Deep Learning continua a impulsionar o estado da arte em domínios como visão computacional, linguagem natural e mecanismos de recomendação. Nesta sessão, você irá aprender como começar a usar o framework de Deep Learning TensorFlow usando o Amazon SageMaker, uma plataforma para criar, treinar e implantar facilmente modelos em escala. Você aprende como criar um modelo usando o TensorFlow configurando um Notebook Jupyter para começar a efetuar reconhecimento de imagem e objeto. Você também aprende como treinar e implantar rapidamente um modelo por meio do Amazon SageMaker.
Deep Learning para Análise de Video e Imagem e Sintetização de Voz - MCL303 ...Amazon Web Services
Saiba como o Amazon Rekognition está usando análise de imagem e video baseado em deep learning para identificação de objetos, pessoas, textos, cenas bem como deteção de conteúdo inapropriado fornecendo alta acurácia em análise facial e reconhecimento de video e imagem. Nesta sessão, também mostraremos como integrar o Amazon Rekognition com Amazon Polly que é um serviço que transforma texto em falas realistas permitindo a crição de aplicações de voz e a construção de uma nova categoria de produtos habilitados por voz. Durante a apresentação, demonstraremos arquiteturas que endereçam casos de uso como segurança pública e midia digital.
O caso de negócios da computação em nuvem vai além do custo total de propriedade (TCO). A AWS ajuda as organizações a reduzir seu tempo de lançamento de um produto no mercado e o tempo gasto em trabalhos indiferenciados, bem como a melhorar a disponibilidade da aplicação. Nesta aula, você aprenderá mais sobre o AWS Cloud Value Framework. Esse modelo quantifica não apenas a economia com o TCO, mas também o valor de negócio da agilidade, redução de riscos e eficiência, elementos fundamentais para defender qualquer proposta de mudança. Após essa sessão, você será capaz de descrever os benefícios da nuvem para diferentes partes de sua organização e apresentar um caso de negócios abrangente.
Executando Kubernetes com Amazon EKS - DEV303 - Sao Paulo SummitAmazon Web Services
O Kubernetes oferece uma poderosa camada de abstração para gerenciar a infraestrutura conteinerizada. O Amazon Elastic Container Service for Kubernetes (Amazon EKS) facilita a execução do Kubernetes na AWS sem ter que gerenciar os nós principais ou o operador do etcd. Nesta sessão, abordamos como o Amazon EKS torna a implementação do Kubernetes na AWS simples e escalável, incluindo rede, segurança, monitoramento e registro. Discutiremos as principais contribuições que estamos dando para que a AWS seja um lugar ainda melhor para executar o Kubernetes e demonstraremos como os clientes da AWS estão começando a usar o Amazon EKS.
Machine Learning e Amazon SageMaker: Algoritmos, Modelos e Inferências - MCL...Amazon Web Services
Atualmente, as organizações estão usando machine learning (ML) para endereçar uma série de desafios nos negócios, desde recomenções de produtos e previsão de preços, até o rastreamento da progressão de doença e previsão de demanda. Até recentemente, desenvolver esses modelos de ML demorava um período significante de tempo e esforços, e exigia especialização nesse campo. Nesta sessão, apresentaremos o Amazon SageMaker, um seviço ML totalmente gerenciado que permite desenvolvedores e cientistas de dados desenvolver e implementar modelos de aprendizagem profunda com mais rapidez e facilidade. Analisaremos os recursos e os benefícios do Amazon SageMaker e discutiremos os algoritmos ML exclusivamente projetados que permitem treinamento otimizado do modelo, para levar você à rápida produtividade.
Computação de Alta Performance (HPC) na AWS - CMP201 - Sao Paulo SummitAmazon Web Services
A computação de alta performance (HPC) na nuvem permite alta escala computacional e intensivos gráficos de cargas de trabalho em diversos setores, incluindo aeroespacial, manufatura, ciências da vida, serviços financeiros e energia. A AWS fornece aos desenvolvedores de aplicações e usuários um poder computacional sem precedentes para aplicações massivamente paralelas em áreas como fluído de grande escala e simulação de materiais, renderização de conteúdo 3D, computação financeira e deep learning. Nesta sessão, fornecemos uma visão geral dos recursos de HPC na AWS. Descrevemos a mais nova geração de instâncias de computação de uso geral e aceleradas, destacamos casos de uso de clientes e parceiros em todos os setores e discutimos casos de uso de HPC novos e emergentes. Os participantes aprenderão as melhores práticas para executar fluxos de trabalho de HPC na nuvem, incluindo automação e otimização de fluxo de trabalho.
The document discusses artificial intelligence, machine learning, and deep learning. It provides definitions and brief histories of AI, machine learning, and deep learning. It also discusses key technologies like Amazon Rekognition, Amazon SageMaker, GPU instances on AWS, and the AWS Academy program for education. Trends in AI like computer vision, natural language processing, robotics, and personalized recommendations are presented through examples. The document emphasizes that AWS provides a broad set of AI and machine learning services and capabilities.
by John Pignata, Startup Solutions Architect, AWS
AWS Lambda and Amazon API Gateway have changed how developers build and run their applications or services. But what are the best practices for tasks such as deployment, monitoring, and debugging in a serverless world? In this session, we’ll dive into best practices that serverless developers can use for application lifecycle management, CI/CD, monitoring, and diagnostics. We’ll talk about how you can build CI/CD pipelines that automatically build, test, and deploy your serverless applications using AWS CodePipeline, AWS CodeBuild, and AWS CloudFormation. We’ll also cover the built-in capabilities of Lambda and API Gateway for creating multiple versions, stages, and environments of your functions and APIs. Finally, we’ll cover monitoring and diagnostics of your Lambda functions with Amazon CloudWatch and AWS X-Ray.
Planificación de arquitecturas de red de AWS - MXO211 - Mexico City SummitAmazon Web Services
Amazon VPC es un servicio que te ayuda a tener control total sobre tus recursos de red en AWS. Con este control ¿te has preguntado cómo es que las nuevas capacidades liberadas afectan la forma en la que diseñaste tu arquitectura de red o cómo cambiar tus arquitecturas existentes? En esta sesión compartiremos ejemplos reales sobre cómo utilizar Amazon VPC para crear arquitecturas de nube híbridas, conectando tus centros de datos locales con AWS. También exploraremos las capacidades recién agregadas a Amazon VPC y cómo puedes utilizarlas.
Deep Learning para Análise de Video e Imagem e Sintetização de Voz - MCL303 ...Amazon Web Services
Saiba como o Amazon Rekognition está usando análise de imagem e video baseado em deep learning para identificação de objetos, pessoas, textos, cenas bem como deteção de conteúdo inapropriado fornecendo alta acurácia em análise facial e reconhecimento de video e imagem. Nesta sessão, também mostraremos como integrar o Amazon Rekognition com Amazon Polly que é um serviço que transforma texto em falas realistas permitindo a crição de aplicações de voz e a construção de uma nova categoria de produtos habilitados por voz. Durante a apresentação, demonstraremos arquiteturas que endereçam casos de uso como segurança pública e midia digital.
