling open-source libraries, including code hygiene, CI/CD, and fast iteration.
Community Building: Strategies for making adoption easy, iterating quickly, and fostering a vibrant, engaged community.
Marketing: Both online and offline marketing tactics, focusing on real, impactful interactions and collaborations.
Mental Health: Maintaining balance and not feeling pressured by user demands.
Key insights include the importance of automation, making the adoption process seamless, and leveraging offline interactions for marketing. The presentation also emphasizes the need for continuous small improvements and building a friendly, inclusive community that contributes to the project's growth.
Vladimir Iglovikov brings his extensive experience as a Kaggle Grandmaster, ex-Staff ML Engineer at Lyft, sharing valuable lessons and practical advice for anyone looking to enhance the adoption of their open-source projects.
Explore more about Albumentations and join the community at:
GitHub: https://github.com/albumentations-team/albumentations
Website: https://albumentations.ai/
LinkedIn: https://www.linkedin.com/company/100504475
Twitter: https://x.com/albumentationsGraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
FIDO Alliance Osaka Seminar: The WebAuthn API and Discoverable Credentials.pdf
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zkStudyClub - Reef: Fast Succinct Non-Interactive Zero-Knowledge Regex Proofs
This paper presents Reef, a system for generating publicly verifiable succinct non-interactive zero-knowledge proofs that a committed document matches or does not match a regular expression. We describe applications such as proving the strength of passwords, the provenance of email despite redactions, the validity of oblivious DNS queries, and the existence of mutations in DNA. Reef supports the Perl Compatible Regular Expression syntax, including wildcards, alternation, ranges, capture groups, Kleene star, negations, and lookarounds. Reef introduces a new type of automata, Skipping Alternating Finite Automata (SAFA), that skips irrelevant parts of a document when producing proofs without undermining soundness, and instantiates SAFA with a lookup argument. Our experimental evaluation confirms that Reef can generate proofs for documents with 32M characters; the proofs are small and cheap to verify (under a second).
Paper: https://eprint.iacr.org/2023/1886
Pushing the limits of ePRTC: 100ns holdover for 100 days
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
Removing Uninteresting Bytes in Software Fuzzing
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
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AWS Rekognition: Rich Image Metadata Extraction Powered by Deep Learning
- 1. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. AWS Rekognition Rich Image Metadata Extraction Powered by Deep Learning Adrian Hornsby, Technical Evangelist @adhorn adhorn@amazon.com
- 6. Deep Learning Process Conv 1 Conv 2 Conv n … … Feature Maps Labrador Dog Beach Outdoors Softmax Probability Fully Connected Layer
- 8. Amazon Rekognition Object and scene detection Facial analysis Face comparison Celebrity recognition Image moderation Amazon Rekognition: Images In, Rich Metadata Out
- 9. Object & Scene Detection
- 10. Object & Scene Detection
- 11. Facial Analysis
- 12. Facial Analysis
- 17. 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
- 18. Image moderation
- 19. Amazon Rekognition Amazon Rekognition: supported actions CompareFaces CreateCollection DeleteCollection DeleteFaces DetectFaces DetectLabels DetectModerationLabels GetCelebrityInfo IndexFaces ListCollections ListFaces RecognizeCelebrities SearchFaces SearchFacesByImage
- 20. Collection IndexFaces SearchFacesbyImage Nearest neighbor search FaceID: 4c55926e-69b3-5c80-8c9b-78ea01d30690 Similarity: 97 FaceID: 02e56305-1579-5b39-ba57-9afb0fd8782d Similarity: 92 FaceID: 02e56305-1579-5b39-ba57-9afb0fd8782d Similarity: 85
- 21. Amazon Rekognition Pricing Free Tier: 5000 images processed per month for first 12 months Image Analysis Tiers Price per 1000 images processed First 1 million images processed* per month $1.00 Next 9 million images processed* per month $0.80 Next 90 million images processed* per month $0.60 Over 100 million images processed* per month $0.40 Face Metadata Storage Price per 1,000 face metadata stored per month Face metadata stored $0.01
- 22. <demo> Rekognition to the test </demo>
- 23. Sheepdog or mop
- 25. Puppy or bagel?
- 28. Reference Architecture Searchable Image Library
- 30. Reference Architecture Face-Based User Verification
- 33. Reference Architecture Facial Recognition – Batch mode
- 34. Using Amazon Rekognition to Identify Persons of Interest for Law Enforcement https://aws.amazon.com/blogs/ai/using-amazon-rekognition-to-identify-persons-of-interest-for-law-enforcement/ Washington County Sheriff’s Office. “Amazon Rekognition has become a powerful tool for identifying suspects for my agency. As the service improves, I hope to make it the standard for facial recognition in law enforcement.” Chris Adzima Senior Information Systems Analyst, Washington County Sheriff’s Office.
- 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
- 39. <demo> Poliko powered by Amazon Polly & Rekognition </demo> http://poliko.adhorn.me
- 40. Demo - Poliko Poliko Take Pic Amazon Cognito 2. Detect Labels 4. Synthesize-speech Amazon Rekognition Amazon Polly 3. Detect Faces Amazon S3 “Static website hosting” enabled
- 41. <demo> Image Recognition and Processing Backend Step Functions </demo> https://github.com/awslabs/lambda-refarch-imagerecognition
- 44. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Thank you! @adhorn adhorn@amazon.com