Build a Visual Search Engine Using Amazon SageMaker and AWS Fargate (AIM341) ...Amazon Web Services
Visual search engines have a growing importance at companies like Pinterest as well as at e-commerce companies like Amazon.com and Gilt. In this chalk talk, we show you how to build a visual search engine using Amazon SageMaker and AWS Fargate.
Deep Dive on Amazon S3 Storage Classes: Creating Cost Efficiencies across You...Amazon Web Services
"Amazon S3 supports a range of storage classes that can help you cost-effectively store data without impacting performance or availability. Each of our storage classes offer different data-access levels, retrieval times, and costs to support various use cases. In this session, Amazon S3 experts dive deep into the different Amazon S3 storage classes, their respective attributes, and when you should use them.
"
Build a Visual Search Engine Using Amazon SageMaker and AWS Fargate (AIM341) ...Amazon Web Services
Visual search engines have a growing importance at companies like Pinterest as well as at e-commerce companies like Amazon.com and Gilt. In this chalk talk, we show you how to build a visual search engine using Amazon SageMaker and AWS Fargate.
Deep Dive on Amazon S3 Storage Classes: Creating Cost Efficiencies across You...Amazon Web Services
"Amazon S3 supports a range of storage classes that can help you cost-effectively store data without impacting performance or availability. Each of our storage classes offer different data-access levels, retrieval times, and costs to support various use cases. In this session, Amazon S3 experts dive deep into the different Amazon S3 storage classes, their respective attributes, and when you should use them.
"
Market Prediction Using ML: Experiment with Amazon SageMaker and the Deutsche...Amazon Web Services
In this workshop, learn how to use machine learning to analyze the Deutsche Börse Public Dataset, which consists of trade data aggregated to one-minute intervals from the Tradex and Eurex engines, comprising a variety of equities, funds, and derivative securities. The public dataset provides initial price, lowest price, highest price, final price, and volume for every minute of the trading day, and for every tradeable security. Learn how to apply a variety of ML models to the data to find patterns and methods to predict price movements or identify trends in the market. Also learn how to interact with data staged for analysis in Amazon S3, use AWS Glue to transform data into analysis-ready formats, and use Amazon SageMaker and Amazon EC2 to experiment with a variety of ML models to derive insights from the data.
Supercharge Any Alexa Skill by Understanding What Games Do (ALX403-R2) - AWS ...Amazon Web Services
Games push the boundaries of tech, and we can learn from them for a wide range of use cases. In this session, we look at what makes an Alexa quiz game, interactive fiction, and companion games work. Learn how to build engaging experiences with dialog management and entity resolution (Alexa NLU), session persistence (Amazon DynamoDB), production value (SSML, Amazon S3), as well as state handling, multi-modal displays, interceptors (ASK SDK), and cross-device interactions (Gadgets, AWS IoT for PC games).
AWS Cloud9과 Workspace만으로 PC없는 개발환경 활용기
클라우드 기반의 IDE와 가상 데스크톱 서비스를 이용하면 언제 어디서나 개발 환경을 구축할 수 있습니다. 애플리케이션 서비스를 위해 서버리스(Serverless)를 이용할 수 있는 것 처럼 개발 환경도 컴퓨터리스(Computerless)로 가능합니다. AWS Cloud9과 Workspace만으로 PC없이 웹 서비스 및 안드로이드 앱 개발을 진행하는 방법을 소개합니다.
Amazon.com 의 개인화 추천 / 예측 기능을 우리도 써 봅시다. :: 심호진 - AWS Community Day 2019AWSKRUG - AWS한국사용자모임
Amazon Personalize
개인화 및 추천에 대하여
Amazon Personalize 소개
Amazon Personalize 사용 방법
데모 - 캡쳐 화면
결론
Amazon Forecast
예측 기술에 대하여
Amazon Forecast 소개
Amazon Forecast 사용 방법
데모 - 캡쳐 화면
결론
New AI/ML Solutions with AWS DeepLens & Amazon SageMaker with ConocoPhillips ...Amazon Web Services
ConocoPhillips is exploring the combination of machine vision and machine learning. Four proof of concepts were developed using AWS DeepLens, Amazon SageMaker, Amazon S3, and more. These projects address the security, safety, and inventory associated with upstream field operations. In this session, we describe our successes, challenges, and lessons learned. We also share our ideas for future product improvements.