O caso de negócios da computação em nuvem vai além do custo total de propriedade (TCO). A AWS ajuda as organizações a reduzir seu tempo de lançamento de um produto no mercado e o tempo gasto em trabalhos indiferenciados, bem como a melhorar a disponibilidade da aplicação. Nesta aula, você aprenderá mais sobre o AWS Cloud Value Framework. Esse modelo quantifica não apenas a economia com o TCO, mas também o valor de negócio da agilidade, redução de riscos e eficiência, elementos fundamentais para defender qualquer proposta de mudança. Após essa sessão, você será capaz de descrever os benefícios da nuvem para diferentes partes de sua organização e apresentar um caso de negócios abrangente.
Executando Kubernetes com Amazon EKS - DEV303 - Sao Paulo SummitAmazon Web Services
O Kubernetes oferece uma poderosa camada de abstração para gerenciar a infraestrutura conteinerizada. O Amazon Elastic Container Service for Kubernetes (Amazon EKS) facilita a execução do Kubernetes na AWS sem ter que gerenciar os nós principais ou o operador do etcd. Nesta sessão, abordamos como o Amazon EKS torna a implementação do Kubernetes na AWS simples e escalável, incluindo rede, segurança, monitoramento e registro. Discutiremos as principais contribuições que estamos dando para que a AWS seja um lugar ainda melhor para executar o Kubernetes e demonstraremos como os clientes da AWS estão começando a usar o Amazon EKS.
Machine Learning e Amazon SageMaker: Algoritmos, Modelos e Inferências - MCL...Amazon Web Services
Atualmente, as organizações estão usando machine learning (ML) para endereçar uma série de desafios nos negócios, desde recomenções de produtos e previsão de preços, até o rastreamento da progressão de doença e previsão de demanda. Até recentemente, desenvolver esses modelos de ML demorava um período significante de tempo e esforços, e exigia especialização nesse campo. Nesta sessão, apresentaremos o Amazon SageMaker, um seviço ML totalmente gerenciado que permite desenvolvedores e cientistas de dados desenvolver e implementar modelos de aprendizagem profunda com mais rapidez e facilidade. Analisaremos os recursos e os benefícios do Amazon SageMaker e discutiremos os algoritmos ML exclusivamente projetados que permitem treinamento otimizado do modelo, para levar você à rápida produtividade.
Computação de Alta Performance (HPC) na AWS - CMP201 - Sao Paulo SummitAmazon Web Services
A computação de alta performance (HPC) na nuvem permite alta escala computacional e intensivos gráficos de cargas de trabalho em diversos setores, incluindo aeroespacial, manufatura, ciências da vida, serviços financeiros e energia. A AWS fornece aos desenvolvedores de aplicações e usuários um poder computacional sem precedentes para aplicações massivamente paralelas em áreas como fluído de grande escala e simulação de materiais, renderização de conteúdo 3D, computação financeira e deep learning. Nesta sessão, fornecemos uma visão geral dos recursos de HPC na AWS. Descrevemos a mais nova geração de instâncias de computação de uso geral e aceleradas, destacamos casos de uso de clientes e parceiros em todos os setores e discutimos casos de uso de HPC novos e emergentes. Os participantes aprenderão as melhores práticas para executar fluxos de trabalho de HPC na nuvem, incluindo automação e otimização de fluxo de trabalho.
The document discusses artificial intelligence, machine learning, and deep learning. It provides definitions and brief histories of AI, machine learning, and deep learning. It also discusses key technologies like Amazon Rekognition, Amazon SageMaker, GPU instances on AWS, and the AWS Academy program for education. Trends in AI like computer vision, natural language processing, robotics, and personalized recommendations are presented through examples. The document emphasizes that AWS provides a broad set of AI and machine learning services and capabilities.
by John Pignata, Startup Solutions Architect, AWS
AWS Lambda and Amazon API Gateway have changed how developers build and run their applications or services. But what are the best practices for tasks such as deployment, monitoring, and debugging in a serverless world? In this session, we’ll dive into best practices that serverless developers can use for application lifecycle management, CI/CD, monitoring, and diagnostics. We’ll talk about how you can build CI/CD pipelines that automatically build, test, and deploy your serverless applications using AWS CodePipeline, AWS CodeBuild, and AWS CloudFormation. We’ll also cover the built-in capabilities of Lambda and API Gateway for creating multiple versions, stages, and environments of your functions and APIs. Finally, we’ll cover monitoring and diagnostics of your Lambda functions with Amazon CloudWatch and AWS X-Ray.
Planificación de arquitecturas de red de AWS - MXO211 - Mexico City SummitAmazon Web Services
Amazon VPC es un servicio que te ayuda a tener control total sobre tus recursos de red en AWS. Con este control ¿te has preguntado cómo es que las nuevas capacidades liberadas afectan la forma en la que diseñaste tu arquitectura de red o cómo cambiar tus arquitecturas existentes? En esta sesión compartiremos ejemplos reales sobre cómo utilizar Amazon VPC para crear arquitecturas de nube híbridas, conectando tus centros de datos locales con AWS. También exploraremos las capacidades recién agregadas a Amazon VPC y cómo puedes utilizarlas.
Building Massively Parallel Event-Driven Architectures (SRV373-R1) - AWS re:I...Amazon Web Services
Data and events are the lifeblood of any modern application. By using stateless, loosely coupled microservices communicating through events, developers can build massively scalable systems that can process trillions of requests in seconds. In this talk, we cover design patterns for using Amazon SQS, Amazon SNS, AWS Step Functions, AWS Lambda, and Amazon S3 to build data processing and real-time notification systems with unbounded scale and serverless cost characteristics. We also explore how these approaches apply to practical use cases, such as training machine learning models, media processing, and data cleansing.
The document discusses different authentication methods for serverless applications on AWS, including Amazon Cognito User Pools, IAM authentication, and custom authorizers. It provides diagrams and explanations of how each method works, with Amazon Cognito User Pools involving authenticating through Cognito and getting JSON Web Tokens to call APIs, IAM authentication using temporary security credentials to access AWS resources, and custom authorizers using a Lambda function to validate tokens and policies.
AWS Lambda를 통한 Tensorflow 및 Keras 기반 추론 모델 서비스하기 :: 이준범 :: AWS Summit Seoul 2018Amazon Web Services Korea
This document discusses building and deploying machine learning models using Amazon Web Services (AWS) Lambda and containers. It describes downloading dependencies from S3, building a Lambda deployment package in a Docker container, and updating a Lambda function to use the new package. Code snippets show setting up a Python virtual environment, installing TensorFlow and other libraries, zipping the environment contents into a deployment package, and uploading/deploying the package to Lambda via the AWS API.
