인공 지능을 공부하려는 개발자들이 필연적으로 부딪히는 문제 상황에 대한 해법을 알려드립니다. 본 세션에서는 손쉬운 딥러닝 인프라 설정, 빠른 모델 학습 과정, 기존 서비스에 인공 지능 기능 탑재 방법 등에 대한 다양한 서비스와 활용 사례를 데모와 함께 보여 드립니다.
- 딥러닝 인프라 설정 및 빠른 모델 학습 과정 소개
- AI 기반 이미지 인식 및 TTS 서비스 서비스 활용 사례
클라우드의 미래를 가늠할 re:Invent를 통해 출시한 다양한 서비스를 소개합니다. 본 세션에서는 폭넓고 깊이 있는 클라우드 (컴퓨팅) 서비스를 제공하기 위한 가상 서버, 콘테이너 및 서버리스 분야의 신규 서비스를 알아봅니다. 베어 메탈을 비롯한 가상화 분야의 혁신 과정과 쿠버네티스 관리형 EKS 서비스와 클러스터 관리 조차 없는 Fargate, 그리고 서버리스 분야의 최신 람다 기능 업데이트 및 아키텍처 확장 사례 등을 공유합니다.
최근 데이터의 폭증과 이를 기반한 빅데이터 분석이 기업 비지니스 성패에 큰 영향을 끼치고 있습니다. 다양한 기업의 데이터 기반 의사 결정을 위한 요구를 수용하는 분석 플랫폼과 인공 지능 기술의 도입은 큰 화두입니다. 본 세션에서는 기업의 비지니스 전략 및 기획을 담당하시는 분들을 위해 클라우드 기반 데이터 분석 플랫폼을 쉽게 접근하고 사용할 수 있는 방법을 사례 위주로 소개합니다.국내외 주요 기업들이 어떻게 AWS기반 데이터 분석 및 기계 학습 서비스로 비지니스 혁신에 활용하고 있는지 알아보시기 바랍니다.
Amazon SageMaker는 기계 학습을 위한 데이터와 알고리즘, 프레임워크를 빠르게 연결하에 손쉽게 ML 구축이 가능한 신규 클라우드 서비스입니다. 이번 시간에는 Amazon S3에 저장된 학습 데이터를 이용하여 가장 일반적으로 사용하는 알고리즘 몇 가지를 직접 실행해 보는 실습을 진행합니다. 이를 위해 유명한 오픈 소스 프레임워크인 TensorFlow와 Keras 그리고 Apache MXNet과 Gluon 등을 사용해 봅니다.
Константин Макарычев (Sofware Engineer): ИСПОЛЬЗОВАНИЕ SPARK ДЛЯ МАШИННОГО ОБ...Provectus
"Apache Spark – опенсорсный движок для обработки больших объёмов данных. Помимо прочего, spark содержит в себе всё необходимое для машинного обучения, и это действительно просто до тех пор, пока не нужно использовать результаты на продакшне. Я расскажу, как работает machine learning на spark и в целом, как вывести всё это в продакшн, и что можно сделать из этого интересного"
클라우드의 미래를 가늠할 re:Invent를 통해 출시한 다양한 서비스를 소개합니다. 본 세션에서는 폭넓고 깊이 있는 클라우드 (컴퓨팅) 서비스를 제공하기 위한 가상 서버, 콘테이너 및 서버리스 분야의 신규 서비스를 알아봅니다. 베어 메탈을 비롯한 가상화 분야의 혁신 과정과 쿠버네티스 관리형 EKS 서비스와 클러스터 관리 조차 없는 Fargate, 그리고 서버리스 분야의 최신 람다 기능 업데이트 및 아키텍처 확장 사례 등을 공유합니다.
최근 데이터의 폭증과 이를 기반한 빅데이터 분석이 기업 비지니스 성패에 큰 영향을 끼치고 있습니다. 다양한 기업의 데이터 기반 의사 결정을 위한 요구를 수용하는 분석 플랫폼과 인공 지능 기술의 도입은 큰 화두입니다. 본 세션에서는 기업의 비지니스 전략 및 기획을 담당하시는 분들을 위해 클라우드 기반 데이터 분석 플랫폼을 쉽게 접근하고 사용할 수 있는 방법을 사례 위주로 소개합니다.국내외 주요 기업들이 어떻게 AWS기반 데이터 분석 및 기계 학습 서비스로 비지니스 혁신에 활용하고 있는지 알아보시기 바랍니다.