Amazon Polly와 Cloud9을 활용한 서버리스 웹 애플리케이션 및 CI/CD 배포 프로세스 구축 (김현수, AWS 솔루션즈 아키텍...Amazon Web Services Korea
Amazon Polly와 Cloud9을 활용한 서버리스 웹 애플리케이션 및 CI/CD 배포 프로세스 구축
서버리스 서비스로 쉽고 빠르게 인공지능 서비스를 활용한 웹 애플리케이션 구축을 할 수 있습니다. Cloud9 IDE를 이용하여 개발 환경을 구축하고, Lambda 함수에 코드를 작성하고, Polly를 이용해서 쉽게 TTS 서비스를 만들고, SAM(Serverless Architecture Model) 템플릿을 작성하여 서비스를 배포하는 방법을 설명합니다. 그리고 Code시리즈 서비스를 이용하여 CI/CD 프로세스를 구축하여 빌드와 단위 테스트 및 배포와 롤백 방법을 설명하고 데모를 시연합니다.
Get behind the keyboard for an immersive experience with autonomous cars at Robocar Rally 2018. In this workshop you will get hands-on-experience with machine learning and robo-racing cars. Developers with no prior machine learning or robotic experience will learn new skills and apply their knowledge in a fun and exciting way. You will join a pit crew where you will build, train, and race a robo-racing car! Start your engines, the race is on!
Machine Learning for the Enterprise, ft. Sony Interactive Entertainment (ENT2...Amazon Web Services
Machine learning is powering innovation across industries, including media & entertainment, healthcare, finance, and many more. In this session, representatives from AWS and Sony Interactive Entertainment discuss building real-world scalable enterprise solutions with machine learning using Amazon SageMaker. Join us as we talk about managing large-scale systems and processes to get more value from data at any scale, with examples from Sony and AWS.
첫 서버를 AWS 서울 리전에 시작한 2년차 개발자로서, AWS Fargate를 비롯 여러 클라우드 매니지드 서비스의 도움을 받아 모놀리틱 애플리케이션을 마이크로서비스 아키텍처로 옮겼던 과정 및 경험 등을 공유합니다. MyMusicTaste는 서비스를 전 세계 리전 배포 방법, 싱글페이지 애플리케이션(SPA)에서 흔히 겪는 SEO 및 CORS 이슈 등을 Lambda@Edge 등으로 손쉽게 대응했던 사례를 공유합니다.
Build a "Who's Who" App for Your Media Content (AIM409) - AWS re:Invent 2018Amazon Web Services
Video has become an increasingly successful medium for advertising, marketing, and engaging customers. However, many companies underutilize their substantial video assets because they are poorly indexed and cataloged. In this workshop, learn how to use machine learning services to gain more value from video by building a customer celebrity detection feature that can recognize mainstream celebrities and individuals from your own uploaded media files.
Let’s Talk about Reinforcement Learning with Amazon SageMaker RL (AIM399) - A...Amazon Web Services
Reinforcement learning has emerged as an exciting new technique in the world of machine learning (ML), where your ML models can achieve specific outcomes without the need for pre-labeled training data. Join us in this chalk talk as we discuss the newly announced Amazon SageMaker RL, which takes a different approach to training ML models. We dive deep into scenarios where there isn’t a right answer; instead, there is an optimal outcome for a given problem. At the end of this chalk talk, you will be familiar with Amazon SageMaker RL and understand how to use reinforcement learning for your businesses and build intelligent applications.
Tailor Your Alexa Skill Responses to Deliver Truly Personal Experiences (ALX3...Amazon Web Services
Delivering truly personal responses to customers is one of the most engaging features of an Alexa skill. In this session, learn the different approaches and best practices in creating responses that are tailored to each one of your customers. By applying what you learn, you can keep them coming back to your voice experience.
Build Models for Aerial Images Using Amazon SageMaker (AIM334) - AWS re:Inven...Amazon Web Services
There are unique challenges to building highly accurate models that detect small objects in aerial and overhead imagery. In this chalk talk, we dive deep into using convolutional neural networks (CNNs) with Amazon SageMaker in order to build and train aerial object detection models. We build advanced models using AWS public datasets, such as SpaceNet and LandSat, as we work with DigitalGlobe's GBDX Notebooks.
