The document introduces several new AWS services and features including:
1) Inf1 instances powered by AWS Inferentia custom ML chip for fast, low-cost machine learning inference.
2) Graviton2 processor-based M6g, C6g, and R6g EC2 instances for improved price-performance.
3) Amazon Braket for exploring and experimenting with quantum computing.
4) AWS Compute Optimizer to identify optimal EC2 instances and Auto Scaling groups using machine learning recommendations.
Module1 - Amazon Personalize 중심으로 살펴보는 추천 시스템의 원리와 구축
Module 2 - 추천 시스템을 위한 데이터 분석 시스템 구축 하기
Module 3 - E-Commerce 사이트를 보다 Smart 하게 만들기 (Amazon Comprehend & Fraud Detector)
Use Amazon Rekognition to Build a Facial Recognition SystemAmazon Web Services
Amazon Rekognition makes it easy to extract meaningful metadata from visual content. In this workshop, you will work in teams to build a simple system to help track missing persons. You’ll develop a solution that leverages Amazon Rekognition and other AWS services to analyze images from various sources (e.g., social media) and provide authorities with timely reports and alerts on new leads for missing individuals. The solution will entail a repeatable and automated process that follows best practices for architecting in the cloud, such as designing for high availability and scalability.
In this session from the London AWS Summit 2015 Tech Track Replay, AWS Technical Evangelist Ian Massingham introduces the new Amazon Machine Learning service.
Amazon Machine Learning is a service that makes it easy for developers of all skill levels to use machine learning technology. Amazon Machine Learning provides visualization tools and wizards that guide you through the process of creating machine learning (ML) models without having to learn complex ML algorithms and technology. Once your models are ready, Amazon Machine Learning makes it easy to obtain predictions for your application using simple APIs, without having to implement custom prediction generation code, or manage any infrastructure.
Amazon.com 의 개인화 추천 / 예측 기능을 우리도 써 봅시다. :: 심호진 - AWS Community Day 2019AWSKRUG - AWS한국사용자모임
Amazon Personalize
개인화 및 추천에 대하여
Amazon Personalize 소개
Amazon Personalize 사용 방법
데모 - 캡쳐 화면
결론
Amazon Forecast
예측 기술에 대하여
Amazon Forecast 소개
Amazon Forecast 사용 방법
데모 - 캡쳐 화면
결론
Module1 - Amazon Personalize 중심으로 살펴보는 추천 시스템의 원리와 구축
Module 2 - 추천 시스템을 위한 데이터 분석 시스템 구축 하기
Module 3 - E-Commerce 사이트를 보다 Smart 하게 만들기 (Amazon Comprehend & Fraud Detector)
Use Amazon Rekognition to Build a Facial Recognition SystemAmazon Web Services
Amazon Rekognition makes it easy to extract meaningful metadata from visual content. In this workshop, you will work in teams to build a simple system to help track missing persons. You’ll develop a solution that leverages Amazon Rekognition and other AWS services to analyze images from various sources (e.g., social media) and provide authorities with timely reports and alerts on new leads for missing individuals. The solution will entail a repeatable and automated process that follows best practices for architecting in the cloud, such as designing for high availability and scalability.
In this session from the London AWS Summit 2015 Tech Track Replay, AWS Technical Evangelist Ian Massingham introduces the new Amazon Machine Learning service.
Amazon Machine Learning is a service that makes it easy for developers of all skill levels to use machine learning technology. Amazon Machine Learning provides visualization tools and wizards that guide you through the process of creating machine learning (ML) models without having to learn complex ML algorithms and technology. Once your models are ready, Amazon Machine Learning makes it easy to obtain predictions for your application using simple APIs, without having to implement custom prediction generation code, or manage any infrastructure.
Amazon.com 의 개인화 추천 / 예측 기능을 우리도 써 봅시다. :: 심호진 - AWS Community Day 2019AWSKRUG - AWS한국사용자모임
Amazon Personalize
개인화 및 추천에 대하여
Amazon Personalize 소개
Amazon Personalize 사용 방법
데모 - 캡쳐 화면
결론
Amazon Forecast
예측 기술에 대하여
Amazon Forecast 소개
Amazon Forecast 사용 방법
데모 - 캡쳐 화면
결론
Deep learning-based image recognition: Intro to Amazon Rekognition: Amazon Web Services
This session will introduce you to Amazon Rekognition, a service that makes it easy to add image analysis to your applications. With Rekognition, you can detect objects, scenes, and faces in images. You can also search and compare faces. Rekognition’s API lets you easily build powerful visual search and discovery into your applications. With Amazon Rekognition, you only pay for the images you analyze and the face metadata you store. There are no minimum fees and there are no upfront commitments. To get started with Rekognition, simply log in to the Rekognition console to try the service with sample photos or your own photos.
(BDT302) Real-World Smart Applications With Amazon Machine LearningAmazon Web Services
Have you always wanted to add predictive capabilities to your application, but haven’t been able to find the time or the right technology to get started? In this session, learn how an end-to-end smart application can be built in the AWS cloud. We demonstrate how to use Amazon Machine Learning (Amazon ML) to create machine learning models, deploy them to production, and obtain predictions in real-time. We then demonstrate how to build a complete smart application using Amazon ML, Amazon Kinesis, and AWS Lambda. We walk you through the process flow and architecture, demonstrate outcomes, and then dive into the code for implementation. In this session, you learn how to use Amazon ML as well as how to integrate Amazon ML into your applications to take advantage of predictive analysis in the cloud.
Amazon SageMaker 기반 고품질 데이터 생성 및 심화 기계학습 기법 - 김필호 솔루션즈 아키텍트, AWS / 강정희 솔루션즈 아...Amazon Web Services Korea
Amazon SageMaker 기반 고품질 데이터 생성 및 심화 기계학습 기법
김필호 솔루션즈 아키텍트, AWS
강정희 솔루션즈 아키텍트, AWS
Amazon SageMaker는 이제 AWS의 인공지능/머신러닝을 대표하는 서비스가 되었습니다. 머신러닝 프로세스의 핵심인 모델 트레이닝과 배포를 손쉽고 효과적으로 할 수 있도록 SageMaker에서는 다양한 기능과 최적화 기법, 최신 알고리즘들이 끊임없이 업데이트되고 있습니다. 본 세션에서는 데이터 전처리, 모델 최적화, 시멘틱 분류 알고리즘 등에 관해 심화된 기술을 다룹니다.
BDA 301 An Introduction to Amazon Rekognition, for Deep Learning-based Comput...Amazon Web Services
This session will introduce you to Amazon Rekognition, a service that makes it easy to add deep learning image analysis to your applications. Amazon Rekognition is based on the same proven, highly scalable, deep learning technology developed by Amazon’s computer vision scientists to analyze billions of images daily for Prime Photos. With Rekognition, you can detect objects, scenes, and faces in images. You can also search and compare faces. Rekognition’s API lets you easily build powerful visual search and discovery into your applications. This session will introduce real world use cases for Rekognition, and show you how to quickly get started using the Rekognition console, so you can easily try the service for free with sample photos or your own photos. With Amazon Rekognition, you only pay for the images you analyze and the face metadata you store, with no minimum fees and there are no upfront commitments. Join this session and learn more about Amazon Rekognition!
AWS System Manager: Parameter Store를 사용한 AWS 구성 데이터 관리 기법 - 정창훈, 당근마켓 / 김대권, ...Amazon Web Services Korea
AWS System Manager: Parameter Store를 사용한 AWS 구성 데이터 관리 기법
정창훈, 당근마켓
AWS Systems Manager는 모든 AWS 리소스에 대한 가시성과 운영 데이터 통합 및 자동화 제어를 가능하게 하는 멋진 서비스입니다. 본 세션에서는 System Manager의 기본 기능에 대한 소개와 함께 어려워서 못쓰기보다 몰라서 안쓰는 Parameter Store의 사용법과 구성 정보 관리 부터 ECS, KMS, Lambda와 같은 AWS의 다른 서비스들과 연동해서 사용하는 방법에 대해서 당근 마켓의 실제 사례와 함께 소개합니다.
Amazon Machine Learning Case Study: Predicting Customer ChurnAmazon Web Services
We do a deeper dive into Amazon Machine Learning, using a specific business problem as an example – predicting if the customer is about to leave your service, also known as customer churn. We examine several practical aspects of building and using a model, including the use of the recipe language for training data manipulation and modeling the costs of false positive/negative errors.
