AWS Named as a Leader in the 2022 Gartner Cloud Infrastructure & Platform Services (CIPS) Magic Quadrant for the 12th Consecutive Year

This year, and for the twelfth consecutive year, AWS has been named as a Leader in the 2022 Magic Quadrant for Cloud Infrastructure and Platform Services (CIPS). Per Gartner, AWS is the longest-running CIPS Magic Quadrant Leader.

AWS was among the first cloud providers when we launched Amazon Elastic Compute Cloud (Amazon EC2) and Amazon Simple Storage Service (Amazon S3) 16 years ago. Our APIs have been adopted by the whole industry and often copied by others.

We believe this report validates AWS’s ability to innovate and deliver the broadest and deepest set of services for cloud computing. I encourage you to read the full report to appreciate the details.

As Jeff Bezos wrote in his first letter to shareholders in 1997 (reprinted at the end of each annual letter since then), Amazon makes decisions and weighs trade-offs differently than some companies. We focus on the long-term value rather than short-term profits, we make bold rather than timid investment decisions, and most importantly, we relentlessly focus on you: our customers. As a matter of fact, 90 percent of AWS’s roadmap for new services and capabilities is directly driven by your feedback and requests.

I work with AWS service teams every day. These teams work hard to innovate on your behalf. They make bold investments to invent, build, and operate services that help you innovate and build amazing experiences for your customers. The entire team is proud to see these efforts recognized by Gartner.

Our teams closely work with the vibrant AWS Partner Network. AWS has the largest and most dynamic community, with millions of active customers every month and more than 100,000 partners from over 150 countries—with almost 70% headquartered outside the United States. There is a real network effect when you use AWS.

The full Gartner report has details about the features and factors they reviewed. It explains the methodology used and the results. This report can serve as a guide when choosing a cloud provider that helps you innovate on behalf of your customers.

-- seb

Gartner, Magic Quadrant for Cloud Infrastructure and Platform Services, 19 October 2022, Raj Bala, et. al. The Magic Quadrant graphic was published by Gartner, Inc. as part of a larger research document and should be evaluated in the context of the entire document. The Gartner document is available upon request from AWS. Gartner does not endorse any vendor, product or service depicted in our research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose. Gartner and Magic Quadrant are registered trademarks of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved.

 

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Introducing Amazon Neptune Serverless – A Fully Managed Graph Database that Adjusts Capacity for Your Workloads

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How Vendors can use Selling Partner APIs to increase their performance

Amazon posted a guide for SP-API and Vendors and Openbridge has released code-free Amazon Vendor Central Software that allows vendors direct access to this API, fully automated.

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AWS Batch for Amazon Elastic Kubernetes Service

Today I’m pleased to announce AWS Batch for Amazon Elastic Kubernetes Service (Amazon EKS). AWS Batch for Amazon EKS is ideal for customers who no longer want to shoulder the burden of configuring, fine-tuning, and managing Kubernetes clusters and pods to use with their batch processing workflows. Furthermore, there is no charge for this service. You only pay for the resources that your batch jobs launch.

When I’ve previously considered Kubernetes, it appeared to be focused on the management and hosting of microservice workloads. I was therefore surprised to discover that Kubernetes is also used by some customers to run large-scale, compute-intensive batch workloads. The differences between batch and microservice workloads mean that using Kubernetes for batch processing can be difficult and requires you to invest significant time in custom configuration and management to fine-tune a suitable solution.

Microservice and batch workloads on Kubernetes
Before we look further at AWS Batch for Amazon EKS, let’s consider some of the important differences between batch and microservice workloads to help set some context on why running batch workloads on Kubernetes can be difficult:

• Microservice workloads are assumed to start and not stop—we expect them to be continuously available. In contrast, batch workloads run to completion and then exit—regardless of success or failure.
• The results from a batch workload might not be available for several minutes—and sometimes hours or even days. Microservice workloads are expected to respond to requests within milliseconds.
• We usually deploy microservice workloads across several Availability Zones to ensure high availability. This isn’t a requirement for batch workloads. Although we might distribute a batch job to allow it to process different input data in a distributed analysis, we more typically want to prioritize fast and optimal access to resources the job needs within the Availability Zone in which it is running.
• Microservice and batch workloads scale differently. For microservices, scaling is generally predictable and usually linear as load increases (or decreases). With batch workloads, you might first perform an initial, or infrequently repeated, proof-of-concept run to analyze performance and discover the correct tuning needed for a full production run. The difference in size between the two can be exponential. Furthermore, with batch workloads, we might scale to an extreme level for a run, then scale back to zero instances for long periods of time, sometimes months.

Although third-party frameworks can help with running batch workloads on Kubernetes, you can also roll your own. Whichever approach you take, significant gaps and challenges can remain in handling the undifferentiated heavy lifting of building, configuring, and maintaining custom batch solutions. Then you also need to consider the scheduling, placing, and scaling of batch workloads on Kubernetes in a cost-effective manner. So how does AWS Batch on Amazon EKS help?