Machine Learning and Predictive Quality Management (AIM311) - AWS re:Invent 2018Amazon Web Services
From refined products to heavy crude, Four-Path Ultrasonic Flow Meters offers the capability to minimize measurement uncertainty of liquid hydrocarbons. Attendees work to build a machine learning (ML) predictive quality management (PQM) solution on AWS to proactively predict the health of the ultrasonic flow meters. This is done using the ML Data Readiness Package based on KNIME, from AWS Marketplace. Another PQM example for attendees to explore uses features extracted from motor current measured with a current probe and an oscilloscope on two phases measured under different speeds, load moments, and load forces. ML is used to proactively classify whether the motor has intact or defective components. A third PQM example involves using raw process sensor data from a hydraulic test rig with a primary working and a secondary cooling-filtration circuit, connected via the oil tank. They then use ML on AWS to proactively predict the cooler condition, hydraulic accumulator condition, internal pump leakage condition, and valve condition.
AWS 인공지능 서비스와 서버리스 서비스를 이용한 동영상 분석 서비스 구축하기 (김현수/황윤상, AWS 솔루션즈 아키텍트) :: AWS D...Amazon Web Services Korea
AWS 인공지능 서비스와 서버리스 서비스를 이용한 동영상 분석 서비스 구축하기
동영상에 포함되어 있는 다양한 정보를 쉽고 빠르게 분석하는 솔루션을 구축할 수 있습니다. VOD 동영상을 S3에 업로드하면, Lambda에서 Elemental MediaConvert를 호출하여 대량의 이미지로 분할하여 S3에 저장합니다. 대량의 이미지는 AWS Lambda를 활용하여 Rekognition 서비스를 호출하여 이미지 정보를 수집합니다. 수집 결과물은 ElasticSearch에 저장하고 Kibana를 통해 시각화 할 수 있습니다.
Broadcasting the World's Largest Sporting Events: AWS Media Services When It ...Amazon Web Services
Learn how TV New Zealand and France Television broadcast the Commonwealth Games and a major cycling event using AWS Media Services. Broadcasting live events is unpredictable. Reliability, agility, and scale all play important roles in ensuring a broadcast takes place without issues. Learn how to create channels on the fly for live events and how to seamlessly handle a fluctuation or influx of viewers using AWS Elemental MediaLive, AWS Elemental MediaPackage, and Amazon CloudFront.
Hollywood's Cloud-Based Content Lakes: Modernized Media Archives (MAE203) - A...Amazon Web Services
Content lake architecture can evolve the media workflow by providing efficiency from content security all the way to value-added services, such as machine learning and content monetization. In this session, technical leaders from 21st Century Fox, Warner Bros., and Astro Malaysia discuss the migration of their petabyte-scale video libraries (production and distribution archives) to the cloud in order to increase the customer reach and value of their media archives. Discover some of the lessons learned, the TCO analysis around various different storage tiers, the challenges and best practices from 10s of petabytes ingest, storage, and value-added compute at scale.
Intelligence of Things: IoT, AWS DeepLens and Amazon SageMaker - AWS Summit S...Amazon Web Services
Intelligence of Things: IoT, AWS DeepLens and Amazon SageMaker
With IoT, machine learning is going everywhere. Using Amazon SageMaker it's never been easier to build Intelligent Things. In this session we look at how we can push intelligence from cloud-trained models to the edge using AWS Greengrass and explore how devices such as AWS DeepLens make it easy to bring intelligence to your things.
Jan Haak, Global Solutions Architect, Amazon Web Services
Ruby Support for AWS Lambda at Native Speed with JetsTung Nguyen
Here's the presentation I gave at the AWS Summit in Toronto 2018.
Abstract:
In this dev chat, we review how to run Ruby on AWS Lambda using a tool called Jets. Jets is a framework that enables you to create serverless applications with the beautiful language, Ruby. Learn how Ruby support was added to Lambda with native-like performance, and discover how Lambda works under the hood to understand how this was accomplished. We show you how to get started through a demo with a Lambda Ruby application, and we deploy it to Lambda with a single command.