Building an end to end image recognition service - Tel Aviv Summit 2018Amazon Web Services
In this session, we’ll learn how to build and deploy end to end solutions for ingesting and processing computer vision solutions, using machine learning models connected to live video streams, and getting insights such as face detection and object analysis. At the end of the session developers of all skill levels will be able to build their own deep learning powered, computer-vision applications. Attendees will learn how to experiment with different projects for face detection, object recognition and other video-based AWS Machine Learning services.
Amazon Machine Learning is a service that makes it easy for developers of all skill levels to use machine learning technology. Amazon Machine Learning provides visualization tools and wizards that guide you through the process of creating machine learning (ML) models without having to learn complex ML algorithms and technology. Once your models are ready, Amazon Machine Learning makes it easy to obtain predictions for your application using simple APIs, without having to implement custom prediction generation code, or manage any infrastructure. More information: https://aws.amazon.com/machine-learning/
Deep learning-based image recognition: Intro to Amazon Rekognition: Amazon Web Services
This session will introduce you to Amazon Rekognition, a service that makes it easy to add image analysis to your applications. With Rekognition, you can detect objects, scenes, and faces in images. You can also search and compare faces. Rekognition’s API lets you easily build powerful visual search and discovery into your applications. With Amazon Rekognition, you only pay for the images you analyze and the face metadata you store. There are no minimum fees and there are no upfront commitments. To get started with Rekognition, simply log in to the Rekognition console to try the service with sample photos or your own photos.
(BDT302) Real-World Smart Applications With Amazon Machine LearningAmazon Web Services
Have you always wanted to add predictive capabilities to your application, but haven’t been able to find the time or the right technology to get started? In this session, learn how an end-to-end smart application can be built in the AWS cloud. We demonstrate how to use Amazon Machine Learning (Amazon ML) to create machine learning models, deploy them to production, and obtain predictions in real-time. We then demonstrate how to build a complete smart application using Amazon ML, Amazon Kinesis, and AWS Lambda. We walk you through the process flow and architecture, demonstrate outcomes, and then dive into the code for implementation. In this session, you learn how to use Amazon ML as well as how to integrate Amazon ML into your applications to take advantage of predictive analysis in the cloud.
Amazon SageMaker 기반 고품질 데이터 생성 및 심화 기계학습 기법 - 김필호 솔루션즈 아키텍트, AWS / 강정희 솔루션즈 아...Amazon Web Services Korea
Amazon SageMaker 기반 고품질 데이터 생성 및 심화 기계학습 기법
김필호 솔루션즈 아키텍트, AWS
강정희 솔루션즈 아키텍트, AWS
Amazon SageMaker는 이제 AWS의 인공지능/머신러닝을 대표하는 서비스가 되었습니다. 머신러닝 프로세스의 핵심인 모델 트레이닝과 배포를 손쉽고 효과적으로 할 수 있도록 SageMaker에서는 다양한 기능과 최적화 기법, 최신 알고리즘들이 끊임없이 업데이트되고 있습니다. 본 세션에서는 데이터 전처리, 모델 최적화, 시멘틱 분류 알고리즘 등에 관해 심화된 기술을 다룹니다.
BDA 301 An Introduction to Amazon Rekognition, for Deep Learning-based Comput...Amazon Web Services
This session will introduce you to Amazon Rekognition, a service that makes it easy to add deep learning image analysis to your applications. Amazon Rekognition is based on the same proven, highly scalable, deep learning technology developed by Amazon’s computer vision scientists to analyze billions of images daily for Prime Photos. With Rekognition, you can detect objects, scenes, and faces in images. You can also search and compare faces. Rekognition’s API lets you easily build powerful visual search and discovery into your applications. This session will introduce real world use cases for Rekognition, and show you how to quickly get started using the Rekognition console, so you can easily try the service for free with sample photos or your own photos. With Amazon Rekognition, you only pay for the images you analyze and the face metadata you store, with no minimum fees and there are no upfront commitments. Join this session and learn more about Amazon Rekognition!
AWS System Manager: Parameter Store를 사용한 AWS 구성 데이터 관리 기법 - 정창훈, 당근마켓 / 김대권, ...Amazon Web Services Korea
AWS System Manager: Parameter Store를 사용한 AWS 구성 데이터 관리 기법
정창훈, 당근마켓
AWS Systems Manager는 모든 AWS 리소스에 대한 가시성과 운영 데이터 통합 및 자동화 제어를 가능하게 하는 멋진 서비스입니다. 본 세션에서는 System Manager의 기본 기능에 대한 소개와 함께 어려워서 못쓰기보다 몰라서 안쓰는 Parameter Store의 사용법과 구성 정보 관리 부터 ECS, KMS, Lambda와 같은 AWS의 다른 서비스들과 연동해서 사용하는 방법에 대해서 당근 마켓의 실제 사례와 함께 소개합니다.
Amazon Machine Learning Case Study: Predicting Customer ChurnAmazon Web Services
We do a deeper dive into Amazon Machine Learning, using a specific business problem as an example – predicting if the customer is about to leave your service, also known as customer churn. We examine several practical aspects of building and using a model, including the use of the recipe language for training data manipulation and modeling the costs of false positive/negative errors.
Building an end to end image recognition service - Tel Aviv Summit 2018Amazon Web Services
In this session, we’ll learn how to build and deploy end to end solutions for ingesting and processing computer vision solutions, using machine learning models connected to live video streams, and getting insights such as face detection and object analysis. At the end of the session developers of all skill levels will be able to build their own deep learning powered, computer-vision applications. Attendees will learn how to experiment with different projects for face detection, object recognition and other video-based AWS Machine Learning services.
Amazon Machine Learning is a service that makes it easy for developers of all skill levels to use machine learning technology. Amazon Machine Learning provides visualization tools and wizards that guide you through the process of creating machine learning (ML) models without having to learn complex ML algorithms and technology. Once your models are ready, Amazon Machine Learning makes it easy to obtain predictions for your application using simple APIs, without having to implement custom prediction generation code, or manage any infrastructure. More information: https://aws.amazon.com/machine-learning/
Building Complex Workloads in Cloud - AWS PS Summit CanberraAmazon Web Services
In this session we will explore technologies & solutions to deploy ever increasing complex workload like High Performance Computing, Big Data and AI seamlessly to the cloud. You will hear from two strategic partners on how they have used AWS cloud and Intel technologies to accelerate innovation for their customers.
Speaker: Jason Jacobs, Industry Manager, ANZ Public Sector, Intel Corporation with Aileen Gemma Smith CEO, Vizalytics and Zack Levy, DevOps Partner, Deloitte Consulting
(CMP405) Containerizing Video: The Next Gen Video Transcoding PipelineAmazon Web Services
"Media delivery requirements are continually changing, driven by accelerating mobile, tablet, smart TV, and set-top technology advances. Media services need to deliver higher-resolution content at lower bitrates to consumers, which has traditionally been a compute-intensive undertaking with slow advancements in the adoption of video codecs, containers, and related technologies.
In this session, we look at some of the existing workflow constraints, and explore a solution to process media in an agile fashion using modern, efficient codecs. We use Amazon S3 events and AWS Lambda to configure media both pre- and post-process, process content in parallel with Amazon ECS using custom containers for a high level of elastic compute density, and deliver generated media to reference protocol clients via Amazon CloudFront. We also leverage Amazon EFS for scalable, shared storage in the distributed containerized environment for video processing. Issues include: parallel processing of content using Amazon ECS, pipelining and conversion of data using AWS Lambda, building an Amazon ECS-based media transcoding cluster, and delivering next-gen media through Amazon CloudFront."
High-Performance-Computing-on-AWS-and-Industry-SimulationAmazon Web Services
High Performance Computing on AWS enables engineers, analysts, and researchers to think beyond the limitations of on-premises HPC infrastructure. AWS HPC solutions address the infrastructure capacity, secure global collaboration, technology obsolescence, and capital expenditure constraints associated with on-premises HPC clusters to give you the freedom to tackle the most challenging HPC workloads and get to your results faster. In this session. we will provide a quick overview of the services that make up the HPC on AWS solution, and share customer success stories across multiple industries, such as Financial Services and Life Sciences.
Deep learning is an implementation of machine learning that uses neural networks to solve difficult and complex problems, such as computer vision, natural language processing, and recommendations. Due to the availability of deep learning libraries and frameworks, developers have the ability to enhance the capabilities of their applications and projects. In this workshop, you learn how to build and deploy a powerful deep learning framework called MXNet on containers. The portability and resource management benefit of containers means developers can focus less on infrastructure and more on building. The labs start by demonstrating the automation capabilities of AWS CloudFormation to stand up core infrastructure; as an added bonus, you use Spot Fleet to leverage the cost benefits of using Spot Instances, especially for developer environments. Then, you walk through creating an MXNet container in Docker and deploying it with Amazon ECS. Finally, you walk through an image classification demo of MXNet to validate that everything is working as expected. Note: This workshop focuses on containerizing MXNet. The features of MXNet and capabilities of deep learning in general are vast, and there are recorded sessions from re:Invent that dive deeper on these topics. All you need to participate is a laptop and AWS account. Pizza will be provided.