Launch Applications the Amazon Way: AWS Startup Day - New York 2018Amazon Web Services
Launch Applications the Amazon Way: John Pignata, AWS We'll show you how to take your application and launch it quickly on a variety of AWS infrastructure. You'll learn how to leverage CodeStar, CodeBuild, CodeDeploy, and Cloud9 to provide your startup with reliable, flexible, and cost efficient build pipelines in minutes. This will set your technical teams up for faster deploys and consistent development environments allowing you to focus on your product, not your deployment process. This is a key pain point for early stage startups, learn how to solve it before it starts to impact your team's productivity.
AWS Dev Lounge: Applying the Twelve-Factor Application Manifesto to Developin...Amazon Web Services
Twelve Factor applications were popularised by developers building large scale software-as-a-service applications on platforms such as Heroku. In recent years, the 12 Factor Manifesto has been considered a source of best practices for both developers and operations engineers regardless of the application’s use-case and at nearly any scale.
In this Dev Lounge session we will take a look at:
How many of the 12 Factor guidelines align directly with best practices for serverless applications
How to address those 12 Factor guidelines that don’t directly align or are interpreted very differently
Implementation examples using AWS Lambda, Amazon API Gateway, and the AWS Code services as well as the Serverless Application Model (SAM) and associated tooling
The document discusses Amazon Web Services (AWS) machine learning capabilities. It provides an overview of the AWS ML stack, which offers the broadest and most complete set of machine learning capabilities across vision, speech, text, search, chatbots, personalization, forecasting, fraud detection, and more. It also discusses several specific AWS machine learning services, including Amazon Rekognition (image and video analysis), Amazon Fraud Detector (online fraud detection), Amazon Kendra (enterprise search), Amazon CodeGuru (automated code reviews and profiling), and Contact Lens for Amazon Connect (contact center analytics).
Building Well Architected .NET Apps (WIN304) - AWS re:Invent 2018Amazon Web Services
The AWS Well-Architected Framework was developed to help cloud architects build secure, high-performing, resilient, and efficient infrastructure for their applications. This framework provides a consistent approach for customers and partners to evaluate architectures, and it provides guidance to help implement designs that scales with your application needs over time. In this session, we cover how to build a .NET application using the AWS Well-Architected Framework.
The document discusses the history and evolution of POOQ, a Korean video streaming service owned jointly by KBS, MBC, and SBS. It describes POOQ moving from a monolithic architecture hosted on-premises (POOQ 1.0) to a scalable cloud-native architecture using local cloud infrastructure (POOQ 2.0). It then discusses POOQ building its own media processing and streaming head end using AWS Elemental and other AWS services (Head End). The presentation concludes with an overview of POOQ's plans to transition to a microservices architecture using Docker, Kubernetes, and other open source tools on AWS (POOQ 3.0).
This document discusses building an image classification model using Amazon SageMaker and deploying it via an API Gateway and Lambda function. Key steps include:
1. Developing a Python Lambda function to preprocess images from API requests and call a SageMaker endpoint for predictions.
2. Creating an IAM role for the Lambda function to access SageMaker.
3. Building and deploying the SageMaker model.
4. Configuring API Gateway to invoke the Lambda function via a REST API, with the Lambda function returning classification results.
This allows building and deploying a machine learning model on SageMaker and serving predictions through a serverless API for image classification.
[REPEAT] Iterating Towards a Cloud-Enabled IT Organization (ENT204-R) - AWS r...Amazon Web Services
Transforming your organization and its people to become cloud-natives can be overwhelming. Platform teams, operations teams, development teams, and even their leaders have nontechnical challenges to consider and overcome to unlock the maximum value of running their businesses on AWS. In this chalk talk, learn how to combine Amazonian ways of working, organizing, and enabling to kickstart your cloud journey with a cloud foundation team and a small number of “two-pizza application teams.”, Also learn how to iteratively scale the concepts used to build these initial teams into a fully cloud-enabled IT organization.
[AWS Container Service] Getting Started with Cloud Map, App Mesh and FirecrackerAmazon Web Services Korea
This document provides an overview and summary of Amazon Web Services (AWS) announcements from a conference in Seoul, South Korea. It includes summaries of new and updated AWS services across various categories such as compute, database, analytics, developer tools, and containers. Key announcements include the general availability of AWS App Mesh for managing communications between microservices applications and the public beta of AWS Cloud Map for service discovery.
BDA308 Deep Dive: Log Analytics with Amazon Elasticsearch ServiceAmazon Web Services
Amazon Elasticsearch Service makes it easy to deploy, secure, operate, and scale Elasticsearch for log analytics, full text search, application monitoring, and more. In this session you learn how to configure a secure, petabyte-scale Amazon Elasticsearch Service cluster and build Kibana dashboards to analyze your data. In addition, we discuss best practices to make your cluster reliable, take backups, and debug slow-running queries and indexing operations.
The document discusses serverless application deployment with AWS SAM (Serverless Application Model). It introduces SAM as an extension to AWS CloudFormation that allows defining serverless resources like Lambda functions, APIs, and DynamoDB tables through templates. It describes using SAM templates to define functions, APIs, and event sources. It also covers deploying SAM applications through packaging and deploying the templates to create the necessary serverless infrastructure.
[AWS Dev Day] 앱 현대화 | DevOps 개발자가 되기 위한 쿠버네티스 핵심 활용 예제 알아보기 - 정영준 AWS 솔루션즈 아키...Amazon Web Services Korea
쿠버네티스에 어플리케이션을 손쉽게 배포하는 방법은 무엇일까요? 복잡하게 배포된 어플리케이션의 파드들은 어떻게 디버깅하고 로깅해야 할까요? 또한 요즘 자주 이야기 되는 클라우드 네이티브 아키텍처로 설계된 어플리케이션은 어떻게 만들고 배포해야하는 걸까요?삼성전자 무선사업부에서 삼성헬스를 EKS 에 배포한 사례를 살펴보며, 이러한 문제를 어떻게 해결했는지 알아봅니다. 또한 복잡하게만 느껴졌던 쿠버네티스의 어플리케이션 배포와 클라우드 네이티브 아키텍처의 베스트 프렉티스를 EKS 에 어플리케이션을 배포하고, 관리하는 예제를 통하여 간편하게 이해할 수 있게 도와드립니다.
[AWS Innovate 온라인 컨퍼런스] Kubernetes와 SageMaker를 활용하여 Machine Learning 워크로드 관리하...Amazon Web Services Korea
발표자료 다시보기: https://youtu.be/6sogVHw9jZ4
Machine Learning 워크로드를 실제 운영환경에서 사용하기 위하여 다양한 툴들과 방법들이 시도되고 있습니다. 본 세션에서는 ML 운영을 위해 어떤 툴들이 활용되고 있는지를 살펴보고, 그 중 엔터프라이즈 환경에서 많이 선택하고 았는 Kubernetes와 Kubeflow를 사용하여, 어떻게 Machine Learning 전처리와 Training 작업을 관리하고 운영환경에 배포할 수 있는지를 데모와 함께 알아봅니다.