Market Prediction Using ML: Experiment with Amazon SageMaker and the Deutsche...Amazon Web Services
In this workshop, learn how to use machine learning to analyze the Deutsche Börse Public Dataset, which consists of trade data aggregated to one-minute intervals from the Tradex and Eurex engines, comprising a variety of equities, funds, and derivative securities. The public dataset provides initial price, lowest price, highest price, final price, and volume for every minute of the trading day, and for every tradeable security. Learn how to apply a variety of ML models to the data to find patterns and methods to predict price movements or identify trends in the market. Also learn how to interact with data staged for analysis in Amazon S3, use AWS Glue to transform data into analysis-ready formats, and use Amazon SageMaker and Amazon EC2 to experiment with a variety of ML models to derive insights from the data.
Supercharge Any Alexa Skill by Understanding What Games Do (ALX403-R2) - AWS ...Amazon Web Services
Games push the boundaries of tech, and we can learn from them for a wide range of use cases. In this session, we look at what makes an Alexa quiz game, interactive fiction, and companion games work. Learn how to build engaging experiences with dialog management and entity resolution (Alexa NLU), session persistence (Amazon DynamoDB), production value (SSML, Amazon S3), as well as state handling, multi-modal displays, interceptors (ASK SDK), and cross-device interactions (Gadgets, AWS IoT for PC games).
AWS Cloud9과 Workspace만으로 PC없는 개발환경 활용기
클라우드 기반의 IDE와 가상 데스크톱 서비스를 이용하면 언제 어디서나 개발 환경을 구축할 수 있습니다. 애플리케이션 서비스를 위해 서버리스(Serverless)를 이용할 수 있는 것 처럼 개발 환경도 컴퓨터리스(Computerless)로 가능합니다. AWS Cloud9과 Workspace만으로 PC없이 웹 서비스 및 안드로이드 앱 개발을 진행하는 방법을 소개합니다.
Amazon.com 의 개인화 추천 / 예측 기능을 우리도 써 봅시다. :: 심호진 - AWS Community Day 2019AWSKRUG - AWS한국사용자모임
Amazon Personalize
개인화 및 추천에 대하여
Amazon Personalize 소개
Amazon Personalize 사용 방법
데모 - 캡쳐 화면
결론
Amazon Forecast
예측 기술에 대하여
Amazon Forecast 소개
Amazon Forecast 사용 방법
데모 - 캡쳐 화면
결론
New AI/ML Solutions with AWS DeepLens & Amazon SageMaker with ConocoPhillips ...Amazon Web Services
ConocoPhillips is exploring the combination of machine vision and machine learning. Four proof of concepts were developed using AWS DeepLens, Amazon SageMaker, Amazon S3, and more. These projects address the security, safety, and inventory associated with upstream field operations. In this session, we describe our successes, challenges, and lessons learned. We also share our ideas for future product improvements.
Amazon Polly와 Cloud9을 활용한 서버리스 웹 애플리케이션 및 CI/CD 배포 프로세스 구축 (김현수, AWS 솔루션즈 아키텍...Amazon Web Services Korea
Amazon Polly와 Cloud9을 활용한 서버리스 웹 애플리케이션 및 CI/CD 배포 프로세스 구축
서버리스 서비스로 쉽고 빠르게 인공지능 서비스를 활용한 웹 애플리케이션 구축을 할 수 있습니다. Cloud9 IDE를 이용하여 개발 환경을 구축하고, Lambda 함수에 코드를 작성하고, Polly를 이용해서 쉽게 TTS 서비스를 만들고, SAM(Serverless Architecture Model) 템플릿을 작성하여 서비스를 배포하는 방법을 설명합니다. 그리고 Code시리즈 서비스를 이용하여 CI/CD 프로세스를 구축하여 빌드와 단위 테스트 및 배포와 롤백 방법을 설명하고 데모를 시연합니다.
Get behind the keyboard for an immersive experience with autonomous cars at Robocar Rally 2018. In this workshop you will get hands-on-experience with machine learning and robo-racing cars. Developers with no prior machine learning or robotic experience will learn new skills and apply their knowledge in a fun and exciting way. You will join a pit crew where you will build, train, and race a robo-racing car! Start your engines, the race is on!