Amazon SageMaker는 기계 학습을 위한 데이터와 알고리즘, 프레임워크를 빠르게 연결하에 손쉽게 ML 구축이 가능한 신규 클라우드 서비스입니다. 이번 시간에는 Amazon S3에 저장된 학습 데이터를 이용하여 가장 일반적으로 사용하는 알고리즘 몇 가지를 직접 실행해 보는 실습을 진행합니다. 이를 위해 유명한 오픈 소스 프레임워크인 TensorFlow와 Keras 그리고 Apache MXNet과 Gluon 등을 사용해 봅니다.
Константин Макарычев (Sofware Engineer): ИСПОЛЬЗОВАНИЕ SPARK ДЛЯ МАШИННОГО ОБ...Provectus
"Apache Spark – опенсорсный движок для обработки больших объёмов данных. Помимо прочего, spark содержит в себе всё необходимое для машинного обучения, и это действительно просто до тех пор, пока не нужно использовать результаты на продакшне. Я расскажу, как работает machine learning на spark и в целом, как вывести всё это в продакшн, и что можно сделать из этого интересного"
Deep learning at supercomputing scale by Rangan Sukumar from CrayBill Liu
Presented at AI NEXTCon Seattle 1/17-20, 2018
http://aisea18.xnextcon.com
join our free online AI group with 50,000+ tech engineers to learn and practice AI technology, including: latest AI news, tech articles/blogs, tech talks, tutorial videos, and hands-on workshop/codelabs, on machine learning, deep learning, data science, etc..
AWS DevDay 2017 - 윤석찬(AWS), 김동희(로켓펀치), 윤석준(직방)
클라우드에서 가상 서버를 운영하지 않더라도 확장성에 대한 걱정없이 대용량 트래픽을 처리하는 웹 애플리케이션을 구축할 수 있다면 어떨까요? 본 세션에서는 AWS Lambda, Amazon API Gateway, Amazon DynamoDB 및 Amazon S3와 같은 완전 관리형 AWS 서비스를 사용하여 손쉽게 서버리스(Serverless) 웹 사이트를 구축하는 방법을 직접 코드 데모와 함께 살펴봅니다. 서버리스 기반으로 비용 효율적인 웹 애플리케이션을 구축한 로켓 펀치 고객 사례와 함께 AWS Serverless Application Model (AWS SAM), Chalice, Apex, Zappa와 같은 서버리스 애플리케이션을 구축하는 데 도움이되는 여러 프레임워크와 배포 방법도 살펴봅니다.
Scalable Deep Learning on AWS Using Apache MXNet - AWS Summit Tel Aviv 2017Amazon Web Services
Artificial Intelligence (AI) and deep learning are now ready to power your business, as it is powering most of the innovation of Amazon.com with autonomous drones, and robots, Amazon Alexa, Amazon Go, and many other hard and important business problems. Come and learn why and how to get started with deep learning, and what you can expect from a future with better AI in the cloud and on the edge.
AWS Batch는 다양한 컴퓨팅 일괄 처리 작업을 손쉽게 관리해 주는 서비스입니다. 작업 규모에 따라 자동으로 EC2 워크로드 배포를 최적화해 줍니다. 본 세션에서는 AWS Batch의 핵심 개념과 서비스 기능 및 배치 작업의 다양한 처리 패턴 및 다른 서비스와 연동한 처리 방법을 다룹니다. 지난 3월 서울 리전에 출시한 Batch 서비스에 대한 자세한 소개와 함께 다양한 활용 팁을 데모와 함께 소개해 드립니다.
New developments in open source ecosystem spark3.0 koalas delta lakeXiao Li
In this talk, we will highlight major efforts happening in the Spark ecosystem. In particular, we will dive into the details of adaptive and static query optimizations in Spark 3.0 to make Spark easier to use and faster to run. We will also demonstrate how new features in Koalas, an open source library that provides Pandas-like API on top of Spark, helps data scientists gain insights from their data quicker.
Neptune Performance Tuning: Get the Best out of Amazon Neptune (DAT360) - AWS...Amazon Web Services
In this builders session, we cover the fundamentals of performance tuning for OLTP query workloads against Amazon Neptune. Using AWS CloudFormation scripts, participants have the opportunity to set up a Neptune and Jupyter Notebook stack, enabling them to run small read and write query workloads against Neptune in their own AWS account. After a short discussion of Neptune’s architecture, we tune the scripts to maximize the throughput of the sample workloads through client-side parameter tuning and server-side improvements, such as failover to larger instance types and provisioning additional read replicas. Throughout the session, we discuss how to use Amazon CloudWatch metrics to understand system behavior and identify optimization potential.
Running Intelligent Applications inside a Database: Deep Learning with Python...Miguel González-Fierro
In this talk we present a new paradigm of computation where the intelligence is computed inside the database. Standard software systems must get the data from the database to execute a routine. If the size of the data is big, there are inefficiencies due to the data movement. Store procedures tried to solve this issue in the past, allowing for computing simple functions inside the database. However, only simple routines can be executed.