AWS Compute Evolved Week: High Performance Computing on AWSAmazon Web Services
AWS Compute Evolved Week at the San Francisco Loft: High Performance Computing on AWS
High Performance Computing (HPC) has been driving technology advancements for many decades. HPC enables performance-demanding applications and workloads to solve complex problems while dramatically reducing time to solution. With a history of requiring very large data centers, HPC is now on the edge of a paradigm shift. The AWS Cloud will allow customers to have access to near infinite compute and storage resources, without the overhead of running their own data centers. There are a vast number of HPC segments and verticals that are already seeing great success running their workloads on AWS. Life Sciences, Financial Services, Energy & Geo Sciences, as well as Manufacturing are successfully deploying their applications on AWS. In these two sessions we will discuss how AWS can help you run HPC workloads in the cloud.
Speakers: Pierre-Yves Aquilanti - Sr. HPC Specialized Solutions Architect, AWS & Anh Tran - Sr. HPC Specialized Solutions Architect, AWS
AMF304-Optimizing Design and Engineering Performance in the Cloud for Manufac...Amazon Web Services
Manufacturing companies in all sectors—including automotive, aerospace, semiconductor, and industrial manufacturing—rely on design and engineering software in their product development processes. These computationally-intensive applications—such as computer-aided design and engineering (CAD and CAE), electronic design automation (EDA), other performance-critical applications—require immense scale and orchestration to meet the demands of today’s manufacturing requirements. In this session, you learn how to achieve the maximum possible performance and throughput from design and engineering workloads running on Amazon EC2, elastic GPUs, and managed services such as AWS Batch and Amazon AppStream 2.0. We demonstrate specific optimization techniques and share samples on how to accelerate batch and interactive workloads on AWS. We also demonstrate how to extend and migrate on-premises, high performance compute workloads with AWS, and use a combination of On-Demand Instances, Reserved Instances, and Spot Instances to minimize costs.
Risk Management and Particle Accelerators: Innovating with New Compute Platfo...Amazon Web Services
What does risk modeling and analytics in financial services have in common with large scale computing in high energy physics? Come to this session to hear how financial services customers like Aon are taking advantage of new approaches like predictive analytics and AI/deep learning on AWS to perform risk modeling and how Brookhaven National Laboratory are using 10s of thousands of cores to do large scale grid computing for Monte Carlo simulations in high energy physics. In addition, we will also showcase how CSIRO eHealth team in Australia are innovating with serverless architectures using AWS Lambda for personalized medicine and genomics.
Speakers: Adrian White, Sr SciCo Technical Manager, Amazon Web Services
AWS re:Invent 2016: High Performance Cinematic Production in the Cloud (MAE304)Amazon Web Services
The process of making a film is highly complex, and comprises of multiple workflows across story development, pre-production, production, post-production and final distribution. Given the size and amount of media and assets associated with each stage, high performance infrastructure is often essential to meeting deadlines.
In this session we will take a deeper dive at running a full cinematic production in the cloud, with a focus on solutions for each of the production stages. We will also look at best practices around design, optimization, performance, scheduling, scalability and low latency utilizing AWS technologies such as EC2, Lambda, Snowball, Direct Connect, and Partner Solutions.
London Microservices Meetup: Lessons learnt adopting microservicesCobus Bernard
Talk about my experiences (and failures) helping companies move to AWS and adopt microservices architecture. Heavy focus on the people aspect with some tech to set the scene.
AWS SSA Webinar 34 - Getting started with databases on AWS - Managing DBs wit...Cobus Bernard
In this session, we will look at how you can enable development teams to create and manage their own databases using AWS CDK. We will look at how to create shareable, reusable code blocks to help speed up development as well as ensuring best practices are applied when creating the databases.
AWS SSA Webinar 33 - Getting started with databases on AWS Amazon DynamoDBCobus Bernard
In this session, we will take a look at Amazon DynamoDB and how you can get started building application with it. We will look at table design, common access patterns and compare it to a relational database.
AWS SSA Webinar 32 - Getting Started with databases on AWS: Choosing the righ...Cobus Bernard
In this session, we will take you through the different database services that you can choose from on AWS. We will take a look at the workings of each one, from Amazon RDS for relational databases, to Amazon QLDB for ledger databases.
AWS SSA Webinar 28 - Getting Started with AWS - Infrastructure as CodeCobus Bernard
One of the parts of doing things properly at scale is being able to describe your infrastructure as code and deploy it as such. If we already treat our infrastructure as code, why not apply all the best practices of software delivery to infrastructure delivery.
In this session we look into Infrastructure as Code solutions, best practices and patterns on AWS.
AWS Webinar 24 - Getting Started with AWS - Understanding DRCobus Bernard
In this session, we will look at how you can use AWS for your disaster recovery (DR) requirements to allow failing over to services hosted on AWS. We will also cover how to think about resiliency and auto-healing systems instead of manual fail-over to a full set of additional hardware. Lastly, we will touch on using DR as a strategy for migrating to the cloud.
AWS Webinar 23 - Getting Started with AWS - Understanding total cost of owner...Cobus Bernard
In this session, we will go over cloud economics and understanding the total cost of ownership (TCO) when building in the cloud and how you are trading upfront capital expenditure (CapEx) for operational expenditure (OpEx). We will also look at how the TCO changes over time as you start modernising your applications to make full use of the cloud's capabilities. Lastly, we will cover the different purchasing options to help you understand how you can reduce costs even further by identifying consistent, base workloads.
AWS SSA Webinar 21 - Getting Started with Data lakes on AWSCobus Bernard
In this session, we will take you through getting started with a Data Lake by looking at how you can ingest data to Amazon S3, query it with Amazon Athena and perform ETL operations on it using AWS Glue. We will be using the Redshift cluster from the previous session to export data to S3 to query.
AWS SSA Webinar 20 - Getting Started with Data Warehouses on AWSCobus Bernard
In this session, we will take you through setting up an Amazon Redshift cluster and at the ways you can populate it with data. We will start by using AWS DMS to replicate the data as-is as well as doing some ETL on it. This will be followed by AWS Glue where you can do more advanced ETL operations. Lastly, we will look at how you can use Amazon Kinesis Firehose to stream event directly to the Redshift cluster.
AWS SSA Webinar 19 - Getting Started with Multi-Region Architecture: ServicesCobus Bernard
In this session, we will start with a service that is running in one region, and then go through the steps to set up the required infrastructure in a new region, deploy there and start service traffic from both. We will also
AWS SSA Webinar 18 - Getting Started with Multi-Region Architecture: DataCobus Bernard
In this session, we will start with an RDS instance in one region, and then look at the ways we can use an additional region, either by migrating the entire database, or making use of Amazon Aurora Global Database to have active writer nodes in more than 1 region.
AWS EMEA Online Summit - Live coding with containersCobus Bernard
In this session, we will go over how to launch containers using Amazon ECS with both Amazon EC2 worker nodes as well as AWS Fargate. We will take a look at using blended on-demand and spot instance for EC2, and Fargate spot to reduce costs when running containers on ECS.
AWS EMEA Online Summit - Blending Spot and On-Demand instances to optimizing ...Cobus Bernard
In this session, we take a look at how you can use new features in auto-scaling groups to blend spot an on-demand instance to reduce your overall costs.
AWS SSA Webinar 17 - Getting Started on AWS with Amazon RDSCobus Bernard
In this session, we will take a deeper look at how to use Amazon RDS to host your database. We will start by spinning up a single instance db and then work through setting up a production ready, multi-available zone cluster with read replicas, daily backups. Lastly, we show you how to use Amazon RDS Proxy to handle the database connection pool and credentials for you.
AWS SSA Webinar 16 - Getting Started on AWS with Amazon EC2Cobus Bernard
In this session, we will take a deeper look at how to deploy, run and monitor applications deployed to Amazon EC2 using AWS CodeDeploy and AWS CodePipeline. We will start by building a golden AMI with our application requirements pre-installed, then using that AMI in an AWS Autoscaling Group where CodePipeline/CodeBuild will deploy our application. You will also learn how using CodeDeploy with Autoscaling groups provide additional resiliency by replacing any instance that has an issue.