Machine Learning for the Enterprise, ft. Sony Interactive Entertainment (ENT2...Amazon Web Services
Machine learning is powering innovation across industries, including media & entertainment, healthcare, finance, and many more. In this session, representatives from AWS and Sony Interactive Entertainment discuss building real-world scalable enterprise solutions with machine learning using Amazon SageMaker. Join us as we talk about managing large-scale systems and processes to get more value from data at any scale, with examples from Sony and AWS.
첫 서버를 AWS 서울 리전에 시작한 2년차 개발자로서, AWS Fargate를 비롯 여러 클라우드 매니지드 서비스의 도움을 받아 모놀리틱 애플리케이션을 마이크로서비스 아키텍처로 옮겼던 과정 및 경험 등을 공유합니다. MyMusicTaste는 서비스를 전 세계 리전 배포 방법, 싱글페이지 애플리케이션(SPA)에서 흔히 겪는 SEO 및 CORS 이슈 등을 Lambda@Edge 등으로 손쉽게 대응했던 사례를 공유합니다.
Build a "Who's Who" App for Your Media Content (AIM409) - AWS re:Invent 2018Amazon Web Services
Video has become an increasingly successful medium for advertising, marketing, and engaging customers. However, many companies underutilize their substantial video assets because they are poorly indexed and cataloged. In this workshop, learn how to use machine learning services to gain more value from video by building a customer celebrity detection feature that can recognize mainstream celebrities and individuals from your own uploaded media files.
Let’s Talk about Reinforcement Learning with Amazon SageMaker RL (AIM399) - A...Amazon Web Services
Reinforcement learning has emerged as an exciting new technique in the world of machine learning (ML), where your ML models can achieve specific outcomes without the need for pre-labeled training data. Join us in this chalk talk as we discuss the newly announced Amazon SageMaker RL, which takes a different approach to training ML models. We dive deep into scenarios where there isn’t a right answer; instead, there is an optimal outcome for a given problem. At the end of this chalk talk, you will be familiar with Amazon SageMaker RL and understand how to use reinforcement learning for your businesses and build intelligent applications.
Tailor Your Alexa Skill Responses to Deliver Truly Personal Experiences (ALX3...Amazon Web Services
Delivering truly personal responses to customers is one of the most engaging features of an Alexa skill. In this session, learn the different approaches and best practices in creating responses that are tailored to each one of your customers. By applying what you learn, you can keep them coming back to your voice experience.
Build Models for Aerial Images Using Amazon SageMaker (AIM334) - AWS re:Inven...Amazon Web Services
There are unique challenges to building highly accurate models that detect small objects in aerial and overhead imagery. In this chalk talk, we dive deep into using convolutional neural networks (CNNs) with Amazon SageMaker in order to build and train aerial object detection models. We build advanced models using AWS public datasets, such as SpaceNet and LandSat, as we work with DigitalGlobe's GBDX Notebooks.
Machine Learning and Predictive Quality Management (AIM311) - AWS re:Invent 2018Amazon Web Services
From refined products to heavy crude, Four-Path Ultrasonic Flow Meters offers the capability to minimize measurement uncertainty of liquid hydrocarbons. Attendees work to build a machine learning (ML) predictive quality management (PQM) solution on AWS to proactively predict the health of the ultrasonic flow meters. This is done using the ML Data Readiness Package based on KNIME, from AWS Marketplace. Another PQM example for attendees to explore uses features extracted from motor current measured with a current probe and an oscilloscope on two phases measured under different speeds, load moments, and load forces. ML is used to proactively classify whether the motor has intact or defective components. A third PQM example involves using raw process sensor data from a hydraulic test rig with a primary working and a secondary cooling-filtration circuit, connected via the oil tank. They then use ML on AWS to proactively predict the cooler condition, hydraulic accumulator condition, internal pump leakage condition, and valve condition.