AWS Batch for Amazon EKS
AWS Batch for Amazon EKS offers a fully managed service to run batch workloads using clusters hosted on Amazon Elastic Compute Cloud (Amazon EC2) with no need to install and manage complex, custom batch solutions to address the differences highlighted earlier. AWS Batch provides a scheduler that controls and runs high-volume batch jobs, together with an orchestration component that evaluates when, where, and how to place jobs submitted to a queue. There’s no need for you, as the user, to coordinate any of this work—you just submit a job request into the queue.

Job queueing, dependency tracking, retries, prioritization, compute resource provisioning for Amazon Elastic Compute Cloud (EC2) and Amazon Elastic Compute Cloud (EC2) Spot, and pod submission are all handled using a serverless queue. As a managed service, AWS Batch for Amazon EKS enables you to reduce your operational and management overhead and focus instead on your business requirements. It provides integration with other services such as AWS Identity and Access Management (IAM), Amazon EventBridge, and AWS Step Functions and allows you to take advantage of other partners and tools in the Kubernetes ecosystem.

When running batch jobs on Amazon EKS clusters, AWS Batch is the main entry point to submit workload requests. Based on the queued jobs, AWS Batch then launches worker nodes in your cluster to process the jobs. These nodes are kept separate in a distinct namespace from your other node groups in Amazon EKS. Similarly, nodes in other pods are isolated from those used with AWS Batch.

How it works
AWS Batch uses managed Amazon EKS clusters, which need to be registered with AWS Batch, and permissions set so that AWS Batch can launch and manage compute environments in those clusters to process jobs submitted to the queue. You can find instructions on how to launch a managed cluster that AWS Batch can use in this topic in the Amazon EKS User Guide. Instructions for configuring permissions can be found in the AWS Batch User Guide.

Once one or more clusters have been registered, and permissions set, users can submit jobs to the queue. When a job is submitted, the following actions take place to process the request:

• On receiving a job request, the queue dispatches a request to the configured compute environment for resources. If an AWS Batch managed scaling group does not yet exist, one is created, and AWS Batch then starts launching Amazon Elastic Compute Cloud (EC2) instances in the group. These new instances are added to the AWS Batch Kubernetes namespace of the cluster.
• The Kubernetes scheduler places any configured DaemonSet on the node.
• Once the node is ready, AWS Batch starts sending pod placement requests to your cluster, using labels and taints to make the placement choices for the pods, bypassing much of the logic of the k8s scheduler.
• This process is repeated, scaling as needed across more EC2 instances in the scaling group until the maximum configured capacity is reached.
• If the job queue has another compute environment defined, such as one configured to use Spot instances, it will launch additional nodes in that compute environment.
• Once all work is complete, AWS Batch removes the nodes from the cluster, and terminates the instances.

These steps are illustrated in the animation below.

Start using your clusters with AWS Batch today
AWS Batch for Amazon Elastic Kubernetes Service (Amazon EKS) is available today. As I noted earlier, there is no charge for this service, and you pay only for the resources your jobs consume. To learn more, visit the Getting Started with Amazon EKS topic in the AWS Batch User Guide. There is also a self-guided workshop to help introduce you to AWS Batch on Amazon EKS.

— Steve

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AWS Week in Review – October 24, 2022

Last week, we announced plans to launch the AWS Asia Pacific (Bangkok) Region, which will become our third AWS Region in Southeast Asia. This Region will have three Availability Zones and will give AWS customers in Thailand the ability to run workloads and store data that must remain in-country.

In the Works – AWS Region in Thailand
With this big news, AWS announced a 190 billion baht (US 5 billion dollars) investment to drive Thailand’s digital future over the next 15 years. It includes capital expenditures on the construction of data centers, operational expenses related to ongoing utilities and facility costs, and the purchase of goods and services from Regional businesses.

Since we first opened an office in Bangkok in 2015, AWS has launched 10 Amazon CloudFront edge locations, a highly secure and programmable content delivery network (CDN) in Bangkok. In 2020, we launched AWS Outposts, a family of fully managed solutions delivering AWS infrastructure and services to virtually any on-premises or edge location for a truly consistent hybrid experience in Thailand. This year, we also plan the upcoming launch of an AWS Local Zone in Bangkok, which will enable customers to deliver applications that require single-digit millisecond latency to end users in Thailand.

Photo courtesy of Conor McNamara, Managing Director, ASEAN at AWS

The new AWS Region in Thailand is also part of our broader, multifaceted investment in the country, covering our local team, partners, skills, and the localization of services, including Amazon Transcribe, Amazon Translate, and Amazon Connect.

Many Thailand customers have chosen AWS to run their workloads to accelerate innovation, increase agility, and drive cost savings, such as 2C2P, CP All Plc., Digital Economy Promotion Agency, Energy Response Co. Ltd. (ENRES), PTT Global Public Company Limited (PTT), Siam Cement Group (SCG), Sukhothai Thammathirat Open University, The Stock Exchange of Thailand, Papyrus Studio, and more.

For example, Dr. Werner Vogels, CTO of Amazon.com, introduced the story of Papyrus Studio, a large film studio and one of the first customers in Thailand.

“Customer stories like Papyrus Studio inspire us at AWS. The cloud can allow a small company to rapidly scale and compete globally. It also provides new opportunities to create, innovate, and identify business opportunities that just aren’t possible with conventional infrastructure.”