AWS SSA Webinar 15 - Getting started on AWS with Containers: Amazon EKSCobus Bernard
In this sesison, we will take a deeper look at how to get started using Amazon EKS by setting up a new cluster using 'eksctl' and deploying a sample application to it. We will look at the components created and how to configure a custom domain and using Amazon Certificate Manager to run it over https. We will also look at using AWS AppMesh for service discovery in the cluster.
AWS SSA Webinar 13 - Getting started on AWS with Containers: Amazon ECSCobus Bernard
In this session, we will take a deeper look at how to deploy, run and monitor applications deployed in containers to Amazon ECS using AWS CodeDeploy, CodePipeline and Amazon ECR. We will start by deploying to ECS with Amazon EC2 instances, and then show how using AWS Fargate simplifies the process.
AWS SSA Webinar 11 - Getting started on AWS: SecurityCobus Bernard
In this session, we will take a deeper look at the security services and features available on AWS. We will look at how Identity and Access Management (IAM) works by covering IAM users, policies, roles, groups. We will also look at AWS Security groups and how they are applied to the different infrastructure components, e.g. Amazon EC2 instances, Load Balancers, Databases (via Amazon RDS). Lastly, we will take a quick look at Amazon Certificate Manager for SSL certificates and mention additional services like Amazon Detective, GuardDuty, Macie, WAF.
AWS SSA Webinar 12 - Getting started on AWS with ContainersCobus Bernard
In this session, we will look a the building blocks available on AWS for Compute, Storage and Networking. It will focus on providing and overview how what each service is used for to prepare the attendee for the 3 followup sessions where each of the 3 categories will be covered in more detail.
Multi-cluster Kubernetes Networking- Patterns, Projects and GuidelinesSanjeev Rampal
Talk presented at Kubernetes Community Day, New York, May 2024.
Technical summary of Multi-Cluster Kubernetes Networking architectures with focus on 4 key topics.
1) Key patterns for Multi-cluster architectures
2) Architectural comparison of several OSS/ CNCF projects to address these patterns
3) Evolution trends for the APIs of these projects
4) Some design recommendations & guidelines for adopting/ deploying these solutions.
1.Wireless Communication System_Wireless communication is a broad term that i...JeyaPerumal1
Wireless communication involves the transmission of information over a distance without the help of wires, cables or any other forms of electrical conductors.
Wireless communication is a broad term that incorporates all procedures and forms of connecting and communicating between two or more devices using a wireless signal through wireless communication technologies and devices.
Features of Wireless Communication
The evolution of wireless technology has brought many advancements with its effective features.
The transmitted distance can be anywhere between a few meters (for example, a television's remote control) and thousands of kilometers (for example, radio communication).
Wireless communication can be used for cellular telephony, wireless access to the internet, wireless home networking, and so on.
ER(Entity Relationship) Diagram for online shopping - TAEHimani415946
https://bit.ly/3KACoyV
The ER diagram for the project is the foundation for the building of the database of the project. The properties, datatypes, and attributes are defined by the ER diagram.
This 7-second Brain Wave Ritual Attracts Money To You.!nirahealhty
Discover the power of a simple 7-second brain wave ritual that can attract wealth and abundance into your life. By tapping into specific brain frequencies, this technique helps you manifest financial success effortlessly. Ready to transform your financial future? Try this powerful ritual and start attracting money today!
3. Amazon Confidential
Amazon EC2 Inf1 Instances
Introducing
The fastest and lowest cost machine learning inference in the cloud
Featuring AWS Inferentia, the first custom ML chip designed by AWS
Inf1 delivers up to 3X higher throughput and up to 40% lower cost
per inference compared to GPU powered G4 instances
Compute
General Availability – December 3
L E A R N M O R E CMP324-R: Deliver high performance ML inference with AWS Inferentia Wednesday, 7pm, Aria
Natural language
processing
PersonalizationObject
detection
Speech
recognition
Image processing Fraud
detection
4. Amazon Confidential
Introducing Amazon EC2 Inferentia
• Fast, low-latency inferencing at a very low cost
• 64 teraOPS on 16-bit floating point (FP16 and BF16) and mixed-precision data.
• 128 teraOPS on 8-bit integer (INT8) data.
• Neuron SDK: https://github.com/aws/aws-neuron-sdk
• Available in Deep Learning AMIs and Deep Learning Containers
• TensorFlow and Apache MXNet, PyTorch coming soon
Instance Name Inferentia Chips vCPUs RAM EBS Bandwidth
inf1.xlarge 1 4 8 GiB Up to 3.5 Gbps
inf1.2xlarge 1 8 16 GiB Up to 3.5 Gbps
inf1.6xlarge 4 24 48 GiB 3.5 Gbps
inf1.24xlarge 16 96 192 GiB 14 Gbps
5. Amazon Confidential
AWS Graviton2 Processor
Introducing
Enabling the best price/performance for your cloud workloads
Graviton1 Processor Graviton2 Processor
DRAFTCompute
Preview – December 3
L E A R N M O R E CMP322-R: Deep dive on EC2 instances powered by AWS Graviton Wednesday 9:15am, MGM
6. Amazon Confidential
AWS Graviton2 Based Instances
Introducing
Up to 40% better price-performance for general purpose, compute
intensive, and memory intensive workloads.
l
M6g C6g R6g
DRAFT
Built for: General-purpose
workloads such as application
servers, mid-size data stores, and
microservices
Instance storage option: M6gd
Built for: Compute intensive
applications such as HPC, video
encoding, gaming, and simulation
workloads
Instance storage option: C6gd
Built for: Memory intensive
workloads such as open-source
databases, or in-memory caches
Instance storage option: R6gd
Compute
Preview – December 3
L E A R N M O R E CMP322-R: Deep dive on EC2 instances powered by AWS Graviton Wednesday 9:15am, MGM
7. Amazon Confidential
SPEC cpu2017
• Industry standard CPU
intensive benchmark
• Run on all vCPUs concurrently
• Comparing performance/vCPU
* All SPEC scores estimates, compiled with GCC9 -O3 -march=native,
run on largest single socket size for each instance type tested.
40%
60%
80%
100%
120%
140%
160%
SPECint2017 Rate SPECfp2017 rate
Performance/vCPU
SPECcpu2017 Rate*
M5 M6G
DRAFTCompute
8. Amazon Confidential
SPEC jvm2008
• Java VM benchmark
• Run across all vCPUs concurrently
• Comparing performance/vCPU
* All SPEC scores estimates, run with OpenJDK11 and skipping compiler* and startup.* tests
Tests run on largest single-socket instance size for each instance type tested.
40%
60%
80%
100%
120%
140%
160%
Performance/vCPU
SpecJVM*
M5 M6G
DRAFTCompute
9. Amazon Confidential
Load Balancing with Nginx
40%
60%
80%
100%
120%
140%
160%
Performance(Requests/s)
M5 M6G
Load
Balancer
(nginx)
NGINX v1.15.9, 512 clients, 128 GET/POST payloads, all HTTPS connections, AES128-GCM-SHA256,
OpenSSL 1.1.1, 4 target machines, all tests run on 4xl size; load generator c5.9xl; web servers c5.4xls;
All servers run in a cluster placement group
DRAFTCompute
11. Amazon Confidential
Media Encoding with x264
• Huge amount of video created daily
• Encoding it reduces bandwidth to
deliver and storage of that video
• Using libx264 encoder encoded
uncompressed 1080p to h264 40%
60%
80%
100%
120%
140%
160%
Performance(Frames/s)
M5 M6G
DRAFTCompute
12. Amazon Confidential
Amazon Braket
Introducing
Fully managed service that makes it easy for scientists and developers to
explore and experiment with quantum computing.