This document discusses Amazon EKS (Elastic Kubernetes Service) and effective cloud native design. It includes diagrams showing the EKS architecture with managed Kubernetes control planes and worker nodes running on AWS. It covers key EKS features like native VPC networking, IAM authentication, load balancing ingress controllers, persistent storage options, cluster auto scaling, and logging integration with CloudWatch Logs. The document also provides examples of using the AWS CLI to create, describe, and delete EKS clusters as well as provisioning clusters using CloudFormation or Terraform.
Best Practices for CI/CD with AWS Lambda and Amazon API Gateway (SRV355-R1) -...Amazon Web Services
Building and deploying serverless applications introduces new challenges for developers whose development workflows are optimized for traditional VM-based applications. In this session, we discuss a method for automating the deployment of serverless applications running on AWS Lambda. First, we cover how you can model and express serverless applications using the open source AWS Serverless Application Model (AWS SAM). Then, we discuss how you can use CI/CD tooling from AWS CodePipeline and AWS CodeBuild, and how to bootstrap the entire toolset using AWS CodeStar. We also cover best practices to embed in your deployment workflow specific to serverless applications.
Amazon Elastic Fabric Adapter: Anatomy, Capabilities, and the Road Aheadinside-BigData.com
This document summarizes a presentation about Amazon's Elastic Fabric Adapter (EFA). The presentation covered an overview of high performance computing on AWS, a deep dive on what EFA is and how it works, and the road ahead for further developing EFA. EFA provides high-speed networking for HPC workloads running on EC2 instances by exposing a reliable datagram interface. It uses a new protocol called Scalable Reliable Datagram and delivers low latency and high bandwidth. The presenter discussed how EFA is currently in preview and outlined plans to continue working with the open source community to upstream the EFA kernel module and integrate with libfabric.
Accelerate Machine Learning with Ease using Amazon SageMakerAmazon Web Services
Organizations are using machine learning (ML) to address a host of business challenges, from product recommendations to demand forecasting. Until recently, developing these ML models took much time and effort, and it required expertise. In this session, we introduce Amazon SageMaker, a fully managed ML service that enables developers and data scientists to develop and deploy deep learning models quickly and easily. We walk through the features and benefits of Amazon SageMaker and discuss the uniquely designed ML algorithms that allow for optimized model training, getting you to production fast.
Accelerate Machine Learning with Ease Using Amazon SageMaker - BDA301 - Chica...Amazon Web Services
Organizations are using machine learning (ML) to address a host of business challenges, from product recommendations to demand forecasting. Until recently, developing these ML models took much time and effort, and it required expertise. In this session, we discuss and dive deep into Amazon SageMaker, a fully managed ML service that enables developers and data scientists to develop and deploy deep learning models quickly and easily. We walk through the features and benefits of Amazon SageMaker and discuss the uniquely designed ML algorithms that allow for optimized model training, getting you to production fast.
Building Massively Parallel Event-Driven Architectures (SRV373-R1) - AWS re:I...Amazon Web Services
Data and events are the lifeblood of any modern application. By using stateless, loosely coupled microservices communicating through events, developers can build massively scalable systems that can process trillions of requests in seconds. In this talk, we cover design patterns for using Amazon SQS, Amazon SNS, AWS Step Functions, AWS Lambda, and Amazon S3 to build data processing and real-time notification systems with unbounded scale and serverless cost characteristics. We also explore how these approaches apply to practical use cases, such as training machine learning models, media processing, and data cleansing.
The document discusses different authentication methods for serverless applications on AWS, including Amazon Cognito User Pools, IAM authentication, and custom authorizers. It provides diagrams and explanations of how each method works, with Amazon Cognito User Pools involving authenticating through Cognito and getting JSON Web Tokens to call APIs, IAM authentication using temporary security credentials to access AWS resources, and custom authorizers using a Lambda function to validate tokens and policies.
AWS Lambda를 통한 Tensorflow 및 Keras 기반 추론 모델 서비스하기 :: 이준범 :: AWS Summit Seoul 2018Amazon Web Services Korea
This document discusses building and deploying machine learning models using Amazon Web Services (AWS) Lambda and containers. It describes downloading dependencies from S3, building a Lambda deployment package in a Docker container, and updating a Lambda function to use the new package. Code snippets show setting up a Python virtual environment, installing TensorFlow and other libraries, zipping the environment contents into a deployment package, and uploading/deploying the package to Lambda via the AWS API.
Launch Applications the Amazon Way: AWS Startup Day - New York 2018Amazon Web Services
Launch Applications the Amazon Way: John Pignata, AWS We'll show you how to take your application and launch it quickly on a variety of AWS infrastructure. You'll learn how to leverage CodeStar, CodeBuild, CodeDeploy, and Cloud9 to provide your startup with reliable, flexible, and cost efficient build pipelines in minutes. This will set your technical teams up for faster deploys and consistent development environments allowing you to focus on your product, not your deployment process. This is a key pain point for early stage startups, learn how to solve it before it starts to impact your team's productivity.
AWS Dev Lounge: Applying the Twelve-Factor Application Manifesto to Developin...Amazon Web Services
Twelve Factor applications were popularised by developers building large scale software-as-a-service applications on platforms such as Heroku. In recent years, the 12 Factor Manifesto has been considered a source of best practices for both developers and operations engineers regardless of the application’s use-case and at nearly any scale.
In this Dev Lounge session we will take a look at:
How many of the 12 Factor guidelines align directly with best practices for serverless applications
How to address those 12 Factor guidelines that don’t directly align or are interpreted very differently
Implementation examples using AWS Lambda, Amazon API Gateway, and the AWS Code services as well as the Serverless Application Model (SAM) and associated tooling
The document discusses Amazon Web Services (AWS) machine learning capabilities. It provides an overview of the AWS ML stack, which offers the broadest and most complete set of machine learning capabilities across vision, speech, text, search, chatbots, personalization, forecasting, fraud detection, and more. It also discusses several specific AWS machine learning services, including Amazon Rekognition (image and video analysis), Amazon Fraud Detector (online fraud detection), Amazon Kendra (enterprise search), Amazon CodeGuru (automated code reviews and profiling), and Contact Lens for Amazon Connect (contact center analytics).
Building Well Architected .NET Apps (WIN304) - AWS re:Invent 2018Amazon Web Services
The AWS Well-Architected Framework was developed to help cloud architects build secure, high-performing, resilient, and efficient infrastructure for their applications. This framework provides a consistent approach for customers and partners to evaluate architectures, and it provides guidance to help implement designs that scales with your application needs over time. In this session, we cover how to build a .NET application using the AWS Well-Architected Framework.
The document discusses the history and evolution of POOQ, a Korean video streaming service owned jointly by KBS, MBC, and SBS. It describes POOQ moving from a monolithic architecture hosted on-premises (POOQ 1.0) to a scalable cloud-native architecture using local cloud infrastructure (POOQ 2.0). It then discusses POOQ building its own media processing and streaming head end using AWS Elemental and other AWS services (Head End). The presentation concludes with an overview of POOQ's plans to transition to a microservices architecture using Docker, Kubernetes, and other open source tools on AWS (POOQ 3.0).