For more information on how to enable AWS and get support in Thailand, contact our AWS Thailand team.

Last Week’s Launches
My favorite news of last week was to launch dark mode as a beta feature in the AWS Management Console. In Unified Settings, you can choose between three settings for visual mode: Browser default, Light, and Dark. Browser default applies the default dark or light setting of the browser, dark applies the new built-in dark mode, and light maintains the current look and feel of the AWS console. Choose your favorite!

Here are some launches that caught my eye for web, mobile, and IoT application developers:

New AWS Amplify Library for Swift – We announce the general availability of Amplify Library for Swift (previously Amplify iOS). Developers can use Amplify Library for Swift via the Swift Package Manager to build apps for iOS and macOS (currently in beta) platforms with Auth, Storage, Geo, and more features.

The Amplify Library for Swift is open source on GitHub, and we deeply appreciate the feedback we have gotten from the community. To learn more, see Introducing the AWS Amplify Library for Swift in the AWS Front-End Web & Mobile Blog or Amplify Library for Swift documentation.

New Amazon IVS Chat SDKs – Amazon Interactive Video Service (Amazon IVS) now provides SDKs for stream chat with support for web, Android, and iOS. The Amazon IVS stream chat SDKs support common functions for chat room resource management, sending and receiving messages, and managing chat room participants.

Amazon IVS is a managed, live-video streaming service using the broadcast SDKs or standard streaming software such as Open Broadcaster Software (OBS). The service provides cross-platform player SDKs for playback of Amazon IVS streams you need to make low-latency live video available to any viewer around the world. Also, it offers Chat Client Messaging SDK. For more information, see Getting Started with Amazon IVS Chat in the AWS documentation.

New AWS Parameters and Secrets Lambda Extension – This is new extension for AWS Lambda developers to retrieve parameters from AWS Systems Manager Parameter Store and secrets from AWS Secrets Manager. Lambda function developers can leverage this extension to improve their application performance as it decreases the latency and the cost of retrieving parameters and secrets.

Previously, you had to initialize either the core library of a service or the entire service SDK inside a Lambda function for retrieving secrets and parameters. Now you can simply use the extension. To learn more, see AWS Systems Manager Parameter Store documentation and AWS Secrets Manager documentation.

New FreeRTOS Long Term Support Version – We announce the second release of FreeRTOS Long Term Support (LTS) – FreeRTOS 202210.00 LTS. FreeRTOS LTS offers a more stable foundation than standard releases as manufacturers deploy and later update devices in the field. This release includes new and upgraded libraries such as AWS IoT Fleet Provisioning, Cellular LTE-M Interface, coreMQTT, and FreeRTOS-Plus-TCP.

All libraries included in this FreeRTOS LTS version will receive security and critical bug fixes until October 2024. With an LTS release, you can continue to maintain your existing FreeRTOS code base and avoid any potential disruptions resulting from FreeRTOS version upgrades. To learn more, see the FreeRTOS announcement.

Here is some news on performance improvement and increasing capacity:

Up to 10X Improving Amazon Aurora Snapshot Exporting Speed – Amazon Aurora MySQL-Compatible Edition for MySQL 5.7 and 8.0 now speed up to 10x faster snapshot exports to Amazon S3. The performance improvement is automatically applied to all types of database snapshot exports, including manual snapshots, automated system snapshots, and snapshots created by the AWS Backup service. For more information, see Exporting DB cluster snapshot data to Amazon S3 in the Amazon Aurora documentation.

3X Increasing Amazon RDS Read Capacity – Amazon Relational Database Service (RDS) for MySQL, MariaDB, and PostgreSQL now supports 15 read replicas per instance, including up to 5 cross-Region read replicas, delivering up to 3x the previous read capacity. For more information, see Working with read replicas in the Amazon RDS documentation.

2X Increasing AWS Snowball Edge Compute Capacity – The AWS Snowball Edge Compute Optimized device doubled the compute capacity up to 104 vCPUs, doubled the memory capacity up to 416GB RAM, and is now fully SSD with 28TB NVMe storage. The updated device is ideal when you need dense compute resources to run complex workloads such as machine learning inference or video analytics at the rugged, mobile edge such as trucks, aircraft or ships.  You can get started by ordering a Snowball Edge device on the AWS Snow Family console.

2X Increasing Amazon SQS FIFO Default Quota – Amazon Simple Queue Service (SQS) announces the increase of default quota up to 6,000 transactions per second per API action. It is double the previous 3,000 throughput quota for a high throughput mode for FIFO (first in, first out) queues in all AWS Regions where Amazon SQS FIFO queue is available. For a detailed breakdown of default throughput quotas per Region, see Quotas related to messages in the Amazon SQS documentation.

For a full list of AWS announcements, be sure to keep an eye on the What’s New at AWS page.

Other AWS News
Here are some other news items that you may find interesting:

22 New or Updated Open Datasets on AWS – We released 22 new or updated datasets, including Amazonia-1 imagery, Bitcoin and Ethereum data, and elevation data over the Arctic and Antarctica. The full list of publicly available datasets is on the Registry of Open Data on AWS and is now also discoverable on AWS Data Exchange.