DRAFTQuantum Technology
Preview – December 2
LEARN MORE CMP213: Introducing Quantum Computing with AWS Wednesday 11:30am, Venetian
13. Amazon Confidential
AWS Compute Optimizer
Introducing
Identify optimal Amazon EC2 instances and EC2 Auto Scaling group
for your workloads using a ML-powered recommendation engine
DRAFTManagement Tools
General Availability – December 3
LEARN MORE CMP323: Optimize Performance and Cost for Your AWS Compute Wednesday, 10:45am, MGM
14. Amazon Confidential
Receive lower rates
automatically. Easy to use
with recommendations in
AWS Cost Explorer
Significant
savings of up to 72%
Flexible across instance family,
size, OS, tenancy or AWS
Region; also applies to AWS
Fargate & soon to AWS
Lambda usage
Compute/Cost Management
LEARN MORE CMP210: Dive deep on Savings Plans Wednesday, 5:30pm
Announced – November 6
Simplify purchasing with a flexible pricing model that offers savings of
up to 72% on Amazon ECS, AWS Fargate & AWS Lambda usage
Savings Plans
15. Amazon Confidential
DRAFTContainers
General Availability – December 3
LEARN MORE CON-326R - Running Kubernetes Applications on AWS Fargate
Wednesday, 4pm, Aria
Thursday, 1:45pm, MGM
Introducing
The only way to run serverless Kubernetes containers securely,
reliably, and at scale
Amazon EKS for AWS Fargate
16. Amazon Confidential
Spare capacity with savings
up to 70% off of Fargate
standard pricing
Improved scalability,
reduced operational cost to
run containers
Containers
New Features
Accelerating momentum for AWS container services
17. Amazon Confidential
Spare capacity with savings
up to 70% off of Fargate
standard pricing
Improved scalability,
reduced operational cost to
run containers
Containers
New Features
Accelerating momentum for AWS container services
18. Amazon Confidential
Spare capacity with savings
up to 70% off of Fargate
standard pricing
Improved scalability,
reduced operational cost to
run containers
Containers
New Features
Accelerating momentum for AWS container services
19. Amazon Confidential
Build and maintain secure OS images more quickly & easily
Introducing
DRAFTCompute
General Availability – December 3
EC2 Image Builder
20. Amazon Confidential
AWS License Manager - Simplified Windows &
SQL Server BYOL
New Feature
DRAFTCompute
General Availability – December 1
• Bring your eligible Windows and SQL BYOL
Licenses to AWS
• Leverage existing licensing investments to save
costs
• Automate ongoing management of EC2
Dedicated Hosts
LEARN MORE
WIN201 - Leadership session: Five New Features of Microsoft and
.NET on AWS that you want to learn
Tuesday, 4pm, MGM
21. Amazon Confidential
Introducing
DRAFTCompute
General Availability – December 1
Helps customers upgrade
legacy applications to run
on newer, supported
versions of Windows Server
without any code changes
Future-proof Reduced risk Cost-effective
Improved security
posture on supported,
new OS
Isolate old runtimes
Compliance with
industry regulations
No application
refactoring or recoding
cost
No extended support
costs
Decouple from
underlying OS
Low risk of failure on
subsequent OS updates
Supports all OS version Reduced operating costs
AWS End of support Migration Program for
Windows Server
23. Amazon Confidential
Amazon S3 Access Points
Introducing
Simplify managing data access at scale for applications using shared data
sets on Amazon S3. Easily create hundreds of access points per bucket,
each with a unique name and permissions customized for each application.
DRAFTStorage
General Availability – December 3
24. Amazon Confidential
EBS Direct APIs for Snapshots
Introducing
A simple set of APIs that provide access to directly read EBS snapshot data, enabling backup providers
to achieve faster backups for EBS volumes at lower costs.
L E A R N M O R E CMP305-R: Amazon EBS snapshots: What’s new, best practices, and security Thursday,1:00pm, MGM
Up to 70% faster
backup times
More granular recovery
point objectives (RPOs)
Lower cost backups
Amazon Confidential
Compute
Easily track incremental
block changes on EBS
volumes to achieve:
General Availability – December 3
26. Amazon Confidential
Amazon Managed Apache Cassandra Service
Introducing
A scalable, highly available, and serverless Apache Cassandra–compatible
database service. Run your Cassandra workloads in the AWS cloud using the
same Cassandra application code and developer tools that you use today.
Apache Cassandra-
compatible
Performance
at scale
Highly available
and secure
No servers
to manage
DRAFTDatabases
Preview – December 3
LEARN MORE DAT324: Overview of Amazon Managed Apache Cassandra Service
27. Amazon Confidential
DRAFTDatabases
Announced – November 26
Amazon Aurora Machine Learning Integration
Simple, optimized, and secure Aurora, SageMaker, and Comprehend (in preview)
integration. Add ML-based predictions to databases and applications using SQL,
without custom integrations, moving data around, or ML experience.
28. Amazon Confidential
Amazon RDS Proxy
Introducing
Fully managed, highly available database proxy feature for Amazon
RDS. Pools and shares connections to make applications more
scalable, more resilient to database failures, and more secure.
DRAFTDatabases
Public Beta – December 3
LEARN MORE DAT368: Setting up database proxy servers with RDS Proxy
29. Amazon Confidential
UltraWarm for Amazon Elasticsearch Service
Introducing
A low cost, scalable warm storage tier for Amazon Elasticsearch Service. Store
up to 10 PB of data in a single cluster at 1/10th the cost of existing storage tiers,
while still providing an interactive experience for analyzing logs.
DRAFTAnalytics
Public Beta – December 3
LEARN MORE ANT229: Scalable, secure, and cost-effective log analytics
30. Amazon Confidential
DRAFTAnalytics
Amazon Redshift RA3 instances with Managed Storage
Optimize your data warehouse costs by paying for compute and storage separately
General Availability – December 3
L E A R N M O R E
ANT213-R1: State of the Art Cloud Data Warehousing
ANT230: Amazon Redshift Reimagined: RA3 and AQUA
Wednesday, 10am, Venetian
Delivers 3x the performance of existing cloud DWs
2x performance and 2x storage as similarly priced
DS2 instances (on-demand)
Automatically scales your DW storage capacity
Supports workloads up to 8PB (compressed)
COMPUTE NODE
(RA3/i3en)
SSD Cache
S 3 S T O R A G E
COMPUTE NODE
(RA3/i3en)
SSD Cache
COMPUTE NODE
(RA3/i3en)
SSD Cache
COMPUTE NODE
(RA3/i3en)
SSD Cache
Managed storage
$/node/hour
$/TB/month
Introducing
31. Amazon Confidential
AQUA (Advanced Query Accelerator) for Amazon Redshift
Introducing
Redshift runs 10x faster than any other cloud data warehouse without increasing cost
DRAFTAnalytics
Private Beta – December 3
LEARN MORE ANT230: Amazon Redshift Reimagined: RA3 and AQUA Wednesday, 10am, Venetian
AQUA brings compute to storage so data doesn't have
to move back and forth
High-speed cache on top of S3 scales out to process
data in parallel across many nodes
AWS designed processors accelerate data compression,
encryption, and data processing
100% compatible with the current version of Redshift
S 3
S T O R A G E
AQUA
ADVANCED QUERY ACCELERATOR
R A 3 C O M P U T E C L U S T E R
32. Amazon Confidential
Amazon Redshift Federated Query
Analyze data across data warehouse, data lakes, and operational
database
New Feature
DRAFTAnalytics
Public Beta – December 3
LEARN MORE ANT213-R1: State of the Art Cloud Data Warehousing Tuesday, 3pm, Bellagio
33. Amazon Confidential
Amazon Redshift Data Lake Export
New Feature
No other data warehouse makes it as easy to gain new insights from
all your data.
DRAFTAnalytics
General Availability – December 3
LEARN MORE
ANT335R: How to build your data analytics stack at scale with Amazon
Redshift
Monday, 7pm, Venetian
Tuesday, 11:30am, Aria
34. Amazon Confidential
AWS Data Exchange
Quickly find diverse data
in one place
Efficiently access
3rd-party data
Easily analyze data
Reach millions of
AWS customers
Easiest way to package and
publish data products
Built-in security and
compliance controls
For
Subscribers
For
Providers
DRAFTAnalytics
Announced – November 13
L E A R N M O R E
ANT238-R: AWS Data Exchange: Easily find & subscribe to third-party
data in the cloud
Thursday, 2:30pm, Venetian
Easily find and subscribe to 3rd-party data in the cloud
36. Amazon Confidential
DRAFTManagement Tools
Announced – November 21
Identify unusual activity in your AWS accounts
Save time sifting through logs
Get ahead of issues before
they impact your business
CloudTrail Insights
Introducing
• Unexpected spikes in resource
provisioning
• Bursts of IAM management
actions
• Gaps in periodic maintenance
activity
L E A R N M O R E MGT420-R: CloudTrail Insights: Identify and Solve Operational Issues
37. Amazon Confidential
AWS Detective
Introducing
Quickly analyze, investigate, and identify the root cause of security
findings and suspicious activities.
Automatically distills
& organizes data into
a graph model
Easy to use visualizations
for faster & effective
investigation
Continuously updated as
new telemetry becomes
available
Preview – December 3
DRAFTSecurity
LEARN MORE SEC312: Introduction to Amazon Detective Thursday, 1:45pm, Venetian
38. Amazon Confidential
AWS IAM Access Analyzer
Introducing
Continuously ensure that policies provide the intended public and cross-account access
to resources, such as Amazon S3 buckets, AWS KMS keys, & AWS Identity and Access
Management roles.