To showcase the capabilities of our new system, we created a lung cancer detection algorithm using Microsoft’s Cognitive Toolkit, also known as CNTK. We used transfer learning between ImageNet dataset, which contains natural images, and a lung cancer dataset, which contains scans of horizontal sections of the lung for healthy and sick patients. Specifically, a pretrained Convolutional Neural Network on ImageNet is used on the lung cancer dataset to generate features. Once the features are computed, a boosted tree is applied to predict whether the patient has cancer or not.
All this process is computed inside the database, so the data movement is minimized. We are even able to execute the algorithm using the GPU of the virtual machine that hosts the database. Using a GPU, we can compute the featurization in less than 1h, in contrast to using a CPU, that would take up to 32h. Finally, we set up an API to connect the solution to a web app, where a doctor can analyze the images and get a prediction of a patient.
BigDL Deep Learning in Apache Spark - AWS re:invent 2017Dave Nielsen
In this talk, you will learn how to use, or create Deep Learning architectures for Image Recognition and other neural network computations in Apache Spark. Alex, Tim and Sujee will begin with an introduction to Deep Learning using BigDL. Then they will explain and demonstrate how image recognition works using step by step diagrams, and code which will give you a fundamental understanding of how you can perform image recognition tasks within Apache Spark. Then, they will give a quick overview of how to perform image recognition on a much larger dataset using the Inception architecture. BigDL was created specifically for Spark and takes advantage of Spark’s ability to distribute data processing workloads across many nodes. As an attendee in this session, you will learn how to run the demos on your laptop, on your own cluster, or use the BigDL AMI in the AWS Marketplace. Either way, you walk away with a much better understanding of how to run deep learning workloads using Apache Spark with BigDL. Presentation by Alex Kalinin, Tim Fox, Sujee Maniyam & Dave Nielsen at re:invent.
Lumberjacking on AWS: Cutting Through Logs to Find What Matters (ARC306) | AW...Amazon Web Services
AWS offers services that revolutionize the scale and cost for customers to extract information from large data sets, commonly called Big Data. This session analyzes Amazon CloudFront logs combined with additional structured data as a scenario for correlating log and transactional data. Successfully implementing this type of solution requires architects and developers to assemble a set of services with multiple decision points. The session provides a design and example of architecting and implementing the scenario using Amazon S3, AWS Data Pipeline, Amazon Elastic MapReduce, and Amazon Redshift. It explores loading, query performance, security, incremental updates, and design trade-off decisions.
Deep learning at supercomputing scale by Rangan Sukumar from CrayBill Liu
Presented at AI NEXTCon Seattle 1/17-20, 2018
http://aisea18.xnextcon.com
join our free online AI group with 50,000+ tech engineers to learn and practice AI technology, including: latest AI news, tech articles/blogs, tech talks, tutorial videos, and hands-on workshop/codelabs, on machine learning, deep learning, data science, etc..
AWS DevDay 2017 - 윤석찬(AWS), 김동희(로켓펀치), 윤석준(직방)
클라우드에서 가상 서버를 운영하지 않더라도 확장성에 대한 걱정없이 대용량 트래픽을 처리하는 웹 애플리케이션을 구축할 수 있다면 어떨까요? 본 세션에서는 AWS Lambda, Amazon API Gateway, Amazon DynamoDB 및 Amazon S3와 같은 완전 관리형 AWS 서비스를 사용하여 손쉽게 서버리스(Serverless) 웹 사이트를 구축하는 방법을 직접 코드 데모와 함께 살펴봅니다. 서버리스 기반으로 비용 효율적인 웹 애플리케이션을 구축한 로켓 펀치 고객 사례와 함께 AWS Serverless Application Model (AWS SAM), Chalice, Apex, Zappa와 같은 서버리스 애플리케이션을 구축하는 데 도움이되는 여러 프레임워크와 배포 방법도 살펴봅니다.
Scalable Deep Learning on AWS Using Apache MXNet - AWS Summit Tel Aviv 2017Amazon Web Services
Artificial Intelligence (AI) and deep learning are now ready to power your business, as it is powering most of the innovation of Amazon.com with autonomous drones, and robots, Amazon Alexa, Amazon Go, and many other hard and important business problems. Come and learn why and how to get started with deep learning, and what you can expect from a future with better AI in the cloud and on the edge.