Sustainability with AWS Partners (ft. AWS On Air) – This episode covers a broad discipline of environmental, social, and governance (ESG) issues across all regions, organization types, and industries. AWS Sustainability & Climate Tech provides a comprehensive portfolio of AWS Partner solutions built on AWS that address climate change events and the United Nation’s Sustainable Development Goals (SDG).

AWS Open Source News and Updates #131 – This newsletter covers latest open-source projects such as Amazon EMR Toolkit for VS Code, a VS Code Extension to make it easier to develop Spark jobs on EMR and AWS CDK For Discourse, sample codes that demonstrates how to create a full environment for Discourse, etc. Remember to check out the Open source at AWS keep up to date with all our activity in open source by following us on @AWSOpen.

Upcoming AWS Events
Check your calendars and sign up for these AWS events:

AWS re:Invent 2022 Attendee Guide – Browse re:Invent 2022 attendee guides, curated by AWS Heroes, AWS industry teams, and AWS Partners. Each guide contains recommended sessions, tips and tricks for building your agenda, and other useful resources. Also, seat reservations for all sessions are now open for all re:Invent attendees. You can still register for AWS re:Invent either offline or online.

AWS AI/ML Innovation Day on October 25 – Join us for this year’s AWS AI/ML Innovation Day, where you’ll hear from Bratin Saha and other leaders in the field about the great strides AI/ML has made in the past and the promises awaiting us in the future.

AWS Container Day at Kubecon 2022 on October 25–28 – Come join us at KubeCon + CloudNativeCon North America 2022, where we’ll be hosting AWS Container Day Featuring Kubernetes on October 25 and educational sessions at our booth on October 26–28. Throughout the event, our sessions focus on security, cost optimization, GitOps/multi-cluster management, hybrid and edge compute, and more.

You can browse all upcoming in-person, and virtual events.

That’s all for this week. Check back next Monday for another Week in Review!

— Channy

This post is part of our Week in Review series. Check back each week for a quick roundup of interesting news and announcements from AWS!

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Looker Studio + Amazon Seller Central

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AWS Week in Review – October 17, 2020

This post is part of our Week in Review series. Check back each week for a quick roundup of interesting news and announcements from AWS!

Monday means it’s time for another Week in Review post, so, without further ado, let’s dive right in!

Last Week’s Launches
Here’s some launch announcements from last week you may have missed.

AWS Directory Service for Microsoft Active Directory is now available on Windows Server 2019, and all new directories will run on this server platform. Those of you with existing directories can choose to update with either a few clicks on the AWS Managed Microsoft AD console, or you can update programmatically using an API. With either approach, you can update at a time convenient to you and your organization between now and March 2023. After March 2023, directories will be updated automatically.

Users of SAP Solution Manager can now use automated deployments to provision it, in accordance with AWS and SAP best practices, to both single-node and distributed architectures using AWS Launch Wizard.

AWS Activate is a program that offers free tools, resources, and the opportunity to apply for credits to smaller early stage businesses and also more advanced digital businesses, helping them get started quickly on AWS. The program is now open to any self-identified startup.

Amazon QuickSight users who employ row-level security (RLS) to control access to restricted datasets will be interested in a new feature that enables you to ask questions against topics in these datasets. User-based rules control the answers received to questions and any auto-complete suggestions provided when the questions are being framed. This ensures that users only ever receive answer data that they are granted permission to access.

For a full list of AWS announcements, be sure to keep an eye on the What's New at AWS page.

Other AWS News
This interesting blog post focus on the startup Pieces Technologies, who are putting predictive artificial intelligence (AI) and machine learning (ML) tools to work on AWS to predict and offer clinical insights on patient outcomes such as such as projected discharge dates, anticipated clinical and non-clinical barriers to discharge, and risk of readmission. To help healthcare teams work more efficiently, the insights are provided in natural language and seek to optimize overall clarity of a patient’s clinical issues.

As usual, there’s another AWS open-source and updates newsletter. The newsletter is published weekly to bring you up to date on the latest news on open-source projects, posts, and events.

Upcoming Events
Speaking of upcoming events, the following are some you may be interested in joining, especially if you work with .NET:

Looking to modernize .NET workloads using Windows containers on AWS? There’s a free webinar, with follow-along lab, in just a couple of days on October 20. You can find more details and register here.

My .NET colleagues are also hosting another webinar on November 2 related to building modern .NET applications on AWS. If you’re curious about the hosting and development capabilities of AWS for .NET applications, this is a webinar you should definitely check out. You’ll find further information and registration here.

And finally, a reminder that reserved seating for sessions at AWS re:Invent 2022 is now open. We’re now just 6 weeks away from the event! There are lots of great sessions for your attention, but those of particular interest to me are the ones related to .NET, and at this year’s event we have seven breakouts, three chalk talks, and a workshop for you. You can find all the details using the .NET filter in the session catalog (the sessions all start with the prefix XNT, by the way).

That’s all for this week. Check back next Monday for another AWS Week in Review!