General Availability – December 2
DRAFTSecurity
Uses automated reasoning, a form of
mathematical logic, to determine all possible
access paths allowed by a resource policy
Analyzes new or updated resource
policies to help you understand
potential security implications
Analyzes resource policies for
public or cross-account access
LEARN MORE SEC309: Deep Dive into AWS IAM Access Analyzer Thursday, 3:15pm, Venetian
39. Amazon Confidential
1
Create or use existing identities, including Azure AD, and manage access
centrally to multiple AWS accounts and business applications, for easy
browser, command line, or mobile single sign-on access by employees.
New Feature
AWS Single Sign-On
Announced – November 25
DRAFTSecurity
LEARN MORE SEC308: Manage federated user permissions at scale with AWS SSO Thursday, 12:15pm, Venetian
40. Amazon Confidential
Existing Service
DRAFTNetworking
Scale connectivity across thousands
of Amazon VPCs, AWS accounts,
and on-premises networks
Amazon VPCAmazon VPC
Amazon VPCAmazon VPC
Customer
gateway
VPN
connection
AWS Direct
Connect Gateway
L E A R N M O R E NET203-L Leadership Session Networking Wednesday, 11:30am, MGM
AWS Transit Gateway
41. Amazon Confidential
New Feature
AWS Transit Gateway Inter-Region Peering
General Availability – December 3
DRAFTNetworking
AWS TRANSIT
GATEWAY
Inter-Region Peering
Build global networks by connecting transit gateways across multiple AWS Regions
L E A R N M O R E NET203-L Leadership Session Networking Wednesday, 11:30am, MGM
42. Amazon Confidential
AWS Transit Gateway Network Manager
Introducing General Availability – December 3
DRAFTNetworking
L E A R N M O R E NET212 - AWS Transit Gateway Network Manager
44. Amazon Confidential
New Feature
Transit Gateway Multicast
General Availability – December 3
DRAFTNetworking
Build and deploy multicast applications in the cloud
L E A R N M O R E NET203-L Leadership Session Networking Wednesday, 11:30am, MGM
45. Amazon Confidential
New Feature
Amazon VPC Ingress Routing
General Availability – December 3
DRAFTNetworking
Route inbound and outbound traffic through a third party or AWS service
L E A R N M O R E NET203-L Leadership Session Networking Wednesday, 11:30am, MGM
47. Amazon Confidential
L E A R N M O R E SVS401 - Optimizing your serverless applications
Wednesday, 1:45pm, Mirage
Thursday, 3:15pm, Venetian
Provisioned Concurrency on AWS Lambda
New Feature
• Keeps functions initialized and hyper-ready, ensuring
start times stay in the milliseconds
• Builders have full control over when provisioned
concurrency is set
• No code changes are required to provision concurrency
on functions in production
• Can be combined with AWS Auto Scaling at launch
DRAFTServerless
General Availability – December 3
48. Amazon Confidential
Achieve up to 67% cost reduction and 50% latency reduction compared
to REST APIs. HTTP APIs are also easier to configure than REST APIs,
allowing customers to focus more time on building applications.
Reduce application costs by
up to 67%
Reduce application latency by
up to 50%
Configure HTTP APIs easier
and faster than before
HTTP APIs for Amazon API Gateway
Introducing
DRAFTMobile Services
Preview – December 4
L E A R N M O R E
CON213-L - Leadership session: Using containers and serverless to
accelerate modern application development (incl schema registry demo)
Wednesday 9:15am, Venetian
49. Amazon Confidential
AWS Step Functions Express Workflows
Introducing
Orchestrate AWS compute, database, and messaging services at rates
greater than 100,000 events/second, suitable for high-volume event
processing workloads such as IoT data ingestion, streaming data
processing and transformation.
DRAFTApp Integration
General Availability – December 3
L E A R N M O R E API321: Event-Processing Workflows at Scale with AWS Step Functions Wednesday, 3:15pm, MGM
50. Amazon Confidential
Amazon EventBridge Schema Registry
Introducing
Store event structure - or schema - in a shared central location, so it’s
faster and easier to find the events you need. Generate code bindings
right in your IDE to represent an event as an object in code.
DRAFTApp Integration
Preview – December 3
LEARN MORE
CON213-L - Leadership session: Using containers and serverless to
accelerate modern application development (incl schema registry demo)
Wednesday 9:15am,
Venetian
51. Amazon Confidential
Amplify for iOS & Android
Introducing
DRAFTMobile Services
General Availability – December 3
Open source libraries and toolchain that enable mobile developers to
build scalable and secure cloud powered serverless applications.
L E A R N M O R E MOB317 - Speed up native mobile development with AWS Amplify Wednesday, 11:30am, Venetian
52. Amazon Confidential
Amplify DataStore
New Feature
DRAFTMobile Services
General Availability – December 3
Multi-platform (iOS/Android/React Native/Web) on-device persistent
storage engine that automatically synchronizes data between
mobile/web apps and the cloud using GraphQL.
L E A R N M O R E MOB402: Build data-driven mobile and web apps with AWS AppSync Wednesday, 2:30pm, Mirage
55. Amazon Confidential
What customers are doing with AWS IoT
Remotely monitor
patient health &
wellness applications
Manage energy resources
more efficiently
Enhance safety in
the home, the office,
and the factory floor
Transform transportation with
connected and autonomous
vehicles
Track inventory
levels and manage
warehouse operations
Improve the performance
and productivity of industrial
processes
Build smarter products & user
experiences in homes,
buildings, and cities
Grow healthier crops with
greater efficiencies
56. Amazon Confidential
Alexa Voice Service (AVS) Integration for IoT Core
New Feature
DRAFTInternet of Things
Announced – November 25
Quickly and cost effectively go to market with Alexa built-in capabilities on new categories of products
such as light switches, thermostats, and small appliances.
Accelerate time to market with
certified partner development kits
that work with AVS Integration for IoT
Core by default.
Lowers the cost of integrating Alexa Voice
up to 50% by reducing the compute and
memory footprint required
Build new categories of Alexa Built-in
products on resource constrained devices
(e.g. ARM ‘M' class microcontrollers with
<1MB embedded RAM).
57. Amazon Confidential
Container Support for AWS IoT Greengrass
New Feature
DRAFTInternet of Things
Announced – November 25
Deploy containers seamlessly to edge devices
Move containers from the cloud
to edge devices using AWS IoT
Greengrass, without rewriting
any code.
Enables both Docker & AWS
Lambda components to
operate seamlessly together at
the edge
Use AWS IoT Greengrass Secrets
Manager to manage credentials
for private container registries.
58. Amazon Confidential
AWS Outposts
Now Available
Fully managed service that extends AWS infrastructure, AWS services, APIs, and tools to virtually any
connected customer site. Truly consistent hybrid experience for applications across on-premises and
cloud environments. Ideal for low latency or local data processing application needs.
Same AWS-designed infrastructure
as in AWS regional data centers
(built on AWS Nitro System)
delivered to customer facilities
Fully managed, monitored, and
operated by AWS
as in AWS Regions
Single pane of management
in the cloud providing the
same APIs and tools as
in AWS Regions
Compute
General Availability – December 3
LEARN MORE
CMP302-R: AWS Outposts: Extend the AWS experience to on-premises
environments
Wednesday at 11:30am, Aria
Thursday at 3:15pm, Mirage
Friday at 10:45am, Mirage
60. Amazon Confidential
Local Zones
Introducing
Extend the AWS Cloud to more locations and closer to your end-users
to support ultra low latency application use cases. Use familiar AWS
services and tools and pay only for the resources you use.
DRAFTCompute
General Availability – December 3
The first Local Zone to be released will be located in Los Angeles.
61. Amazon Confidential
AWS Wavelength
Introducing
Embeds AWS compute and storage inside telco providers’ 5G
networks. Enables mobile app developers to deliver applications with
single-digit millisecond latencies. Pay only for the resources you use.
DRAFTCompute
Announcement – December 3
62. Amazon Confidential
AWS Wavelength
Introducing
Embeds AWS compute and storage inside telco providers’ 5G
networks. Enables mobile app developers to deliver applications with
single-digit millisecond latencies. Pay only for the resources you use.
DRAFTCompute
Announcement – December 3
66. Pre:Invent highlights
https://aws.amazon.com/about-aws/whats-new/machine-learning
• Amazon Comprehend: 6 new languages
• Amazon Translate: 22 new languages
• Amazon Transcribe: 15 new languages, alternative transcriptions
• Amazon Lex: SOC compliance, sentiment analysis,
web & mobile integration with Amazon Connect
• Amazon Personalize: batch recommendations
• Amazon Forecast: use any quantile for your predictions
With region expansion across the board!