AWS Batch는 다양한 컴퓨팅 일괄 처리 작업을 손쉽게 관리해 주는 서비스입니다. 작업 규모에 따라 자동으로 EC2 워크로드 배포를 최적화해 줍니다. 본 세션에서는 AWS Batch의 핵심 개념과 서비스 기능 및 배치 작업의 다양한 처리 패턴 및 다른 서비스와 연동한 처리 방법을 다룹니다. 지난 3월 서울 리전에 출시한 Batch 서비스에 대한 자세한 소개와 함께 다양한 활용 팁을 데모와 함께 소개해 드립니다.
New developments in open source ecosystem spark3.0 koalas delta lakeXiao Li
In this talk, we will highlight major efforts happening in the Spark ecosystem. In particular, we will dive into the details of adaptive and static query optimizations in Spark 3.0 to make Spark easier to use and faster to run. We will also demonstrate how new features in Koalas, an open source library that provides Pandas-like API on top of Spark, helps data scientists gain insights from their data quicker.
Neptune Performance Tuning: Get the Best out of Amazon Neptune (DAT360) - AWS...Amazon Web Services
In this builders session, we cover the fundamentals of performance tuning for OLTP query workloads against Amazon Neptune. Using AWS CloudFormation scripts, participants have the opportunity to set up a Neptune and Jupyter Notebook stack, enabling them to run small read and write query workloads against Neptune in their own AWS account. After a short discussion of Neptune’s architecture, we tune the scripts to maximize the throughput of the sample workloads through client-side parameter tuning and server-side improvements, such as failover to larger instance types and provisioning additional read replicas. Throughout the session, we discuss how to use Amazon CloudWatch metrics to understand system behavior and identify optimization potential.
Running Intelligent Applications inside a Database: Deep Learning with Python...Miguel González-Fierro
In this talk we present a new paradigm of computation where the intelligence is computed inside the database. Standard software systems must get the data from the database to execute a routine. If the size of the data is big, there are inefficiencies due to the data movement. Store procedures tried to solve this issue in the past, allowing for computing simple functions inside the database. However, only simple routines can be executed.
To showcase the capabilities of our new system, we created a lung cancer detection algorithm using Microsoft’s Cognitive Toolkit, also known as CNTK. We used transfer learning between ImageNet dataset, which contains natural images, and a lung cancer dataset, which contains scans of horizontal sections of the lung for healthy and sick patients. Specifically, a pretrained Convolutional Neural Network on ImageNet is used on the lung cancer dataset to generate features. Once the features are computed, a boosted tree is applied to predict whether the patient has cancer or not.
All this process is computed inside the database, so the data movement is minimized. We are even able to execute the algorithm using the GPU of the virtual machine that hosts the database. Using a GPU, we can compute the featurization in less than 1h, in contrast to using a CPU, that would take up to 32h. Finally, we set up an API to connect the solution to a web app, where a doctor can analyze the images and get a prediction of a patient.
BigDL Deep Learning in Apache Spark - AWS re:invent 2017Dave Nielsen
In this talk, you will learn how to use, or create Deep Learning architectures for Image Recognition and other neural network computations in Apache Spark. Alex, Tim and Sujee will begin with an introduction to Deep Learning using BigDL. Then they will explain and demonstrate how image recognition works using step by step diagrams, and code which will give you a fundamental understanding of how you can perform image recognition tasks within Apache Spark. Then, they will give a quick overview of how to perform image recognition on a much larger dataset using the Inception architecture. BigDL was created specifically for Spark and takes advantage of Spark’s ability to distribute data processing workloads across many nodes. As an attendee in this session, you will learn how to run the demos on your laptop, on your own cluster, or use the BigDL AMI in the AWS Marketplace. Either way, you walk away with a much better understanding of how to run deep learning workloads using Apache Spark with BigDL. Presentation by Alex Kalinin, Tim Fox, Sujee Maniyam & Dave Nielsen at re:invent.
Lumberjacking on AWS: Cutting Through Logs to Find What Matters (ARC306) | AW...Amazon Web Services
AWS offers services that revolutionize the scale and cost for customers to extract information from large data sets, commonly called Big Data. This session analyzes Amazon CloudFront logs combined with additional structured data as a scenario for correlating log and transactional data. Successfully implementing this type of solution requires architects and developers to assemble a set of services with multiple decision points. The session provides a design and example of architecting and implementing the scenario using Amazon S3, AWS Data Pipeline, Amazon Elastic MapReduce, and Amazon Redshift. It explores loading, query performance, security, incremental updates, and design trade-off decisions.
Amazon EC2 provides a broad selection of instance types to accommodate a diverse mix of workloads. In this session, we provide an overview of the Amazon EC2 instance platform, key platform features, and the concept of instance generations. We dive into the current generation design choices of the different instance families, including the General Purpose, Compute Optimized, Storage Optimized, Memory Optimized, and GPU instance families. We also detail best practices and share performance tips for getting the most out of your Amazon EC2 instances.
