The document discusses machine learning options on AWS for noobs, geeks, and gurus. It provides an overview of various AWS machine learning services like Rekognition, Comprehend, Lex, and SageMaker and explains when each type of user would likely use them. For noobs, it recommends fully managed services with no assembly required. For geeks, it suggests services requiring some configuration. For gurus, it discusses options for building custom models like using Deep Learning AMIs or deep learning VMs. It concludes by noting there are multiple ways to approach machine learning depending on one's needs and abilities.
This presentation includes an intro to AI/ML, a summary of AI/ML services on AWS, and a discussion of the evolution of a Twitter bot that uses ML to classify aircraft found in images.
AWS Bay Area Meetup The Evolution of AircraftMLjerryhargrove
AircraftML is a Twitter-bot that uses AWS deep-learning services and features to identify and classify images of aircraft. Using a model built using Amazon SageMaker and trained with tens of thousands of labeled aircraft images, this serverless system is able to accurately classify many modern commercial aircraft. This talk will focus on how the architecture of AircraftML has evolved over time, where it started and where it is today, and what the driving factors for those changes were, including technology and cost.
Adoptar o implementar nuevas tecnologías a nuestros sistemas basados en Java tiene muchos ángulos de análisis que van más allá de lo técnico.
En esta sesion veremos 10 formas prácticas y efectivas en las que puedes contribuir activamente en el ecosistema Cloud Native de Java con JakartaEE y Apache TomEE.
Con esta información podrás tomar mejores decisiones en las mejoras continuas de tus arquitecturas y sistemas basados en java.
stackconf 2021 | Why you should take care of infrastructure driftNETWAYS
As infrastructure as code (IaC) becomes widely adopted by users with heterogenous skillsets, and as IaC codebases become larger and larger, it becomes harder to track drift. Drift is a deviation between the actual infrastructure state and the IaC codebase. It causes issues for security posture management, collaborative work, and maintenance. There are a lot of juicy stories from the trenches to be told on infrastructure drift. Sure enough, we all do GitOps by the book! Or we all have the right processes in place. But we also have to interact with other teams. We also have to grant some level of access to our infrastructures to some services or tools that may eventually generate uncontrolled changes. You can’t efficiently improve what you don’t track. We track coverage for unit tests, why not infrastructure as code coverage? How can we make sure our infrastructure code matches our actual infrastructure state? In this talk, using Terraform with AWS resources, I will show how infrastructure drift can go undetected despite our best efforts or tooling and cause issues and end the talk by showing our own free and open source tool driftctl, (just released under Apache-2.0 licence) that tracks IaC coverage and warns of infrastructure drift.
This presentation includes an intro to AI/ML, a summary of AI/ML services on AWS, and a discussion of the evolution of a Twitter bot that uses ML to classify aircraft found in images.
AWS Bay Area Meetup The Evolution of AircraftMLjerryhargrove
AircraftML is a Twitter-bot that uses AWS deep-learning services and features to identify and classify images of aircraft. Using a model built using Amazon SageMaker and trained with tens of thousands of labeled aircraft images, this serverless system is able to accurately classify many modern commercial aircraft. This talk will focus on how the architecture of AircraftML has evolved over time, where it started and where it is today, and what the driving factors for those changes were, including technology and cost.
Adoptar o implementar nuevas tecnologías a nuestros sistemas basados en Java tiene muchos ángulos de análisis que van más allá de lo técnico.
En esta sesion veremos 10 formas prácticas y efectivas en las que puedes contribuir activamente en el ecosistema Cloud Native de Java con JakartaEE y Apache TomEE.