70. Introducing Amazon Rekognition Custom Labels
• Import images labeled by Amazon
SageMaker Ground Truth…
• Or label images automatically based on folder structure
• Train a model on fully managed
infrastructure
• Split the data set for training and validation
• See precision, recall, and F1 score at the end of training
• Select your model
• Use it with the usual Rekognition APIs
72. Customers are forced to choose
ML only systems are high speed and low
cost, but do not support nuanced decision
making
Human only workflows offer nuanced
decision making, but they’re low speed and
high cost.
OR
74. A2I lets you easily implement human review in
machine learning workflows to improve the accuracy,
speed, and scale of complex decisions.
Introducing Amazon Augmented AI (A2I)
75. How Amazon Augmented AI works
Client application
sends input data
AWS AI Service or
custom ML model
makes predictions
Results stored
to your S3
1 2
4
Low confidence predictions
sent for human review
3
High-confidence predictions
returned immediately to client
application
5
Amazon Rekognition
Amazon Textract
76. Human Review Workforces
Amazon Mechanical Turk
An on-demand 24x7 workforce
of over 500,000 independent
contractors worldwide, powered
by Amazon Mechanical Turk
Private
A team of workers that you have
sourced yourself, including your
own employees or contractors
for handling data that needs to
stay within your organization
Vendors
A curated list of third-party
vendors that specialize in
providing data labeling services,
available via de AWS Marketplace
78. Fraud detection is difficult
$$$ billions lost to
fraud each year
Online business prone
to fraud attacks
Bad actors often
change tactics
Changing rules =
more human reviews
Dependent on others to
update detection logic
79. Fraud detection with ML is also difficult
Top data scientists are
costly & hard to find
One-size-fits-all models
underperform
Often need to
supplement data
Data transformation +
feature engineering
Fraud imbalance =
needle in a haystack
80. Introducing Amazon Fraud Detector
A fraud detection service that makes
it easy for businesses to use machine
learning to detect online fraud in
real-time, at scale
81. Amazon Fraud Detector – Key Features
Pre-built fraud
detection model
templates
Automatic
creation of
custom fraud
detection
models
Models learn
from past
attempts to
defraud Amazon
Amazon
SageMaker
integration
One interface to
review past
evaluations and
detection logic
85. Challenges in contact centers
• Better visibility into quality of customer interactions
• Cost prohibitive
•
• Timely discovery of emerging issues
• Support for live calls
• End user experience
86. Introducing Contact Lens For Amazon Connect
Theme
detection
Built-in automatic
call transcription
Automated
contact
categorization
Enhanced
Contact Search
Real-time sentiment
dashboard
and alerting
Presents
recurring
issues based
on
Customer
feedback
Identify call types
such as script
compliance,
competitive
mentions,
and cancellations.
Filter calls of
interest based
on words
spoken and
customer
sentiment
View entire call
transcript directly in
Amazon Connect
Quickly identify
when customers
are having a
poor experience
on live calls
Easily use the power of machine learning to improve the quality of your customer experience
without requiring any technical expertise
90. Typical Application Build and Run Process
Write +
Review
Build +
Test
Deploy Measure Improve
1. Code Reviews require expertise in multiple areas such as
knowledge of AWS APIs, Concurrency, etc.
2. Code analyzer tools require high accuracy.
3. Distributed Cloud application are difficult to optimize.
4. Performance engineering expertise is hard to find.
91. Introducing AWS CodeGuru
Built-in code reviews
with intelligent
recommendations
Detect and optimize
expensive lines of
code before
production
Easily identify latency
and performance
improvements
production
environment
CodeGuru Reviewer CodeGuru Profiler
92. CodeGuru Reviewer: How It Works
Input:
Source Code
Feature Extraction Machine Learning
Output:
Recommendations
Customer provides source
code as input
Java
AWS CodeCommit
Github
Extract semantic features /
patterns
ML algorithms identify similar
code for comparison
Customers see
recommendations as Pull
Request feedback
93. CodeGuru Example – Looping vs Waiting
do {
DescribeTableResult describe = ddbClient.describeTable(new DescribeTableRequest().withTableName(tableName));
String status = describe.getTable().getTableStatus();
if (TableStatus.ACTIVE.toString().equals(status)) {
return describe.getTable();
}
if (TableStatus.DELETING.toString().equals(status)) {
throw new ResourceInUseException("Table is " + status + ", and waiting for it to become ACTIVE is not useful.");
}
Thread.sleep(10 * 1000);
elapsedMs = System.currentTimeMillis() - startTimeMs;
} while (elapsedMs / 1000.0 < waitTimeSeconds);
throw new ResourceInUseException("Table did not become ACTIVE after ");
This code appears to be waiting for a resource before it runs. You could use the waiters feature to help improve
efficiency. Consider using TableExists, TableNotExists. For more information,
see https://aws.amazon.com/blogs/developer/waiters-in-the-aws-sdk-for-java/
Recommendation
Code
We should use waiters instead - will help remove a lot of this code.Developer Feedback
94. CodeGuru Profiler: How It Works
Input:
Live application stack
trace
Application profile
sampling
Pattern matching
Output:
Method names,
Recommendations
and searchable
visualizations
Customer application
runs in production
CodeGuru Profiler
continuously captures
application stack trace
information
CodeGuru Profiler detects
performance inefficiencies in
the live application
Customers see
recommendations in their
automated efficiency reports
and visualizations
Amazon Confidential
98. Employees spend 20% of their
time looking for information.
—McKinsey
20%
44%44% of the time, they cannot
find the information they need to
do their job.
—IDC
99. Introducing Kendra
Easy to find what you are
looking for
Fast search, and
quick to set up
Native connectors
(S3, Sharepoint,
file servers,
HTTP, etc.)
Natural language
Queries
NLU and
ML core
Simple API
and console
experiences
Code samples
Incremental
learning through
feedback
Domain
Expertise
101. Getting started with Kendra
Step 1
Create an index
An index is the place where
you add your data sources
to make them searchable
in Kendra.
Step 2
Add data sources
Add and sync your data
from S3, Sharepoint, Box
and other data sources, to
your index.
Step 3
Test & deploy
After syncing your data,
visit the Search console
page to test search &
deploy Kendra in your
search application.
105. Using Kubernetes for ML is hard to
manage and scale
Build and manage services
within Kubernetes cluster for ML
Make disparate open-source
libraries and frameworks work
together in a secure and
scalable way
Requires time and expertise from
infrastructure, data science, and
development teams
Need an easier way to use
Kubernetes for ML
+
+
=
106. Fully managed
infrastructure in SageMaker
Introducing Amazon SageMaker Operators for Kubernetes
Kubernetes customers can now train, tune, & deploy models in
Amazon SageMaker
107. Machine learning is iterative involving
dozens of tools and hundreds of
iterations
Multiple tools needed for
different phases of the
ML workflow
Lack of an integrated
experience
Large number of iterations
Cumbersome, lengthy processes, resulting in
loss of productivity
+
+
=
108. Introducing Amazon SageMaker Studio
The first fully integrated development environment (IDE) for machine learning
Organize, track, and
compare thousands of
experiments
Easy experiment
management
Share scalable notebooks
without tracking code
dependencies
Collaboration at
scale
Get accurate models for
with full visibility & control
without writing code
Automatic model
generation
Automatically debug errors,
monitor models, & maintain
high quality
Higher quality ML
models
Code, build, train, deploy, &
monitor in a unified visual
interface
Increased
productivity
109.