Reinventing Amazon EC2 Instance Launches with Launch Templates (CMP369-R1) - ...Amazon Web Services
Launch templates are a new way to interact with Amazon EC2. They allow Amazon EC2 customers to define a configuration, persist it, and reuse it across many Amazon EC2 products/APIs. In this chalk talk, we look into specifics of launch templates, various scenarios and use cases, and how to use them with Amazon EC2 RunInstances, Spot Fleet, and Auto Scaling APIs. With launch templates, we are truly reinventing Amazon EC2. Join us and discover how you can achieve the benefits of launch templates.
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.
Machine learning at scale with aws sage makerPhilipBasford
The adoption of Machine Learning (ML) has boomed over the last 12 months; from initial prototypes and now into fully managed production workloads that embed ML in critical areas of both start-up and enterprise businesses. These workloads need to be highly available, elastic, have low latency, be very secure, and also cost efficient.
The corner stone of this is AWS SageMaker. AWS SageMaker offers a great platform for Machine Learning that includes one-click deployment of models for inference using AWS SageMaker Endpoints. This talk will provide advice and recommendations on how to use cases AWS SageMaker Endpoints as there is an awful lot more to AWS SageMaker Endpoints than meets the eye. During this talk we will look how to use AWS SageMaker Endpoints, how to build a custom model; look at how to scale them using Auto Scaling, look at canary style deployments, how to monitor them with CloudWatch. We will also look at how AWS SageMaker Endpoints can be used within serverless APIs with real-time observations using AWS X-Ray.
Visibility into Serverless Applications built using AWS Fargate (CON312-R1) -...Amazon Web Services
Ever wondered how you would get visibility into your application when you go serverless? In this session, we will dive deep into various visibility aspects of your serverless applications on AWS Fargate. We will cover best practices around logging, alerting, metric collection and monitoring health of your containers. We will also learn several ways to troubleshoot container start up issues or application errors. Catalytic will then show how they’re using Fargate to perform parallelized bioinformatics workflows and how they gain better visibility into their applications running on Fargate.
In this session, we discuss how scientists worldwide – from CERN, the National Health Service in the UK, and the Communication Research Centre (CRC) Canada – are using AWS to accelerate the pace of research. Enjoy a range of case studies and overview of tools, such as high-performance computing, machine learning, and deep learning, using AWS research. Discover how to seamlessly extend on-premises clusters into Amazon EC2; and how researchers from PHAC use this method daily at the National Microbiology Lab (NML) in Winnipeg to sequence genome data, rapidly detect disease outbreaks, and inform public health responses. Finally, learn about CRC’s advanced, secure scientific-computing environment in the cloud, plus its role in democratizing high-performance computing for various scientific needs.
Our company went from $230K down to $25K. This was done by taking planned steps towards cost reduction, it wasn't an accident. More importantly, we did this without loss of functionality.
AWS re:Invent re:Cap 행사에서 발표된 강연 자료입니다. 아마존 웹서비스의 김일호 솔루션스 아키텍트가 발표한 내용입니다.
내용 요약: Hadoop과 Elastic MapReduce, Redshift, Kinesis, Data Pipeline, S3 등 다양한 서비스들을 활용하는 데이터 분석의 모범사례 및 아키텍처 설계 패턴에 대해 말씀드리고, re:Invent에서 새로 추가된 Amazon EC2 컴퓨팅 최적화 인스턴스 C4와 새로 발표된 Amazon EBS 볼륨 확장 및 성능 향상에 대해 함께 살펴볼 예정입니다.
AWS October Webinar Series - Using Spot Instances to Save up to 90% off Your ...Amazon Web Services
Amazon EC2 allows you to bid for and run spare EC2 capacity, known as Spot instances, in a dynamically priced market. On average, customers save 80% to 90% compared to On Demand prices by using Spot instances. Achieving these savings has historically required time and effort to find the best deals while managing compute capacity as supply and demand fluctuate.
In this webinar, we dive into best practices and new features that will help you realize immediate cost savings, maximize compute capacity within your budget, and maintain application availability and performance with less up-front or ongoing development effort. Attendees leave with practical knowledge of Spot bidding strategies, market trends, instance selection and benchmarking, and fault-tolerant architecture with examples taken from common Spot use cases such as web services, big data/analytics, media processing, and continuous integration workloads.