This document discusses building an image classification model using Amazon SageMaker and deploying it via an API Gateway and Lambda function. Key steps include:
1. Developing a Python Lambda function to preprocess images from API requests and call a SageMaker endpoint for predictions.
2. Creating an IAM role for the Lambda function to access SageMaker.
3. Building and deploying the SageMaker model.
4. Configuring API Gateway to invoke the Lambda function via a REST API, with the Lambda function returning classification results.
This allows building and deploying a machine learning model on SageMaker and serving predictions through a serverless API for image classification.
[REPEAT] Iterating Towards a Cloud-Enabled IT Organization (ENT204-R) - AWS r...Amazon Web Services
Transforming your organization and its people to become cloud-natives can be overwhelming. Platform teams, operations teams, development teams, and even their leaders have nontechnical challenges to consider and overcome to unlock the maximum value of running their businesses on AWS. In this chalk talk, learn how to combine Amazonian ways of working, organizing, and enabling to kickstart your cloud journey with a cloud foundation team and a small number of “two-pizza application teams.”, Also learn how to iteratively scale the concepts used to build these initial teams into a fully cloud-enabled IT organization.
[AWS Container Service] Getting Started with Cloud Map, App Mesh and FirecrackerAmazon Web Services Korea
This document provides an overview and summary of Amazon Web Services (AWS) announcements from a conference in Seoul, South Korea. It includes summaries of new and updated AWS services across various categories such as compute, database, analytics, developer tools, and containers. Key announcements include the general availability of AWS App Mesh for managing communications between microservices applications and the public beta of AWS Cloud Map for service discovery.
BDA308 Deep Dive: Log Analytics with Amazon Elasticsearch ServiceAmazon Web Services
Amazon Elasticsearch Service makes it easy to deploy, secure, operate, and scale Elasticsearch for log analytics, full text search, application monitoring, and more. In this session you learn how to configure a secure, petabyte-scale Amazon Elasticsearch Service cluster and build Kibana dashboards to analyze your data. In addition, we discuss best practices to make your cluster reliable, take backups, and debug slow-running queries and indexing operations.
The document discusses serverless application deployment with AWS SAM (Serverless Application Model). It introduces SAM as an extension to AWS CloudFormation that allows defining serverless resources like Lambda functions, APIs, and DynamoDB tables through templates. It describes using SAM templates to define functions, APIs, and event sources. It also covers deploying SAM applications through packaging and deploying the templates to create the necessary serverless infrastructure.
[AWS Dev Day] 앱 현대화 | DevOps 개발자가 되기 위한 쿠버네티스 핵심 활용 예제 알아보기 - 정영준 AWS 솔루션즈 아키...Amazon Web Services Korea
쿠버네티스에 어플리케이션을 손쉽게 배포하는 방법은 무엇일까요? 복잡하게 배포된 어플리케이션의 파드들은 어떻게 디버깅하고 로깅해야 할까요? 또한 요즘 자주 이야기 되는 클라우드 네이티브 아키텍처로 설계된 어플리케이션은 어떻게 만들고 배포해야하는 걸까요?삼성전자 무선사업부에서 삼성헬스를 EKS 에 배포한 사례를 살펴보며, 이러한 문제를 어떻게 해결했는지 알아봅니다. 또한 복잡하게만 느껴졌던 쿠버네티스의 어플리케이션 배포와 클라우드 네이티브 아키텍처의 베스트 프렉티스를 EKS 에 어플리케이션을 배포하고, 관리하는 예제를 통하여 간편하게 이해할 수 있게 도와드립니다.
[AWS Innovate 온라인 컨퍼런스] Kubernetes와 SageMaker를 활용하여 Machine Learning 워크로드 관리하...Amazon Web Services Korea
발표자료 다시보기: https://youtu.be/6sogVHw9jZ4
Machine Learning 워크로드를 실제 운영환경에서 사용하기 위하여 다양한 툴들과 방법들이 시도되고 있습니다. 본 세션에서는 ML 운영을 위해 어떤 툴들이 활용되고 있는지를 살펴보고, 그 중 엔터프라이즈 환경에서 많이 선택하고 았는 Kubernetes와 Kubeflow를 사용하여, 어떻게 Machine Learning 전처리와 Training 작업을 관리하고 운영환경에 배포할 수 있는지를 데모와 함께 알아봅니다.
This document discusses Amazon EKS (Elastic Kubernetes Service) and effective cloud native design. It includes diagrams showing the EKS architecture with managed Kubernetes control planes and worker nodes running on AWS. It covers key EKS features like native VPC networking, IAM authentication, load balancing ingress controllers, persistent storage options, cluster auto scaling, and logging integration with CloudWatch Logs. The document also provides examples of using the AWS CLI to create, describe, and delete EKS clusters as well as provisioning clusters using CloudFormation or Terraform.
Best Practices for CI/CD with AWS Lambda and Amazon API Gateway (SRV355-R1) -...Amazon Web Services
Building and deploying serverless applications introduces new challenges for developers whose development workflows are optimized for traditional VM-based applications. In this session, we discuss a method for automating the deployment of serverless applications running on AWS Lambda. First, we cover how you can model and express serverless applications using the open source AWS Serverless Application Model (AWS SAM). Then, we discuss how you can use CI/CD tooling from AWS CodePipeline and AWS CodeBuild, and how to bootstrap the entire toolset using AWS CodeStar. We also cover best practices to embed in your deployment workflow specific to serverless applications.
Amazon Elastic Fabric Adapter: Anatomy, Capabilities, and the Road Aheadinside-BigData.com
This document summarizes a presentation about Amazon's Elastic Fabric Adapter (EFA). The presentation covered an overview of high performance computing on AWS, a deep dive on what EFA is and how it works, and the road ahead for further developing EFA. EFA provides high-speed networking for HPC workloads running on EC2 instances by exposing a reliable datagram interface. It uses a new protocol called Scalable Reliable Datagram and delivers low latency and high bandwidth. The presenter discussed how EFA is currently in preview and outlined plans to continue working with the open source community to upstream the EFA kernel module and integrate with libfabric.
Accelerate Machine Learning with Ease using Amazon SageMakerAmazon Web Services
Organizations are using machine learning (ML) to address a host of business challenges, from product recommendations to demand forecasting. Until recently, developing these ML models took much time and effort, and it required expertise. In this session, we introduce Amazon SageMaker, a fully managed ML service that enables developers and data scientists to develop and deploy deep learning models quickly and easily. We walk through the features and benefits of Amazon SageMaker and discuss the uniquely designed ML algorithms that allow for optimized model training, getting you to production fast.
Accelerate Machine Learning with Ease Using Amazon SageMaker - BDA301 - Chica...Amazon Web Services
Organizations are using machine learning (ML) to address a host of business challenges, from product recommendations to demand forecasting. Until recently, developing these ML models took much time and effort, and it required expertise. In this session, we discuss and dive deep into Amazon SageMaker, a fully managed ML service that enables developers and data scientists to develop and deploy deep learning models quickly and easily. We walk through the features and benefits of Amazon SageMaker and discuss the uniquely designed ML algorithms that allow for optimized model training, getting you to production fast.