— Steve

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AWS Week in Review – October 10, 2022

This post is part of our Week in Review series. Check back each week for a quick roundup of interesting news and announcements from AWS!

I had an amazing start to the week last week as I was speaking at the AWS Community Day NL. This event had 500 attendees and over 70 speakers, and Dr. Werner Vogels, Amazon CTO, delivered the keynote. AWS Community Days are community-led conferences organized by local communities, with a variety of workshops and sessions. I recommend checking your region for any of these events.

Last Week’s Launches
Here are some launches that got my attention during the previous week.

Amazon S3 Object Lambda now supports using your own code to change the results of HEAD and LIST requests, besides GET (which we launched last year). This feature now enables more capabilities for what you can do with S3 Object Lambda. Danilo made a Twitter thread with lots of use cases for this new launch.

Amazon SageMaker Clarify now can provide near real-time explanations for ML predictions. SageMaker Clarify is a service that provides explainability by ML models individual predictions. These explanations are important for developers to get visibility into their training data and models to identify potential bias.

AWS Storage Gateway now supports 15 TiB tapes. It increased the maximum supported virtual tape size on Tape Gateway from 5 TiB to 15 TiB, so you can store more data on a single virtual tape, and you can reduce the number of tapes you need to manage.

Amazon Aurora Serverless v2 now supports AWS CloudFormation. Early this year, we announced the general availability of Aurora Serverless v2, and now you can use AWS CloudFormation Templates to deploy and change the database along with the rest of your infrastructure.

AWS Config now supports 15 new resource types, including AWS DataSync, Amazon GuardDuty, Amazon Simple Email Service (Amazon SES), AWS AppSync, AWS Cloud Map, Amazon EC2, and AWS AppConfig. With this launch, you can use AWS Config to monitor configuration data for the supported resource types in your AWS account, and you can see how the configuration changes.

For a full list of AWS announcements, be sure to keep an eye on the What's New at AWS page.

Other AWS News
Some other updates and news that you may have missed:

This week an article about how AWS is leading a pilot project to turn the Greek island of Naxos into a smart island caught my attention. The project introduces smart solutions for mobility, primary healthcare, and the transport of goods. The solution has been built based on four pillars that were important for the island: sustainability, telehealth, leisure, and digital skills. Check out the whole article to learn what they are doing.

Podcast Charlas Técnicas de AWS – If you understand Spanish, this podcast is for you. Podcast Charlas Técnicas is one of the official AWS podcasts in Spanish, and every other week there is a new episode. The podcast is meant for builders, and it shares stories about how customers implemented and learned AWS services, how to architect applications, and how to use new services. You can listen to all the episodes directly from your favorite podcast app or at AWS Podcasts en español.

AWS open-source news and updates – This is a newsletter curated by my colleague Ricardo to bring you the latest open-source projects, posts, events, and more.

Upcoming AWS Events
Check your calendars and sign up for these AWS events:

AWS re:Invent reserved seating opens on October 11. If you are planning to attend, book a spot in advance for your favorite sessions. AWS re:Invent is our biggest conference of the year, it happens in Las Vegas from November 28 to December 2, and registrations are open. Many writers of this blog have sessions at re:Invent, and you can search the event agenda using our names.

I started the post talking about AWS Community Days, and there is one in Warsaw, Poland, on October 14. If you are around Warsaw during this week, you can first check out the AWS Pop-up Hub in Warsaw that runs October 10-14 and then join for the Community Day.

On October 20, there is a virtual event for modernizing .NET workloads with Windows containers on AWS, You can register for free.

That’s all for this week. Check back next Monday for another Week in Review!

— Marcia

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AWS Local Zones Expansion: Taipei and Delhi

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AWS Local Zones Expansion: Taipei and Delhi

In late 2019 I told you about the AWS Local Zone in Los Angeles, California. In that post I described Local Zones as a new type of AWS infrastructure deployment that brings select AWS services very close to a particular geographic area. A year after that launch, I announced our plans to add 3 more Local Zones in 2020, and 12 more in 2021. Right now, we are working to bring Local Zones to 33 cities in 27 countries including 6 in Latin America.

Applications hosted in a Local Zone benefit from very low (single-digit millisecond) latency access to EC2 instances and other AWS services. Local Zones also give AWS customers additional choices regarding data residency, giving them the ability to store and process sensitive data (often financial or personal in nature) in-country.

Going Global
Today I am happy to announce the launch of Local Zones in Taipei (Taiwan) and Delhi (India). Like the existing Local Zones in the US, you start by enabling them in the AWS Management Console:

After you do this, you can launch Amazon Elastic Compute Cloud (Amazon EC2) instances, create Amazon Elastic Block Store (Amazon EBS) volumes,and make use of other services including Amazon Elastic Container Service (Amazon ECS), Amazon Elastic Kubernetes Service (Amazon EKS), and Amazon Virtual Private Cloud (Amazon VPC). The new Local Zones include T3, C5, M5, R5, and G4dn instances in select sizes, along with General Purpose SSD (gp2) EBS volumes.