Similar to 개발자가 알아두면 좋을 5가지 AWS 인공 지능 깨알 지식 - 윤석찬 (AWS 테크 에반젤리스트) (20)
사례로 알아보는 Database Migration Service : 데이터베이스 및 데이터 이관, 통합, 분리, 분석의 도구 - 발표자: ...Amazon Web Services Korea
Database Migration Service(DMS)는 RDBMS 이외에도 다양한 데이터베이스 이관을 지원합니다. 실제 고객사 사례를 통해 DMS가 데이터베이스 이관, 통합, 분리를 수행하는 데 어떻게 활용되는지 알아보고, 동시에 데이터 분석을 위한 데이터 수집(Data Ingest)에도 어떤 역할을 하는지 살펴보겠습니다.
Amazon Elasticache - Fully managed, Redis & Memcached Compatible Service (Lev...Amazon Web Services Korea
Amazon ElastiCache는 Redis 및 MemCached와 호환되는 완전관리형 서비스로서 현대적 애플리케이션의 성능을 최적의 비용으로 실시간으로 개선해 줍니다. ElastiCache의 Best Practice를 통해 최적의 성능과 서비스 최적화 방법에 대해 알아봅니다.
Internal Architecture of Amazon Aurora (Level 400) - 발표자: 정달영, APAC RDS Speci...Amazon Web Services Korea
ccAmazon Aurora 데이터베이스는 클라우드용으로 구축된 관계형 데이터베이스입니다. Aurora는 상용 데이터베이스의 성능과 가용성, 그리고 오픈소스 데이터베이스의 단순성과 비용 효율성을 모두 제공합니다. 이 세션은 Aurora의 고급 사용자들을 위한 세션으로써 Aurora의 내부 구조와 성능 최적화에 대해 알아봅니다.
[Keynote] 슬기로운 AWS 데이터베이스 선택하기 - 발표자: 강민석, Korea Database SA Manager, WWSO, A...Amazon Web Services Korea
오랫동안 관계형 데이터베이스가 가장 많이 사용되었으며 거의 모든 애플리케이션에서 널리 사용되었습니다. 따라서 애플리케이션 아키텍처에서 데이터베이스를 선택하기가 더 쉬웠지만, 구축할 수 있는 애플리케이션의 유형이 제한적이었습니다. 관계형 데이터베이스는 스위스 군용 칼과 같아서 많은 일을 할 수 있지만 특정 업무에는 완벽하게 적합하지는 않습니다. 클라우드 컴퓨팅의 등장으로 경제적인 방식으로 더욱 탄력적이고 확장 가능한 애플리케이션을 구축할 수 있게 되면서 기술적으로 가능한 일이 달라졌습니다. 이러한 변화는 전용 데이터베이스의 부상으로 이어졌습니다. 개발자는 더 이상 기본 관계형 데이터베이스를 사용할 필요가 없습니다. 개발자는 애플리케이션의 요구 사항을 신중하게 고려하고 이러한 요구 사항에 맞는 데이터베이스를 선택할 수 있습니다.
Demystify Streaming on AWS - 발표자: 이종혁, Sr Analytics Specialist, WWSO, AWS :::...Amazon Web Services Korea
실시간 분석은 AWS 고객의 사용 사례가 점점 늘어나고 있습니다. 이 세션에 참여하여 스트리밍 데이터 기술이 어떻게 데이터를 즉시 분석하고, 시스템 간에 데이터를 실시간으로 이동하고, 실행 가능한 통찰력을 더 빠르게 얻을 수 있는지 알아보십시오. 일반적인 스트리밍 데이터 사용 사례, 비즈니스에서 실시간 분석을 쉽게 활성화하는 단계, AWS가 Amazon Kinesis와 같은 AWS 스트리밍 데이터 서비스를 사용하도록 지원하는 방법을 다룹니다.
Amazon EMR - Enhancements on Cost/Performance, Serverless - 발표자: 김기영, Sr Anal...Amazon Web Services Korea
Amazon EMR은 Apache Spark, Hive, Presto, Trino, HBase 및 Flink와 같은 오픈 소스 프레임워크를 사용하여 분석 애플리케이션을 쉽게 실행할 수 있는 관리형 서비스를 제공합니다. Spark 및 Presto용 Amazon EMR 런타임에는 오픈 소스 Apache Spark 및 Presto에 비해 두 배 이상의 성능 향상을 제공하는 최적화 기능이 포함되어 있습니다. Amazon EMR Serverless는 Amazon EMR의 새로운 배포 옵션이지만 데이터 엔지니어와 분석가는 클라우드에서 페타바이트 규모의 데이터 분석을 쉽고 비용 효율적으로 실행할 수 있습니다. 이 세션에 참여하여 개념, 설계 패턴, 라이브 데모를 사용하여 Amazon EMR/EMR 서버리스를 살펴보고 Spark 및 Hive 워크로드, Amazon EMR 스튜디오 및 Amazon SageMaker Studio와의 Amazon EMR 통합을 실행하는 것이 얼마나 쉬운지 알아보십시오.