Work with Machine Learning in Amazon SageMaker - BDA203 - Atlanta AWS SummitAmazon Web Services
Organizations are using machine learning (ML) to address a host of business challenges, from product recommendations to demand forecasting. Until recently, developing these ML models took considerable time and effort, and it required expertise. In this session, we dive deep into Amazon SageMaker, a fully managed ML service that enables developers and data scientists to develop and deploy deep learning models quickly and easily. We walk through the features and benefits of Amazon SageMaker to get your ML models from concept to production.
Build Deep Learning Applications Using Apache MXNet, Featuring Workday (AIM40...Amazon Web Services
The document discusses using machine learning for information extraction from enterprise documents. It describes using MXNet and Apache SageMaker for building and deploying models. It discusses various algorithms and techniques used for problems like document scanning, text recognition and understanding.
Build Deep Learning Applications Using Apache MXNet - Featuring Chick-fil-A (...Amazon Web Services
The Apache MXNet deep learning framework is used for developing, training, and deploying diverse AI applications, including computer vision, speech recognition, natural language processing, and more at scale. In this session, learn how to get started with Apache MXNet on the Amazon SageMaker machine learning platform. Chick-fil-A share how they got started with MXNet on Amazon SageMaker to measure waffle fry freshness and how they leverage AWS services to improve the Chick-fil-A guest experience.
Work with Machine Learning in Amazon SageMaker - BDA203 - Toronto AWS SummitAmazon Web Services
Organizations are using machine learning (ML) to address a host of business challenges, from product recommendations to demand forecasting. Until recently, developing these ML models took considerable time and effort, and it required expertise. In this session, we dive deep into Amazon SageMaker, a fully managed ML service that enables developers and data scientists to develop and deploy deep learning models quickly and easily. We walk through the features and benefits of Amazon SageMaker to get your ML models from concept to production.
This document provides an agenda and overview for an MLOps workshop hosted by Amazon Web Services. The agenda includes introductions to Amazon AI, MLOps, Amazon SageMaker, machine learning pipelines, and a hands-on exercise to build an MLOps pipeline. It discusses key concepts like personas in MLOps, the CRISP-DM process, microservices deployment, and challenges of MLOps. It also provides overviews of Amazon SageMaker for machine learning and AWS services for continuous integration/delivery.
Predicting the Future with Amazon SageMaker - AWS Summit Sydney 2018Amazon Web Services
The document discusses Amazon SageMaker, a fully managed service that enables developers and data scientists to build, train, and deploy machine learning models at scale. It provides an overview of how SageMaker simplifies and automates many complex ML workflow tasks like setting up environments, training models, and deploying models into production. Key features highlighted include built-in algorithms, frameworks and SDK support, hyperparameter tuning, and one-click deployment. Examples are given of using the SageMaker APIs from the command line and Python.
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
Get Started with Deep Learning and Computer Vision Using AWS DeepLens (AIM316...Amazon Web Services
If you're new to deep learning, this workshop is for you. Learn how to build and deploy computer vision models using the AWS DeepLens deep learning-enabled video camera. Also learn to build a machine learning application and a model from scratch using Amazon SageMaker. Finally, learn to extend that model to Amazon SageMaker to build an end-to-end AI application.
Learn how to get started with Amazon SageMaker—our fully-managed service that spans the entire machine learning (ML) workflow—so you can build, train, and deploy models quickly. Use Amazon SageMaker to label and prepare your data, choose an algorithm, train, tune, and optimize it for deployment, make predictions, and take action. Get your models to production faster with Amazon SageMaker SDKs, builder tools, and APIs tailored to your programming language or platform. Also, discover how Amazon SageMaker Ground Truth can aid in the adoption of ML technology for your organization.
Building Deep Learning Applications with TensorFlow and SageMaker on AWS - Te...Amazon Web Services
Deep learning continues to push the state of the art in domains such as computer vision, natural language understanding, and recommendation engines. One of the key reasons for this progress is the availability of highly flexible and developer friendly deep learning frameworks. In this workshop, we provide an overview of deep learning, focusing on getting started with the TensorFlow framework on AWS.
[NEW LAUNCH!] Introducing Amazon Elastic Inference: Reduce Deep Learning Infe...Amazon Web Services
Deploying deep learning applications at scale can be cost prohibitive due to the need for hardware acceleration to meet latency and throughput requirements of inference. Amazon Elastic Inference helps you tackle this problem by reducing the cost of inference by up to 75% with GPU-powered acceleration that can be right-sized to your application’s inference needs. In this session, learn about how to deploy TensorFlow, Apache MXNet, and ONNX models with Amazon Elastic Inference on Amazon EC2 and Amazon SageMaker. Hear from Autodesk on the positive impact of AI on tools used to design and make a better world. Learn about how Autodesk and the Autodesk AI Lab are using Amazon Elastic Inference to make it cost efficient to run these tools at scale.
Talk by Sangeetha Krishnan, MTS at Adobe on the topic "Build, train and deploy your ML models with Amazon Sage Maker" at AWS Community Day, Bangalore 2018
Machine Learning with Amazon SageMaker - Algorithms and Frameworks - BDA304 -...Amazon Web Services
Algorithms and frameworks form a fundamental part of machine learning (ML). These critical components enable developers and data scientists to easily and quickly build ML models with well-defined interfaces for a range of use cases. The most commonly used algorithms and frameworks, built-in with Amazon SageMaker, make ML easier to address these use cases. In this session, we discuss the built-in algorithms and frameworks and how you can leverage them for your ML models. We also discuss the flexibility of bringing your own algorithm into Amazon SageMaker depending on your needs.
Deep Learning Applications Using PyTorch, Featuring Facebook (AIM402-R) - AWS...Amazon Web Services
With support for PyTorch 1.0 on Amazon SageMaker, you now have a flexible deep learning framework combined with a fully managed machine learning platform to transition seamlessly from research prototyping to production deployment. In this session, learn how to develop with PyTorch 1.0 within Amazon SageMaker using a novel generative adversarial network (GAN) tutorial. Then, hear from Facebook on how you can use the FAIRSeq modeling toolkit, which serves 6B translations daily for Facebook users, to train your own custom PyTorch models on Amazon SageMaker. Facebook also discusses the evolution of PyTorch 1.0 and features introduced to accelerate research and deployment.
BDA301 Working with Machine Learning in Amazon SageMaker: Algorithms, Models,...Amazon Web Services
Amazon SageMaker is a machine learning platform that allows users to build, train, and deploy machine learning models at scale. It provides several components including algorithms, hyperparameter optimization, and tools for building, hosting and deploying models. Users can choose from Amazon's built-in algorithms or bring their own custom algorithms. Models can be trained on Amazon's GPU and CPU instances and deployed for low latency inference.