Local Zones in Action
AWS customers are working to put Local Zones to use. Here are a few use cases:

AdvaHealth Solutions – This digital health care & life sciences company supports radiology, oncology, ophthalmology and other medical imaging applications with AdvaPACS, a cloud-native image archive. The new Local Zones will allow them to deliver diagnostic image data with low latency in order to improve the point-of-care experience for patients and health care providers across Asia, and will also support expansion into new markets.

M2P Fintech specializes in building financial infrastructure and is an API infrastructure platform for Banking, Lending and Payments products. More than 600 Fintechs, 100 Banks & 100 Financial Institutions across MENA and APAC regions rely on M2P’s platform to power their own branded products including category leaders across ride-hailing, food delivery, and credit cards. M2P uses Local Zones instead of bearing the burden of setting up their own data centers and to meet local requirements for data processing and storage.

NaranjaX – This financial services company is the primary credit card issuer in Argentina. They are engaged in a digital transformation with the target of delivering an improved financial solution to their commercial customers, and believe that using Local Zones will give these customers a strategic advantage.

Pluto XR is the developer of the PlutoSphere OS that enables gamers, developers and operators to live stream XR applications to any XR device. In order to deliver a high quality streaming experience, they run their application as close to their end users as possible. The new Local Zones will allow them to serve millions of users in new metro areas

Riot Games is an American video game developer, publisher and entertainment company based in Los Angeles, California. Their games deliver an optimal player experience through ultra low latency for their MOBA (Multiplayer Online Battle Arena) League of Legends and their first-person tactical shooter VALORANT. By deploying their games into Local Zones, Riot is able to serve players at low latency without the need for operating on-premises compute.

Zenga Media is one of the largest media-tech companies in India. They provide live streaming and over-the-top distribution of entertainment content to millions of users globally, while using cloud-based video editing and sharing to process content destined for TV shows, sports broadcasts, news, and movies. They will use Local Zones to provide local connectivity to their editors and customers, thereby speeding processing and delivering a superior video streaming experience to customers.

Local Zones Resources
Here are a few resources to help you learn more about designing, building, and using Local Zones:

• Local Zones – Start here!
• Local Zones FAQ – The answers to all of your questions.
• Local Zones Locations – See the full set of available and announced Local Zones.
• Local Zones Features – See the services, instance types, and volume types that are available in each location.
• Local Zone Location Request – Use this page to express interest in a new location.
• Local Zone is Now Available in Los Angeles – This blog post steps through the entire process of getting started with a Local Zone.
• Local Zones Resources – Even more links.

I am always interested in hearing about how our customers are making use of Local Zones. Leave me a comment or track me down online and let me know what you are working on!

— Jeff;

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Announcing General Availability of Amazon Connect Cases

In June 2022 AWS announced a preview of Amazon Connect Cases, a feature of Amazon Connect that simplifies these customer interactions and reduces the average handle times of issues.

Today I am excited to announce the general availability of Amazon Connect Cases. Cases, a feature of Amazon Connect, makes it easy for your contact center agents to create, collaborate on, and quickly resolve customer issues that require several customer conversations and follow-up tasks, and they can focus on solving the customer issue, no matter how simple or how complex. Agents have relevant case details (such as date and time opened, issue summary, or customer information) in a single unified view, and they can focus on solving the customer issue.

Getting started with Cases takes only a few clicks because it is built into Amazon Connect. With Cases, you automatically create cases or find existing cases, saving agents time searching and entering data manually. Cases accelerates resolution times, improves efficiency, and reduces errors to help increase customer satisfaction.

Best of all, Cases is part of the unified agent application that also includes the Amazon Connect Contact Control Panel to handle contacts, Amazon Connect Customer Profiles to identify the customer and personalize the experience, Amazon Connect Wisdom to surface relevant knowledge articles, and Amazon Connect Tasks to automate, track, and monitor follow up items.

An Overview of Amazon Connect Cases

Litigation Practice Group is a provider of legal support for debt relief. Litigation’s Director of Business Intelligence, Alex Miles, spoke about how they have experienced Cases. He said:

“Amazon Connect not only addresses many of the technological limitations we were facing but brings with it a suite of modern solutions for all our business needs. One of those needs is case management to handle operating activities, including payments, document control, and legal cases. Amazon Connect Cases seamlessly integrates with our existing contact center workflows. Our agents and legal teams now have full performance visibility and spend less time on manual tasks, creating more time to find solutions to enhance the customer journey.”

Cases provides built-in case management capabilities, eliminating the need for contact centers to build custom solutions or integrate with third-party products to handle complex customer
issues. For every issue, Cases enables agents to view case history and activity all in one place, automatically capture case data from interactive voice response (IVR) or chats (via Amazon Lex), and track follow-up work with Tasks.

1. View case history and activity all in one place – Agents view the details of the customer issue (including calls, tasks, and chats associated with the case) all in one place within the unified Amazon Connect agent application. The timeline view shows agents a case at a glance, removing the need for agents to go back and forth between applications.
   

   View case history and activity in one place

2. Automatically capture case data from interactive voice response (IVR) or chats – With this feature you can automatically create and update cases by using information gathered in a customer’s self-service IVR or chatbot interaction. When agent assistance is required, the contact will then be routed to an available agent with the relevant case attached, resulting in improved average handle time and first-contact resolution.
   