Con esta información podrás tomar mejores decisiones en las mejoras continuas de tus arquitecturas y sistemas basados en java.
stackconf 2021 | Why you should take care of infrastructure driftNETWAYS
As infrastructure as code (IaC) becomes widely adopted by users with heterogenous skillsets, and as IaC codebases become larger and larger, it becomes harder to track drift. Drift is a deviation between the actual infrastructure state and the IaC codebase. It causes issues for security posture management, collaborative work, and maintenance. There are a lot of juicy stories from the trenches to be told on infrastructure drift. Sure enough, we all do GitOps by the book! Or we all have the right processes in place. But we also have to interact with other teams. We also have to grant some level of access to our infrastructures to some services or tools that may eventually generate uncontrolled changes. You can’t efficiently improve what you don’t track. We track coverage for unit tests, why not infrastructure as code coverage? How can we make sure our infrastructure code matches our actual infrastructure state? In this talk, using Terraform with AWS resources, I will show how infrastructure drift can go undetected despite our best efforts or tooling and cause issues and end the talk by showing our own free and open source tool driftctl, (just released under Apache-2.0 licence) that tracks IaC coverage and warns of infrastructure drift.
아마존닷컴은 쇼핑 상품 추천, 배송 및 물류 예측 등에 기계 학습 기술을 활용해 왔으며, 최근 프라임 서비스를 위한 음악, 이미지, 영상 인식, 무인 매장인 아마존고 및 음성 비서 서비스인 알렉사에 딥러닝 기술을 활용하고 있다. 본 세션에서는 이러한 주요 딥러닝 활용 기술 사례를 알아보고, AWS 클라우드를 통해 제공하는 이미지/영상 인식, 음성 인식 및 합성, 기계 번역, 자연어 처리 등 다양한 딥러닝 기반 서비스 구현 방법을 살펴본다. 개발자들이 직접 딥러닝 기반 데이터 처리, 모델 학습 및 서비스 배포까지 손쉽게 구성할 수 있는 Amazon SageMaker와 Deep Lens를 통해 어떻게 IoT 기반 서비스로 활용할 수 있는지 시연을 통해 알아본다.
발표자: 윤석찬(아마존 테크 에반젤리스트)
발표일: 2018.2.
아마존닷컴은 쇼핑 상품 추천, 배송 및 물류 예측 등에 기계 학습 기술을 활용해 왔으며, 최근 프라임 서비스를 위한 음악, 이미지, 영상 인식, 무인 매장인 아마존고 및 음성 비서 서비스인 알렉사에 딥러닝 기술을 활용하고 있다. 본 세션에서는 이러한 주요 딥러닝 활용 기술 사례를 알아보고, AWS 클라우드를 통해 제공하는 이미지/영상 인식, 음성 인식 및 합성, 기계 번역, 자연어 처리 등 다양한 딥러닝 기반 서비스 구현 방법을 살펴본다. 개발자들이 직접 딥러닝 기반 데이터 처리, 모델 학습 및 서비스 배포까지 손쉽게 구성할 수 있는 Amazon SageMaker와 Deep Lens를 통해 어떻게 IoT 기반 서비스로 활용할 수 있는지 시연을 통해 알아본다.
the lecture is about introduction of Interaction design and related learning resourses by alite. The lecture took place in China Academy of Art on Mar.25,2011.
A look at how HTML5 aims to plug the holes that Flash has been filling in browsers for the last decade, looking at both HTML5 and non-HTML5 JavaScript APIs.
For Flash Brighton in Feb 2010.
Chaos Engineering is the discipline of experimenting on a distributed system in order to build confidence in the system’s capability to withstand turbulent conditions in production.
Machine learning and Data Science are cool, and really useful, but also they can be quite overwhelming when you are getting started, with complicated jargon and lots and lots of statistics and mathematical background. But, can we apply on it the long lasting Developer rule of “Hello world Now, Theory later”? In this talk I will share 5 tips from my journey becoming a Data Hacker, How the maker-mentality helped me become a better data-scientist, And how you can use your engineering strengths to become jack-of-all-trades Data Hacker.
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The boom of AI brought to the market a set of impressive solutions both on the hardware and software side. On the other hand, massive implementation of AI in various areas brings about problems, and security is one of the greatest concerns.