AWS 인공지능 서비스와 서버리스 서비스를 이용한 동영상 분석 서비스 구축하기 (김현수/황윤상, AWS 솔루션즈 아키텍트) :: AWS D...Amazon Web Services Korea
AWS 인공지능 서비스와 서버리스 서비스를 이용한 동영상 분석 서비스 구축하기
동영상에 포함되어 있는 다양한 정보를 쉽고 빠르게 분석하는 솔루션을 구축할 수 있습니다. VOD 동영상을 S3에 업로드하면, Lambda에서 Elemental MediaConvert를 호출하여 대량의 이미지로 분할하여 S3에 저장합니다. 대량의 이미지는 AWS Lambda를 활용하여 Rekognition 서비스를 호출하여 이미지 정보를 수집합니다. 수집 결과물은 ElasticSearch에 저장하고 Kibana를 통해 시각화 할 수 있습니다.
Broadcasting the World's Largest Sporting Events: AWS Media Services When It ...Amazon Web Services
Learn how TV New Zealand and France Television broadcast the Commonwealth Games and a major cycling event using AWS Media Services. Broadcasting live events is unpredictable. Reliability, agility, and scale all play important roles in ensuring a broadcast takes place without issues. Learn how to create channels on the fly for live events and how to seamlessly handle a fluctuation or influx of viewers using AWS Elemental MediaLive, AWS Elemental MediaPackage, and Amazon CloudFront.
Hollywood's Cloud-Based Content Lakes: Modernized Media Archives (MAE203) - A...Amazon Web Services
Content lake architecture can evolve the media workflow by providing efficiency from content security all the way to value-added services, such as machine learning and content monetization. In this session, technical leaders from 21st Century Fox, Warner Bros., and Astro Malaysia discuss the migration of their petabyte-scale video libraries (production and distribution archives) to the cloud in order to increase the customer reach and value of their media archives. Discover some of the lessons learned, the TCO analysis around various different storage tiers, the challenges and best practices from 10s of petabytes ingest, storage, and value-added compute at scale.
Intelligence of Things: IoT, AWS DeepLens and Amazon SageMaker - AWS Summit S...Amazon Web Services
Intelligence of Things: IoT, AWS DeepLens and Amazon SageMaker
With IoT, machine learning is going everywhere. Using Amazon SageMaker it's never been easier to build Intelligent Things. In this session we look at how we can push intelligence from cloud-trained models to the edge using AWS Greengrass and explore how devices such as AWS DeepLens make it easy to bring intelligence to your things.
Jan Haak, Global Solutions Architect, Amazon Web Services
Ruby Support for AWS Lambda at Native Speed with JetsTung Nguyen
Here's the presentation I gave at the AWS Summit in Toronto 2018.
Abstract:
In this dev chat, we review how to run Ruby on AWS Lambda using a tool called Jets. Jets is a framework that enables you to create serverless applications with the beautiful language, Ruby. Learn how Ruby support was added to Lambda with native-like performance, and discover how Lambda works under the hood to understand how this was accomplished. We show you how to get started through a demo with a Lambda Ruby application, and we deploy it to Lambda with a single command.
Using AI for real-life data enrichment - Tel Aviv Summit 2018Amazon Web Services
In this session, we will learn how we used Amazon Machine Learning services to enrich our datasets, we will use Amazon rekognition to extract data from pictures and Amazon comprehend to get sentiments and areas of interests from posts. We'll use Amazon SageMaker built-in algorithms to easily build and train a machine learning model and deploy it into a production-ready hosted environment.
Predicting the Future with Amazon SageMaker - AWS Summit Sydney 2018Amazon Web Services
Predicting the Future with Amazon SageMaker
Amazon SageMaker removes all the barriers that typically slow down developers who want to use machine learning. In this session you will learn how to use built-in, high performance machine learning algorithms for predictions and computer vision within your application. We will deploy machine learning models into production and start generating classifications with a few API calls using the SageMaker SDK. Additionally we will demonstrate how to run your custom trained machine learning model directly out of your web application to classify incoming user generated content.
Steve Shirkey, ASEAN Solutions Architect, Amazon Web Services
[REPEAT 1] Create and Publish AR, VR, and 3D Applications Using Amazon Sumeri...Amazon Web Services
In this session, learn how Amazon Sumerian can help 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. Learn how you can use Sumerian to build highly immersive and interactive scenes that run on popular hardware, such as Oculus Go, Oculus Rift, Google Daydream, and HTC Vive as well as on Android and iOS mobile devices.