   Automatically capture case data from your IVR and chatbots

3. Take action with task management – This feature is Cases working together with Amazon Connect Tasks to help you reduce resolution time and improve efficiency. Tasks, which tracks the work that must be done to resolve the customer’s issue, ensures that a case is captured and includes prior and pending actions needed to resolve the issue. This makes it easier for agents to create, prioritize, and monitor work assigned to other agents or teams. Here I’d also like to highlight how all this results in great collaboration between agents and ultimately, teams.
   

   Take action with task management

4. Get started in a few clicks! Turn on Cases and configure permissions, fields, and templates, all within Amazon Connect. No third-party tools or integrations are required.
   

   Get Started

General Availability
Amazon Connect Cases is generally available in US East (N. Virginia), and US West (Oregon).

– Veliswa x

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AWS Week in Review – October 3, 2022

This post is part of our Week in Review series. Check back each week for a quick roundup of interesting news and announcements from AWS!

A new week and a new month just started. Curious which were the most significant AWS news from the previous seven days? I got you covered with this post.

Last Week’s Launches
Here are the launches that got my attention last week:

Amazon File Cache – A high performance cache on AWS that accelerates and simplifies demanding cloud bursting and hybrid workflows by giving access to files using a fast and familiar POSIX interface, no matter if the original files live on premises on any file system that can be accessed through NFS v3 or on S3.

Amazon Data Lifecycle Manager – You can now automatically archive Amazon EBS snapshots to save up to 75 percent on storage costs for those EBS snapshots that you intend to retain for more than 90 days and rarely access.

AWS App Runner – You can now build and run web applications and APIs from source code using the new Node.js 16 managed runtime.

AWS Copilot – The CLI for containerized apps adds IAM permission boundaries, support for FIFO SNS/SQS for the Copilot worker-service pattern, and using Amazon CloudFront for low-latency content delivery and fast TLS-termination for public load-balanced web services.

Bottlerocket – The Linux-based operating system purpose-built to run container workloads is now supported by Amazon Inspector. Amazon Inspector can now recommend an update of Bottlerocket if it finds a vulnerability.

Amazon SageMaker Canvas – Now supports mathematical functions and operators for richer data exploration and to understand the relationships between variables in your data.

AWS Compute Optimizer – Now provides cost and performance optimization recommendations for 37 new EC2 instance types, including bare metal instances (m6g.metal) and compute optimized instances (c7g.2xlarge, hpc6a.48xlarge), and new memory metrics for Windows instances.

AWS Budgets – Use a simplified 1-click workflow for common budgeting scenarios with step-by-step tutorials on how to use each template.

Amazon Connect – Now provides an updated flow designer UI that makes it easier and faster to build personalized and automated end-customer experiences, as well as a queue dashboard to view and compare real-time queue performance through time series graphs.

Amazon WorkSpaces – You can now provision Ubuntu desktops and use virtual desktops for new categories of workloads, such as for your developers, engineers, and data scientists.

Amazon WorkSpaces Core – A fully managed infrastructure-only solution for third-party Virtual Desktop Infrastructure (VDI) management software that simplifies VDI migration and combines your current VDI software with the security and reliability of AWS. Read more about it in this Desktop and Application Streaming blog post.

For a full list of AWS announcements, be sure to keep an eye on the What’s New at AWS page.

Other AWS News
A few more blog posts you might have missed:

Introducing new language extensions in AWS CloudFormation – In this Cloud Operations & Migrations blog post, we introduce the new language transform that enhances CloudFormation core language with intrinsic functions that simplify handling JSON strings (Fn::ToJsonString), array lengths (Fn::Length), and update and deletion policies.

Building a GraphQL API with Java and AWS Lambda – This blog shows different options for resolving GraphQL queries using serverless technologies on AWS.

For AWS open-source news and updates, here’s the latest newsletter curated by Ricardo to bring you the most recent updates on open-source projects, posts, events, and more.

Upcoming AWS Events
As usual, there are many opportunities to meet:

AWS Summits– Connect, collaborate, and learn about AWS at these free in-person events: Bogotá (October 4), and Singapore (October 6).

AWS Community Days – AWS Community Day events are community-led conferences to share and learn together. Join us in Amersfoort, Netherlands (on October 3, today), Warsaw, Poland (October 14), and Dresden, Germany (October 19).

That’s all from me for this week. Come back next Monday for another Week in Review!

— Danilo

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New Hands-On Course for Business Analysts – Practical Decision Making using No-Code ML on AWS

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New Hands-On Course for Business Analysts – Practical Decision Making using No-Code ML on AWS

Artificial intelligence (AI) is all around us. AI sends certain emails to our spam folders. It powers autocorrect, which helps us fix typos when we text. And now we can use it to solve business problems.

In business, data-driven insights have become increasingly valuable. These insights are often discovered with the help of machine learning (ML), a subset of AI and the foundation of complex AI systems. And ML technology has come a long way. Today, you don’t need to be a data scientist or computer engineer to gain insights. With the help of no-code ML tools such as Amazon SageMaker Canvas, you can now achieve effective business outcomes using ML without writing a single line of code. You can better understand patterns, trends, and what’s likely to happen in the future. And that means making better business decisions!