110. Data science and collaboration
needs to be easy
Setup and manage resources
Collaboration across
multiple data scientists
Different data science
projects have different
resource needs
Managing notebooks and
collaborating across
multiple data scientists is
highly complicated
+
+
=
111. Introducing Amazon SageMaker Notebooks
Access your notebooks in
seconds with your corporate
credentials
Fast-start shareable notebooks
Administrators manage
access and permissions
Share your notebooks
as a URL with a single click
Dial up or down
compute resources
Start your notebooks
without spinning up
compute resources
112. Data Processing and
Model Evaluation involves a lot of
operational overhead
Building and scaling infrastructure
for data processing workloads is
complex
Use of multiple tools or services
implies learning and
implementing new APIs
All steps in the ML workflow need
enhanced security, authentication
and compliance
Need to build and manage tooling
to run large data processing and
model evaluation workloads
+
+
=
113. Introducing Amazon SageMaker Processing
Analytics jobs for data processing and model evaluation
Use SageMaker’s built-in
containers or bring your own
Bring your own script for
feature engineering
Custom processing
Achieve distributed
processing for clusters
Your resources are created,
configured, & terminated
automatically
Leverage SageMaker’s
security & compliance
features
114. Managing trials and experiments is
cumbersome
Hundreds of experiments
Hundreds of parameters
per experiment
Compare and contrast
Very cumbersome and
error prone
+
+
=
115. Introducing Amazon SageMaker Experiments
Experiment
tracking at scale
Visualization for
best results
Flexibility with
Python SDK & APIs
Iterate quickly
Track parameters & metrics
across experiments & users
Organize
experiments
Organize by teams, goals, &
hypotheses
Visualize & compare
between experiments
Log custom metrics &
track models using APIs
Iterate & develop high-
quality models
A system to organize, track, and evaluate training experiments
116. Debugging and profiling
deep learning is painful
Large neural networks
with many layers
Many connections
Additional tooling for analysis
and debug
Extraordinarily difficult
to inspect, debug, and profile
the ‘black box’
+
+
=
117. Automatic data
analysis
Relevant data
capture
Automatic error
detection
Improved productivity
with alerts
Visual analysis
and debug
Introducing Amazon SageMaker Debugger
Analyze and debug data
with no code changes
Data is automatically
captured for analysis
Errors are automatically
detected based on rules
Take corrective action based
on alerts
Visually analyze & debug
from SageMaker Studio
Analysis & debugging, explainability, and alert generation
118. Deploying a model is not the end, you
need to continuously monitor it in
production and iterate
Concept drift due to
divergence of data
Model performance can
change due to unknown
factors
Continuous monitoring of model
performance and data involves a
lot of effort and expense
Model monitoring is
cumbersome but critical
+
+
=
119. Successful ML requires
complex, hard to discover
combinations
Largely explorative &
iterative
Requires broad and
complete
knowledge of ML domain
Lack of visibility
Time consuming,
error prone process
even for ML experts
+
+
=
of algorithms, data, parameters
120. Introducing Amazon SageMaker Autopilot
Quick to start
Provide your data in a
tabular form & specify target
prediction
Automatic
model creation
Get ML models with feature
engineering & automatic model
tuning automatically done
Visibility & control
Get notebooks for your
modelswith source code
Automatic model creation with full visibility & control
Recommendations &
Optimization
Get a leaderboard & continue
to improve your model
122. Introducing Amazon SageMaker Model Monitor
Automatic data
collection
Continuous
Monitoring
CloudWatch
Integration
Data is automatically
collected from your
endpoints
Automate corrective
actions based on Amazon
CloudWatch alerts
Continuous monitoring of models in production
Visual
Data analysis
Define a monitoring
schedule and detect
changes in quality against
a pre-defined baseline
See monitoring results,
data statistics, and
violation reports in
SageMaker Studio
Flexibility
with rules
Use built-in rules to
detect data drift or write
your own rules for
custom analysis
123. AWS DeepRacer improvements
• AWS DeepRacer Evo
• Stereo camera
• LIDAR sensor
• New racing opportunities
• Create your own races
• Object Detection & Avoidance
• Head-to-head racing
124. AWS DeepComposer
• The world’s first machine
learning-enabled musical
keyboard
• Compose music using Generative
Adversarial Networks (GAN)
• Use a pretrained model, or train
your own
Up to 16 chips
100GBPS networking
3x more throughput + 40% lower cost / inference than G4
Using Inf1 instances, customers can run large scale machine learning inference applications like image recognition, speech recognition, natural language processing, personalization, and fraud detection, at the lowest cost in the cloud.
x64 vs ARM
Amazon Braket helps overcome these challenges by providing a service that lets developers, researchers, and scientists explore, evaluate, and experiment with quantum computing. Amazon Braket lets you design your own quantum algorithms from scratch or choose from a set of pre-built algorithms. Once you define your algorithm, Amazon Braket provides a fully managed simulation service to help troubleshoot and verify your implementation.
When you are ready, you can run your algorithm on your choice of different quantum computers, including gate based superconductor computers from Rigetti, quantum annealing superconductor computers from D-Wave, and ion trap computers from IonQ
ADD NOTES!!!
Analyses under and over provisioned capacity
Only recommends
Explain difference between RI and SP
Fargate profile ->
Subnets
Iam profile/role
K8s namespace
Mention eks price drop to 10c/h
There is a maximum of 4 vCPU and 30Gb memory per pod.
Currently there is no support for stateful workloads that require persistent volumes or file systems.
You cannot run Daemonsets, Privileged pods, or pods that use HostNetwork or HostPort.
The only load balancer you can use is an Application Load Balancer.
Capacity providers: run across fargate + fargate spot, increase ASG capacity, or balance over Azs
ensuring that a service runs an equal number of tasks in multiple availability zones without requiring the service to rebalance.
CLI v2 – more defaults, less ECS primitives
Only a few commands to spin up and deploy an ECS cluster + CI/CD pipeline (and a test env)
ECS Clustering autoscaling
ASG -> need more capacity for containers, scale out please
Test in console!!!!
Set limits on licenses (EG Cores) and then see what you are using
Allows you to pick the BYOL as MyAMI when launching
2003, 2008, and 2008 R2
AWS Partner/Professional Services assesses
Then package
Then migrate
Multiple access points instead of complex bucket policy
Limit access to specific accounts IDs
Can enforce with SCP
Access point name unique per account, so have the same name everywhere
Read diff directly without having to use snapshot to create EBS
Uses SageMaker + Comprehend
Pull data from DB
Hot (indexing / updating / fast access to data)
UltraWarm stores on S3, uses Nitro for nodes that cache, prefetch, query data
Local SSDs for cache
Custom AWS processor / FPGA, speeds up filtering and aggregation closer to the data (s3)
Across Aurora + RDS (postgres only), S3 and Redshift
Save to s3 in Parquet format from a Redshift Query
Talk about DMS + this
Alerts on spikes in activity (creation, IAM, etc)
VPC flow log, CloudTrail, GuardDuty -> visualize and drill down
Within 30mins for a new resource / change
Looks who outside the account can access
Analyses in the same region, so enable for all regions -> plug the SCP to lock down regions
S3, IAM Role, KMS, Lambdas & layers, SQS
Integrates with Salesforce, Box, Office365
Hub and Spoke – single point to connect your vpn / DirectConnect
Peer Transits in regions
Before: VPCs using peering or use PrivateLink
Multicast domains – routing rules
Visualise network health
Multicast for media apps – first cloud
Via Transit gateways
Segment network into domain to broadcast to
Transit gateway act like multiple multicast routers
Send to list of computers
Route to ENI (appliance via marketplace)
Begins execution in Double digit milliseconds
Schedule scaling and target tracking (CloudWatch metric, scale based on this)
Simpler and cheaper ($1/M vs $3.5/M)
Faster
>100,000 events systems
Instead of adding the SDK and building with services, use Amplify to set up use-case centric ”I want to do X” projects
Add Alexa to devices by offloading the work to virtual device in the cloud for
media retrieval, audio decoding, audio mixing, and state management
Run lambdas and containers on IoT edge devices
Can use Secrets manager to store credentials for e.g. Docker Hub to pull image
5g outposts, run single digit latencies
HITECH act – up to 6h extra per day to capture data
Currently need to call out “full stop” or “semi-colon”
Highly accurate, just send audio, no provisioning
Train recognition with own images “turbo charger” / “spark plug”
Upload fraud data sets, build model, then call fraud api from your app
CodeGuru Reviewer detects and flags wide-ranging issues in source code such as thread safety issues, use of un-sanitized inputs, inappropriate handling of sensitive data, and resource leaks. It also detects deviation from best practices for using AWS APIs and SDKs, flagging common issues that can lead to production issues, such as detection of missing pagination or error handling with batch operations.
Kubectl -> SageMaker
Before, had to create, manage and scale infrastructure
Now, use SageMaker
IDE for building, testing and running ML models
Share notebook along with infra to run with people
Visualize the training and compare
No startup time
Converting the data set to the input format expected by the ML algorithm you’re using,
Transforming existing features to a more expressive representation, such as one-hot encoding categorical features,
Rescaling or normalizing numerical features,
Engineering high level features, e.g. replacing mailing addresses with GPS coordinates,
Cleaning and tokenizing text for natural language processing applications,
Use a container to pre-process the data (run wherever you want, ECS, EKS, or part of SageMaker Processing)
Then store on S3
Same for post-processing or evaluating your model with test data
Keeps track by grouping inputs, params, configs and results in experiments
From inside SageMaker Studio
Can also use the SDK to run from your notebooks, then visually compare
Visualises inside SageMaker studio, and can send alerts to devs when detecting anomalies
Give tabular data, point to output column, then hit go
Figures out which algorithm to use
Trained with data, then input starts changing over time
Detects that there is drift / lower accuracy, notifies dev
analyze the data collected in production, and compare it against your training or validation data to detect deviations