AWS re:Invent 2018 - Machine Learning recap (December 2018)Julien SIMON
AWS is improving machine learning services in three key areas: cost, data preparation, and ease of use. New services like Amazon SageMaker GroundTruth and Amazon Personalize aim to reduce the cost and complexity of obtaining labeled data and building models. AWS is also optimizing frameworks like TensorFlow for faster, more efficient training and lowering inference costs with Elastic Inference. The goal is to continue driving down barriers to ML for all developers.
re:Invent Deep Dive on Amazon SageMaker, Amazon Forecast and Amazon PersonaliseAmazon Web Services
The document discusses new machine learning services from AWS including improvements to reduce the cost of training and inference, make obtaining labeled data easier through Amazon SageMaker Ground Truth, and increase ease of use with services like Amazon Personalize, Amazon Forecast, and the AWS Marketplace for Machine Learning. It also previewed upcoming services like Amazon SageMaker Reinforcement Learning and AWS DeepRacer for building autonomous systems through reinforcement learning.
This document discusses machine learning and Amazon Web Services' ML products and services. It covers AWS's ML infrastructure, AI services like Amazon Rekognition, efforts to improve training and inference costs through new instance types and Amazon Elastic Inference, and making it easier for developers to obtain labeled data through Amazon SageMaker. The document emphasizes that AWS has more ML customers and services than any other provider and is focused on increasing ease of use, reducing costs, and improving data preparation for ML developers.
Similar to Construindo Aplicações Deep Learning com TensorFlow e Amazon SageMaker - MCL302 - Sao Paulo Summit (20)
Come costruire servizi di Forecasting sfruttando algoritmi di ML e deep learn...Amazon Web Services
Il Forecasting è un processo importante per tantissime aziende e viene utilizzato in vari ambiti per cercare di prevedere in modo accurato la crescita e distribuzione di un prodotto, l’utilizzo delle risorse necessarie nelle linee produttive, presentazioni finanziarie e tanto altro. Amazon utilizza delle tecniche avanzate di forecasting, in parte questi servizi sono stati messi a disposizione di tutti i clienti AWS.
In questa sessione illustreremo come pre-processare i dati che contengono una componente temporale e successivamente utilizzare un algoritmo che a partire dal tipo di dato analizzato produce un forecasting accurato.
Big Data per le Startup: come creare applicazioni Big Data in modalità Server...Amazon Web Services
La varietà e la quantità di dati che si crea ogni giorno accelera sempre più velocemente e rappresenta una opportunità irripetibile per innovare e creare nuove startup.
Tuttavia gestire grandi quantità di dati può apparire complesso: creare cluster Big Data su larga scala sembra essere un investimento accessibile solo ad aziende consolidate. Ma l’elasticità del Cloud e, in particolare, i servizi Serverless ci permettono di rompere questi limiti.
Vediamo quindi come è possibile sviluppare applicazioni Big Data rapidamente, senza preoccuparci dell’infrastruttura, ma dedicando tutte le risorse allo sviluppo delle nostre le nostre idee per creare prodotti innovativi.
Ora puoi utilizzare Amazon Elastic Kubernetes Service (EKS) per eseguire pod Kubernetes su AWS Fargate, il motore di elaborazione serverless creato per container su AWS. Questo rende più semplice che mai costruire ed eseguire le tue applicazioni Kubernetes nel cloud AWS.In questa sessione presenteremo le caratteristiche principali del servizio e come distribuire la tua applicazione in pochi passaggi
Vent'anni fa Amazon ha attraversato una trasformazione radicale con l'obiettivo di aumentare il ritmo dell'innovazione. In questo periodo abbiamo imparato come cambiare il nostro approccio allo sviluppo delle applicazioni ci ha permesso di aumentare notevolmente l'agilità, la velocità di rilascio e, in definitiva, ci ha consentito di creare applicazioni più affidabili e scalabili. In questa sessione illustreremo come definiamo le applicazioni moderne e come la creazione di app moderne influisce non solo sull'architettura dell'applicazione, ma sulla struttura organizzativa, sulle pipeline di rilascio dello sviluppo e persino sul modello operativo. Descriveremo anche approcci comuni alla modernizzazione, compreso l'approccio utilizzato dalla stessa Amazon.com.
Come spendere fino al 90% in meno con i container e le istanze spot Amazon Web Services
L’utilizzo dei container è in continua crescita.
Se correttamente disegnate, le applicazioni basate su Container sono molto spesso stateless e flessibili.
I servizi AWS ECS, EKS e Kubernetes su EC2 possono sfruttare le istanze Spot, portando ad un risparmio medio del 70% rispetto alle istanze On Demand. In questa sessione scopriremo insieme quali sono le caratteristiche delle istanze Spot e come possono essere utilizzate facilmente su AWS. Impareremo inoltre come Spreaker sfrutta le istanze spot per eseguire applicazioni di diverso tipo, in produzione, ad una frazione del costo on-demand!
In recent months, many customers have been asking us the question – how to monetise Open APIs, simplify Fintech integrations and accelerate adoption of various Open Banking business models. Therefore, AWS and FinConecta would like to invite you to Open Finance marketplace presentation on October 20th.
Event Agenda :
Open banking so far (short recap)
• PSD2, OB UK, OB Australia, OB LATAM, OB Israel
Intro to Open Finance marketplace
• Scope
• Features
• Tech overview and Demo
The role of the Cloud
The Future of APIs
• Complying with regulation
• Monetizing data / APIs
• Business models
• Time to market
One platform for all: a Strategic approach
Q&A
Rendi unica l’offerta della tua startup sul mercato con i servizi Machine Lea...Amazon Web Services
Per creare valore e costruire una propria offerta differenziante e riconoscibile, le startup di successo sanno come combinare tecnologie consolidate con componenti innovativi creati ad hoc.
AWS fornisce servizi pronti all'utilizzo e, allo stesso tempo, permette di personalizzare e creare gli elementi differenzianti della propria offerta.
Concentrandoci sulle tecnologie di Machine Learning, vedremo come selezionare i servizi di intelligenza artificiale offerti da AWS e, anche attraverso una demo, come costruire modelli di Machine Learning personalizzati utilizzando SageMaker Studio.
OpsWorks Configuration Management: automatizza la gestione e i deployment del...Amazon Web Services
Con l'approccio tradizionale al mondo IT per molti anni è stato difficile implementare tecniche di DevOps, che finora spesso hanno previsto attività manuali portando di tanto in tanto a dei downtime degli applicativi interrompendo l'operatività dell'utente. Con l'avvento del cloud, le tecniche di DevOps sono ormai a portata di tutti a basso costo per qualsiasi genere di workload, garantendo maggiore affidabilità del sistema e risultando in dei significativi miglioramenti della business continuity.
AWS mette a disposizione AWS OpsWork come strumento di Configuration Management che mira ad automatizzare e semplificare la gestione e i deployment delle istanze EC2 per mezzo di workload Chef e Puppet.
Scopri come sfruttare AWS OpsWork a garanzia e affidabilità del tuo applicativo installato su Instanze EC2.