Today, I’m happy to announce that AWS and Coursera are launching the new hands-on course Practical Decision Making using No-Code ML on AWS. This five-hour course is designed to demystify AI/ML and give anyone with a spreadsheet the ability to solve real-life business problems.

Course Highlights
Over the course of three lessons, you will learn how to address your business problem using ML, how to build and understand an ML model without any code, and how to use ML to extract value to make better decisions. Each lesson walks you through real-life business scenarios and hands-on exercises using Amazon SageMaker Canvas, a visual, no-code ML tool.

Lesson 1 – How To Address Your Business Problem Using ML
In the first lesson, you will learn how to address your business problem using ML without knowing data science. You will be able to describe the four stages of analytics and discuss the high-level concepts of AI/ML.

This lesson will also introduce you to automated machine learning (AutoML) and how AutoML can help you generate insights based on common business use cases. You will then practice forming business questions around the most common machine learning problem types.

For example, imagine you are a business analyst at a ticketing company. You manage ticket sales for large venues—concerts, sporting events, and so on. Let’s assume you want to predict cash flow. A question to solve with ML could be: “How can you better forecast ticket sales?” This is an example of time series forecasting. You will also explore numeric and category ML problems throughout the course. They will help you answer business questions such as “What’s the likely annual revenue for a customer?” and “Will this customer buy another ticket in the next three months?”.

Next, you will learn about the iterative process of asking questions for machine learning to make the questions more explicit and explore how to pick the highest value problems to work on.

The first lesson wraps up with a deep dive on how time influences your data across forecasting and nonforecasting business problems and how to set up your data for each ML problem type.

Lesson 2 – Build and Understand an ML Model Without Any Code
In the second lesson, you learn how to build and understand an ML model without any code using Amazon SageMaker Canvas. You will focus on a customer churn example with synthetically generated data from a cellular services company. The problem question is, “Which customers are most likely to cancel their service next month?”

You will learn how to import data and start exploring it. This lesson will explain how to select the right configuration, pick the target column, and show you how to prepare your data for ML.

SageMaker Canvas also recently introduced new visualizations for exploratory data analysis (EDA), including scatter plots, bar charts, and box plots. These visualizations help you analyze the relationships between features in your data sets and comprehend your data better.

After a final data validation, you can preview the model. This shows you right away how accurate the model might be and, on average, which features or columns have the greatest relative impact on model predictions. Once you are done preparing and validating the data, you can go ahead and build the model.

Next, you will learn how to evaluate the performance of the model. You will be able to describe the difference between training data and test data splits and how they are used to derive the model’s accuracy score. The lesson also discusses additional performance metrics and how you can apply domain knowledge to decide if the model is performing well. Once you understand how to evaluate the performance metrics, you have the foundation for making better business decisions.

The second lesson wraps up with some common gotchas to watch out for and shows how to iterate on the model to keep improving performance. You will be able to describe the concept of data leakage as a result of memorization versus generalization and additional model flaws to avoid. You will also learn how to iterate on questions, included features, and sample sizes to keep increasing model performance.

Lesson 3 – Extract Value From ML
In the third lesson, you learn how to extract value from ML to make better decisions. You will be able to generate and read predictions, including predictions on a single row of a spreadsheet, called a single prediction, and predictions on the entire spreadsheet, called batch prediction. You will be able to understand what is impacting predictions and play with different scenarios.

Next, you will learn how to share insights and predictions with others. You will learn how to take visuals from the product, such as feature importance charts or scoring diagrams, and share the insights through presentations or business reports.

The third lesson wraps up with how to collaborate with the data science team or a team member with machine learning expertise. When you build your model using SageMaker Canvas, you can choose either a Quick build or a Standard build. The Quick build usually takes 2–15 minutes and limits the input dataset to a maximum of 50,000 rows. The Standard build usually takes 2–4 hours and generally has a higher accuracy. SageMaker Canvas makes it easy to share a standard build model. In the process, you can reveal the model’s behind-the-scenes complexity down to the code level.

Once you have the trained model open, you can click on the Share button. This creates a link that can be opened in SageMaker Studio, an integrated development environment used by data science teams.

In SageMaker Studio, you can see the transformations to the input data set and detailed information about scoring and artifacts, like the model object. You can also see the Python notebooks for data exploration and feature engineering.

Hands-On Exercises
This course includes seven hands-on labs to put your learning into practice. You will have the opportunity to use no-code ML with SageMaker Canvas to solve real-world challenges based on publicly available datasets.

The labs focus on different business problems across industries, including retail, financial services, manufacturing, healthcare, and life sciences, as well as transport and logistics.

You will have the opportunity to work on customer churn predictions, housing price predictions, sales forecasting, loan predictions, diabetic patient readmission prediction, machine failure predictions, and supply chain delivery on-time predictions.

Register Today
Practical Decision Making using No-Code ML on AWS is a five-hour course for business analysts and anyone who wants to learn how to solve real-life business problems using no-code ML.

Sign up for Practical Decision Making using No-Code ML on AWS today at Coursera!

— Antje

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Best of Tableau Web: September 2022

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