In this talk we will present results of hands-on vulnerability research of different components of AI infrastructure including NVIDIA DGX GPU servers, ML frameworks such as Pytorch, Keras and Tensorflow, data processing pipelines and specific applications, including Medical Imaging and face recognition powered CCTV. Updated Internet Census toolkit based on the Grinder framework will be introduced.
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.
Why Agile Works But Isn't Working For YouDavid Harvey
Presentation for Causerie at ADMB Bruges, Thursday 12 February 2009.
Audio will be added once I have it - the pictures are nice, but I'm not sure they make that much sense without the narrative (or do they? What do you think?)
(NB first slide is not a mistake - deliberately empty)
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.
Stefan Judis "Did we(b development) lose the right direction?"Fwdays
Keeping up with the state of web technology is one of the biggest challenges for us developers today. We invent new tools; we define new best practices, everything’s new, always... And we do all that for good user experience! We do all that to build the best possible web – it’s all about our users.
But is it, really? Or do developers like to play with technology secretly loving the new and shiny? Or do we only pretend that it’s about users, and behind closed doors, it’s developer experience that matters to us? Did we lose direction? Is it time for a critical look at the state of the web and the role JavaScript plays in it?
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아마존닷컴은 쇼핑 상품 추천, 배송 및 물류 예측 등에 기계 학습 기술을 활용해 왔으며, 최근 프라임 서비스를 위한 음악, 이미지, 영상 인식, 무인 매장인 아마존고 및 음성 비서 서비스인 알렉사에 딥러닝 기술을 활용하고 있다. 본 세션에서는 이러한 주요 딥러닝 활용 기술 사례를 알아보고, AWS 클라우드를 통해 제공하는 이미지/영상 인식, 음성 인식 및 합성, 기계 번역, 자연어 처리 등 다양한 딥러닝 기반 서비스 구현 방법을 살펴본다. 개발자들이 직접 딥러닝 기반 데이터 처리, 모델 학습 및 서비스 배포까지 손쉽게 구성할 수 있는 Amazon SageMaker와 Deep Lens를 통해 어떻게 IoT 기반 서비스로 활용할 수 있는지 시연을 통해 알아본다.
발표자: 윤석찬(아마존 테크 에반젤리스트)
발표일: 2018.2.
아마존닷컴은 쇼핑 상품 추천, 배송 및 물류 예측 등에 기계 학습 기술을 활용해 왔으며, 최근 프라임 서비스를 위한 음악, 이미지, 영상 인식, 무인 매장인 아마존고 및 음성 비서 서비스인 알렉사에 딥러닝 기술을 활용하고 있다. 본 세션에서는 이러한 주요 딥러닝 활용 기술 사례를 알아보고, AWS 클라우드를 통해 제공하는 이미지/영상 인식, 음성 인식 및 합성, 기계 번역, 자연어 처리 등 다양한 딥러닝 기반 서비스 구현 방법을 살펴본다. 개발자들이 직접 딥러닝 기반 데이터 처리, 모델 학습 및 서비스 배포까지 손쉽게 구성할 수 있는 Amazon SageMaker와 Deep Lens를 통해 어떻게 IoT 기반 서비스로 활용할 수 있는지 시연을 통해 알아본다.
the lecture is about introduction of Interaction design and related learning resourses by alite. The lecture took place in China Academy of Art on Mar.25,2011.
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For Flash Brighton in Feb 2010.
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Machine learning and Data Science are cool, and really useful, but also they can be quite overwhelming when you are getting started, with complicated jargon and lots and lots of statistics and mathematical background. But, can we apply on it the long lasting Developer rule of “Hello world Now, Theory later”? In this talk I will share 5 tips from my journey becoming a Data Hacker, How the maker-mentality helped me become a better data-scientist, And how you can use your engineering strengths to become jack-of-all-trades Data Hacker.
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The boom of AI brought to the market a set of impressive solutions both on the hardware and software side. On the other hand, massive implementation of AI in various areas brings about problems, and security is one of the greatest concerns.
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Audio will be added once I have it - the pictures are nice, but I'm not sure they make that much sense without the narrative (or do they? What do you think?)
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