Amazon OpenSearch - Use Cases, Security/Observability, Serverless and Enhance...Amazon Web Services Korea
로그 및 지표 데이터를 쉽게 가져오고, OpenSearch 검색 API를 사용하고, OpenSearch 대시보드를 사용하여 시각화를 구축하는 등 Amazon OpenSearch의 새로운 기능과 기능에 대해 자세히 알아보십시오. 애플리케이션 문제를 디버깅할 수 있는 OpenSearch의 Observability 기능에 대해 알아보세요. Amazon OpenSearch Service를 통해 인프라 관리에 대해 걱정하지 않고 검색 또는 모니터링 문제에 집중할 수 있는 방법을 알아보십시오.
Enabling Agility with Data Governance - 발표자: 김성연, Analytics Specialist, WWSO,...Amazon Web Services Korea
데이터 거버넌스는 전체 프로세스에서 데이터를 관리하여 데이터의 정확성과 완전성을 보장하고 필요한 사람들이 데이터에 액세스할 수 있도록 하는 프로세스입니다. 이 세션에 참여하여 AWS가 어떻게 분석 서비스 전반에서 데이터 준비 및 통합부터 데이터 액세스, 데이터 품질 및 메타데이터 관리에 이르기까지 포괄적인 데이터 거버넌스를 제공하는지 알아보십시오. AWS에서의 스트리밍에 대해 자세히 알아보십시오.
Amazon Redshift Deep Dive - Serverless, Streaming, ML, Auto Copy (New feature...Amazon Web Services Korea
이 세션에 참여하여 Amazon Redshift의 새로운 기능을 자세히 살펴보십시오. Amazon Data Sharing, Amazon Redshift Serverless, Redshift Streaming, Redshift ML 및 자동 복사 등에 대한 자세한 내용과 데모를 통해 Amazon Redshift의 새로운 기능을 알고 싶은 사용자에게 적합합니다.
From Insights to Action, How to build and maintain a Data Driven Organization...Amazon Web Services Korea
데이터는 혁신과 변혁의 토대입니다. 비즈니스 혁신을 이끄는 혁신은 특정 시점의 전략이나 솔루션이 아니라 성장을 위한 반복적이고 집단적인 계획입니다. 혁신에 이러한 접근 방식을 채택하는 기업은 전략과 비즈니스 문화에서 데이터를 기반으로 하는 경우가 많습니다. 이러한 접근 방식을 개발하려면 리더가 데이터를 조직의 자산처럼 취급하고 조직이 더 나은 비즈니스 성과를 위해 데이터를 활용할 수 있도록 권한을 부여해야 합니다. AWS와 Amazon이 어떻게 데이터와 분석을 활용하여 확장 가능한 비즈니스 효율성을 창출하고 고객의 가장 복잡한 문제를 해결하는 메커니즘을 개발했는지 알아보십시오.
[Keynote] Accelerating Business Outcomes with AWS Data - 발표자: Saeed Gharadagh...Amazon Web Services Korea
데이터는 최종 소비자의 성공에 초점을 맞춘 디지털 혁신에서 중추적인 역할을 하고 있습니다. 모든 기업들은 데이터를 자산으로 사용하여 사례 제공을 추진하고 까다로운 결과를 해결하고 있습니다. AWS 클라우드 기술과 분석 솔루션의 강력한 성능을 통해 고객은 혁신 여정을 가속화할 수 있습니다. 이 세션에서는 기업 고객들이 클라우드에서 데이터의 힘을 활용하여 혁신 목표를 달성하고 필요한 결과를 제공하는 방법에 대해 다룹니다.
LG전자 - Amazon Aurora 및 RDS 블루/그린 배포를 이용한 데이터베이스 업그레이드 안정성 확보 - 발표자: 이은경 책임, L...Amazon Web Services Korea
LG ThinQ는 LG전자의 가전제품과 서비스를 아우르는 플랫폼 브랜드로서 앱 하나로 간편한 컨트롤, 똑똑한 케어, 스마트한 쇼핑까지 한번에 가능한 플랫폼입니다. ThinQ 플랫폼은 글로벌 서비스로 제공되고 있어, 작업 시간을 최소화하고, 서비스의 영향을 최소화 할 필요가 있었습니다. 따라서 DB 버전 업그레이드 작업 시 애플리케이션 배포가 필요없는 Blue/Green Deployment 방식은 최선의 선택이 되었습니다.