Resiliency and Availability Design Patterns for the CloudAmazon Web Services
We have traditionally built robust software systems by trying to avoid mistakes and by dodging failures when they occur in production or by testing parts of the system in isolation from one another. Modern methods and techniques take a very different approach based on resiliency, which promotes embracing failure instead of trying to avoid it. Resilient architectures enhance observability, leverage well-known patterns such as graceful degradation, timeouts and circuit breakers but also new patterns like cell-based architecture and shuffle sharding. In this session, will review the most useful patterns for building resilient software systems and especially show the audience how they can benefit from the patterns.
This is the presentation I gave at the Toronto Serverless Meetup in September 2018.
We review how to run Ruby on AWS Lambda using a tool called Jets. Jets is a framework that enables you to create serverless applications with the beautiful language, Ruby. Learn how Ruby support was added to Lambda with native-like performance, and discover how Lambda works under the hood to understand how this was accomplished. We'll provide a live demo.
We'll also cover 4 different architectures built with Jets:
1. Web Restful API Architecture
2. Event Driven Security: Auto-Remediation
3. Continuous Compliance: AWS Config Rules
4. Event Driven Internet of Things
[NEW LAUNCH!] Introducing Amazon SageMaker RL - Build and Train Reinforcement...Amazon Web Services
Reinforcement Learning is an exciting area within machine learning that enables development of many intelligent applications such as autonomous vehicles and robots. The applications with Reinforcement Learning can span across many areas including energy management, financial portfolio management, operations research, natural language processing, and many more. In this interactive workshop, you will learn the basics of Reinforcement Learning (RL) and how you can build and train RL models with the newly announced Amazon SageMaker RL. We will model a simulation environment to represent real-world problems. Further, we will train RL models in this environment and tune them to obtain the required results. By the end of this workshop, you will become familiar with Reinforcement Learning and be able to use SageMaker RL for your own business problems to build intelligent applications.
[NEW LAUNCH!] [REPEAT 1] AWS DeepRacer Workshops –a new, fun way to learn rei...Amazon Web Services
Get behind the keyboard for an immersive experience with the newly launched AWS DeepRacer. In this workshop you will get hands-on-experience with reinforcement learning. Developers with no prior machine learning experience will learn new skills and apply their knowledge in a fun and exciting way. You will join a pit crew where you will build and train machine learning models that you can then try out at the MGM Speedway event at the Grand Garden Arena! Please bring your laptop, and start your engines, the race is on!
Supercharge Your ML Model with SageMaker - AWS Summit Sydney 2018Amazon Web Services
Supercharge Your Machine Learning Model with Amazon SageMaker
In this session you will learn how to use Amazon SageMaker to build, train, test, and deploy a machine learning model. We will use a real life use case to share the simplicity of building and deploying ML models on Amazon SageMaker.
Koorosh Lohrasbi, Solutions Architect, Amazon Web Services
How to Use Predictive Scaling (API331-R1) - AWS re:Invent 2018Amazon Web Services
Do you have cyclical loads for your application? Do you want your applications to scale to a 9 to 5 pattern in various geographies? Learn how to set up Predictive Scaling using the AWS Auto Scaling Console. We will walk through use cases such as using Predictive Scaling with your existing scaling policies, setting up Predictive Scaling for multiple Auto Scaling Groups with a single scaling plan, and using Predictive Scaling with blue-green deployments. You will leave the session with a solid understanding of when and how to use Predictive Scaling.
Artificial Intelligence (AI) services on the AWS cloud bring the power of deep learning within reach of every developer, allowing you to develop new tools and enrich your systems with new capabilities. In this session, we will look into the opportunities to apply one or more of these services provide a number of examples and use cases to help you get started.
Globalizing Player Accounts at Riot Games While Maintaining Availability (ARC...Amazon Web Services
The Player Accounts team at Riot Games needed to consolidate the player account infrastructure and provide a single, global accounts system for the League of Legends player base. To do this, they migrated hundreds of millions of player accounts into a consolidated, globally replicated composite database cluster in AWS. This provided higher fault tolerance and lower latency access to account data. In this talk, we discuss this effort to migrate eight disparate database clusters into AWS as a single composite database cluster replicated in four different AWS regions, provisioned with terraform, and managed and operated by Ansible.