Microsoft Active Directory su AWS per supportare i tuoi Windows WorkloadsAmazon Web Services
Vuoi conoscere le opzioni per eseguire Microsoft Active Directory su AWS? Quando si spostano carichi di lavoro Microsoft in AWS, è importante considerare come distribuire Microsoft Active Directory per supportare la gestione, l'autenticazione e l'autorizzazione dei criteri di gruppo. In questa sessione, discuteremo le opzioni per la distribuzione di Microsoft Active Directory su AWS, incluso AWS Directory Service per Microsoft Active Directory e la distribuzione di Active Directory su Windows su Amazon Elastic Compute Cloud (Amazon EC2). Trattiamo argomenti quali l'integrazione del tuo ambiente Microsoft Active Directory locale nel cloud e l'utilizzo di applicazioni SaaS, come Office 365, con AWS Single Sign-On.
Dal riconoscimento facciale al riconoscimento di frodi o difetti di fabbricazione, l'analisi di immagini e video che sfruttano tecniche di intelligenza artificiale, si stanno evolvendo e raffinando a ritmi elevati. In questo webinar esploreremo le possibilità messe a disposizione dai servizi AWS per applicare lo stato dell'arte delle tecniche di computer vision a scenari reali.
Amazon Web Services e VMware organizzano un evento virtuale gratuito il prossimo mercoledì 14 Ottobre dalle 12:00 alle 13:00 dedicato a VMware Cloud ™ on AWS, il servizio on demand che consente di eseguire applicazioni in ambienti cloud basati su VMware vSphere® e di accedere ad una vasta gamma di servizi AWS, sfruttando a pieno le potenzialità del cloud AWS e tutelando gli investimenti VMware esistenti.
Molte organizzazioni sfruttano i vantaggi del cloud migrando i propri carichi di lavoro Oracle e assicurandosi notevoli vantaggi in termini di agilità ed efficienza dei costi.
La migrazione di questi carichi di lavoro, può creare complessità durante la modernizzazione e il refactoring delle applicazioni e a questo si possono aggiungere rischi di prestazione che possono essere introdotti quando si spostano le applicazioni dai data center locali.
Crea la tua prima serverless ledger-based app con QLDB e NodeJSAmazon Web Services
Molte aziende oggi, costruiscono applicazioni con funzionalità di tipo ledger ad esempio per verificare lo storico di accrediti o addebiti nelle transazioni bancarie o ancora per tenere traccia del flusso supply chain dei propri prodotti.
Alla base di queste soluzioni ci sono i database ledger che permettono di avere un log delle transazioni trasparente, immutabile e crittograficamente verificabile, ma sono strumenti complessi e onerosi da gestire.
Amazon QLDB elimina la necessità di costruire sistemi personalizzati e complessi fornendo un database ledger serverless completamente gestito.
In questa sessione scopriremo come realizzare un'applicazione serverless completa che utilizzi le funzionalità di QLDB.
Con l’ascesa delle architetture di microservizi e delle ricche applicazioni mobili e Web, le API sono più importanti che mai per offrire agli utenti finali una user experience eccezionale. In questa sessione impareremo come affrontare le moderne sfide di progettazione delle API con GraphQL, un linguaggio di query API open source utilizzato da Facebook, Amazon e altro e come utilizzare AWS AppSync, un servizio GraphQL serverless gestito su AWS. Approfondiremo diversi scenari, comprendendo come AppSync può aiutare a risolvere questi casi d’uso creando API moderne con funzionalità di aggiornamento dati in tempo reale e offline.
Inoltre, impareremo come Sky Italia utilizza AWS AppSync per fornire aggiornamenti sportivi in tempo reale agli utenti del proprio portale web.
Database Oracle e VMware Cloud™ on AWS: i miti da sfatareAmazon Web Services
Molte organizzazioni sfruttano i vantaggi del cloud migrando i propri carichi di lavoro Oracle e assicurandosi notevoli vantaggi in termini di agilità ed efficienza dei costi.
La migrazione di questi carichi di lavoro, può creare complessità durante la modernizzazione e il refactoring delle applicazioni e a questo si possono aggiungere rischi di prestazione che possono essere introdotti quando si spostano le applicazioni dai data center locali.
In queste slide, gli esperti AWS e VMware presentano semplici e pratici accorgimenti per facilitare e semplificare la migrazione dei carichi di lavoro Oracle accelerando la trasformazione verso il cloud, approfondiranno l’architettura e dimostreranno come sfruttare a pieno le potenzialità di VMware Cloud ™ on AWS.
1) The document discusses building a minimum viable product (MVP) using Amazon Web Services (AWS).
2) It provides an example of an MVP for an omni-channel messenger platform that was built from 2017 to connect ecommerce stores to customers via web chat, Facebook Messenger, WhatsApp, and other channels.
3) The founder discusses how they started with an MVP in 2017 with 200 ecommerce stores in Hong Kong and Taiwan, and have since expanded to over 5000 clients across Southeast Asia using AWS for scaling.
This document discusses pitch decks and fundraising materials. It explains that venture capitalists will typically spend only 3 minutes and 44 seconds reviewing a pitch deck. Therefore, the deck needs to tell a compelling story to grab their attention. It also provides tips on tailoring different types of decks for different purposes, such as creating a concise 1-2 page teaser, a presentation deck for pitching in-person, and a more detailed read-only or fundraising deck. The document stresses the importance of including key information like the problem, solution, product, traction, market size, plans, team, and ask.
This document discusses building serverless web applications using AWS services like API Gateway, Lambda, DynamoDB, S3 and Amplify. It provides an overview of each service and how they can work together to create a scalable, secure and cost-effective serverless application stack without having to manage servers or infrastructure. Key services covered include API Gateway for hosting APIs, Lambda for backend logic, DynamoDB for database needs, S3 for static content, and Amplify for frontend hosting and continuous deployment.
This document provides tips for fundraising from startup founders Roland Yau and Sze Lok Chan. It discusses generating competition to create urgency for investors, fundraising in parallel rather than sequentially, having a clear fundraising narrative focused on what you do and why it's compelling, and prioritizing relationships with people over firms. It also notes how the pandemic has changed fundraising, with examples of deals done virtually during this time. The tips emphasize being fully prepared before fundraising and cultivating connections with investors in advance.
AWS_HK_StartupDay_Building Interactive websites while automating for efficien...Amazon Web Services
This document discusses Amazon's machine learning services for building conversational interfaces and extracting insights from unstructured text and audio. It describes Amazon Lex for creating chatbots, Amazon Comprehend for natural language processing tasks like entity extraction and sentiment analysis, and how they can be used together for applications like intelligent call centers and content analysis. Pre-trained APIs simplify adding machine learning to apps without requiring ML expertise.
Amazon Elastic Container Service (Amazon ECS) è un servizio di gestione dei container altamente scalabile, che semplifica la gestione dei contenitori Docker attraverso un layer di orchestrazione per il controllo del deployment e del relativo lifecycle. In questa sessione presenteremo le principali caratteristiche del servizio, le architetture di riferimento per i differenti carichi di lavoro e i semplici passi necessari per poter velocemente migrare uno o più dei tuo container.