KB국민카드 - 클라우드 기반 분석 플랫폼 혁신 여정 - 발표자: 박창용 과장, 데이터전략본부, AI혁신부, KB카드│강병억, Soluti...Amazon Web Services Korea
온프레미스 분석 플랫폼에는 자원 증설 비용, 자원 관리 비용, 신규 자원 도입 및 환경 설정의 리드타임 등 다양한 측면에서의 한계가 존재합니다. 이에 KB국민카드에서는 기존 분석 플랫폼의 한계를 극복함과 동시에 시너지를 낼 수 있는 클라우드 기반 분석 플랫폼을 설계 및 도입하였습니다. 본 사례 소개는 KB국민카드의 데이터 혁신 여정과 노하우를 소개합니다.
SK Telecom - 망관리 프로젝트 TANGO의 오픈소스 데이터베이스 전환 여정 - 발표자 : 박승전, Project Manager, ...Amazon Web Services Korea
SK Telecom의 망관리 프로젝트인 TANGO에서는 오라클을 기반으로 시스템을 구축하여 운영해 왔습니다. 하지만 늘어나는 사용자와 데이터로 인해 유연하고 비용 효율적인 인프라가 필요하게 되었고, 이에 클라우드 도입을 검토 및 실행에 옮기게 되었습니다. TANGO 프로젝트의 클라우드 도입을 위한 검토부터 준비, 실행 및 이를 통해 얻게 된 교훈과 향후 계획에 대해 소개합니다.
코리안리 - 데이터 분석 플랫폼 구축 여정, 그 시작과 과제 - 발표자: 김석기 그룹장, 데이터비즈니스센터, 메가존클라우드 ::: AWS ...Amazon Web Services Korea
2022년 코리안리는 핵심업무시스템(기간계/정보계 시스템)을 AWS 클라우드로 전환하는 사업과 AWS 클라우드 기반에서 손익분석을 위한 어플리케이션 구축 사업을 동시에 진행하고 있었습니다. 이에 따라 클라우드 전환 이후 시스템 간 상호운용성과 호환성을갖춘 데이터 분석 플랫폼 또한 필요하게 되었습니다. 코리안리 IT 환경에 적합한 플랫폼 선정을 위하여 AWS Native Analytics Platform, 3rd Party Analytics Platform (클라우데라, 데이터브릭스)과의 PoC를 진행하고, 최종적으로 AWS Native Analytics Platform 으로 확정하였습니다. 코리안리는 메가존클라우드와 함께 2022년 10월부터 4개월(구축 3개월, 안정화 및 교육 1개월) 동안 AWS 기반 데이터 분석 플랫폼을 구축하고 활용 범위를 지속적으로 확대하고 있습니다.
LG 이노텍 - Amazon Redshift Serverless를 활용한 데이터 분석 플랫폼 혁신 과정 - 발표자: 유재상 선임, LG이노...Amazon Web Services Korea
LG 이노텍은 세계 시장을 선도하는 글로벌 소재·부품기업으로, Amazon Redshift 을 데이터 분석 플랫폼의 핵심 서비스로 활용하고 있습니다.지속적인 데이터 증가와 업무 확대에 따른 유연한 아키텍처 개선의 필요성에 대처하기 위해, 2022년에 AWS 에서 발표된 Redshift Serverless 를 활용한, 비용 최적화된 아키텍처 개선 과정의 실사례를 엿볼수 있는 기회가 됩니다.
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
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.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
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:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
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.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
Communications Mining Series - Zero to Hero - Session 1DianaGray10
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
44. .
Live Street Camera Amazon Kinesis Video Streams
1. Camera-captured video
streams are processed by
Kinesis Video Streams
End User
3. End user is notified
in case of face matches
Amazon SNS AWS Lambda Amazon Kinesis
Streams
Amazon Rekognition Video Face collection
2. Rekognition Video analyses the
video and searches faces on screen
against a collection of millions of faces
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GuardDuty
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Data Sources
VPC flow logs
DNS Logs
CloudTrail
Events
Amazon
CloudWatch
rules
Amazon
GuardDuty
AWS
StepFunctions
Lambda
function
End UserAmazon SNS
3. End user is notified in case of risk
Lambda
function
EC2 Systems
Manager
EC2
2. EC2 System Manager fixes compromised EC2
Instances and credentials by documents
1. Guardduty continuously analyzes
data sources and intelligently detect
threats and sends CloudWatch Logs
54. KERAS
AWS DEEP LEARNING AMI
AMAZON SAGEMAKER
REKOGNITION REKOGNITION VIDEO POLLY TRANSCRIBE TRANSLATE COMPREHEND LEX
AWS DEEPLENS AMAZON MACHINE
LEARNING
SPARK & EMR AMAZON
MECHANICAL TURK
GPU ( P3 INSTANCES) CPU (C5) IOT (GREENGRASS) MOBILE
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