Sequence-to-Sequence Modeling with Apache MXNet, Sockeye, and Amazon SageMake...Amazon Web Services
In this session, we discuss the "encoder-decoder architecture with attention," a state-of-the-art architecture for natural language processing. This architecture is implemented in the Sockeye package of MXNet and is used by the sequence-to-sequence algorithm of Amazon SageMaker.
Working with Scalable Machine Learning Algorithms in Amazon SageMaker - AWS O...Amazon Web Services
Learning Objectives:
- Become aquainted with the popular algorithms provided with Amazon SageMaker
- Learn how to use algorithms for training in Amazon SageMaker
- Learn how the algorithms in Amazon SageMaker were architected to be faster and more efficient by design
[NEW LAUNCH!] Introduction to AWS Global Accelerator (NET330) - AWS re:Invent...Amazon Web Services
This session introduces AWS Global Accelerator, a new global service that enables you to optimally route traffic to your multi-regional endpoints via static Anycast IP addresses that are announced from the expansive AWS edge network. This session walks through the various features and customer use cases for Global Accelerator. Several example use cases demonstrate how you can use Ubiquity to achieve near-zero application downtime and reduce latency for your global applications. We will walk you through the architecture and will also include a demo of the workflow. Attend this session if you are looking at ways to accelerate performance of your global applications, achieve high availability for your mission critical applications or easily manage multiple IP addresses through a static Anycast IP that fronts your applications.
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
Similar to SageMaker로 강화학습(RL) 마스터링 :: 남궁선 - AWS Community Day 2019 (20)
자연어 처리 ML모델을 활용한 이커머스 문제 해결하기 - 진현두 (카카오스타일) :: AWS Community Day Online 2021AWSKRUG - AWS한국사용자모임
이커머스에서 가장 중요한 사용자 리뷰! 사용자 리뷰 내 특정 키워드 관련 표현을 스마트하게 찾고, 하이라이팅 정보를 제공하는 ML모델 개발하여 서비스에 반영하였습니다. 복잡한 전처리와 모델링전반의 프로세스를 Amazon SageMaker + Custom Docker 로 구현 방법을 소개합니다.
자바개발자가 최대한 빠르게 서비스를 오픈하는 방법 - 최진환 (드라마앤컴퍼니) :: AWS Community Day Online 2021AWSKRUG - AWS한국사용자모임
사이드프로젝트를 진행하면서 겪은 다양한 인프라 구축 노하우를 소개합니다.왜 EKS가 아닌 Elastic Beanstalk를 사용했는지, Codepipeline을 이용한 깃헙에서 배포까지의 플로우, AWS ChatBot을 사용한 모니터링과 CodeBuild로 빌드하기 등을 소개합니다.
EKS에서 Opentelemetry로 코드실행 모니터링하기 - 신재현 (인덴트코퍼레이션) :: AWS Community Day Online...AWSKRUG - AWS한국사용자모임
EKS환경에서 Opentelemetry와 Jaeger를 활용하여 서버의 코드가 잘 동작하는지 어떤로직에서 문제가 발생했는지 모니터링 하는 방법을 알아봅니다. 마지막으로 Grafana를 이용해 쉽게 원하는 코드를 조회 하는 방법도 실습해볼 예정입니다. K8S를 모르셔도 참석할 수 있습니다.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Unleashing the Power of Data_ Choosing a Trusted Analytics Platform.pdfEnterprise Wired
In this guide, we'll explore the key considerations and features to look for when choosing a Trusted analytics platform that meets your organization's needs and delivers actionable intelligence you can trust.
Adjusting OpenMP PageRank : SHORT REPORT / NOTESSubhajit Sahu
For massive graphs that fit in RAM, but not in GPU memory, it is possible to take
advantage of a shared memory system with multiple CPUs, each with multiple cores, to
accelerate pagerank computation. If the NUMA architecture of the system is properly taken
into account with good vertex partitioning, the speedup can be significant. To take steps in
this direction, experiments are conducted to implement pagerank in OpenMP using two
different approaches, uniform and hybrid. The uniform approach runs all primitives required
for pagerank in OpenMP mode (with multiple threads). On the other hand, the hybrid
approach runs certain primitives in sequential mode (i.e., sumAt, multiply).
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found