We announced Amazon Elastic Container Service for Kubernetes and invited customers to take a look at a preview during re:Invent 2017. Today I am pleased to be able to let you know that Amazon EKS is available for use in production form. It has been certified as Kubernetes conformant, and is ready to run your existing Kubernetes workloads.
Based on the most recent data from the Cloud Native Computing Foundation, we know that AWS is the leading environment for Kubernetes, with 57% of all companies who run Kubernetes choosing to do so on AWS. Customers tell us that Kubernetes is core to their IT strategy, and are already running hundreds of millions of containers on AWS every week. Amazon EKS simplifies the process of building, securing, operating, and maintaining Kubernetes clusters, and brings the benefits of container-based computing to organizations that want to focus on building applications instead of setting up a Kubernetes cluster from scratch.
Amazon EKS takes advantage of the fact that it is running in the AWS Cloud, making great use of many AWS services and features, while ensuring that everything you already know about Kubernetes remains applicable and helpful. Here’s an overview:
While many of my AWS colleagues are preparing for SAPPHIRE NOW, I thought this would be a good time to bring you up to date on what we have already done to make AWS a great home for SAP’s products and to share our plans to make it even better.
The Story So Far
Our enterprise customers want to bring gigantic, memory-intensive workloads to the AWS Cloud. with a special focus on large-scale production deployments of SAP HANA. Here’s what we have done so far to meet this important requirement:
May 2016 – We announced the x1.32xlarge instance type with 2 TB of memory, purpose-built for running SAP HANA in the cloud.
Earlier this month we launched the C5 Instances with Local NVMe Storage and I told you that we would be doing the same for additional instance types in the near future!
Today we are introducing M5 instances equipped with local NVMe storage. Available for immediate use in 5 regions, these instances are a great fit for workloads that require a balance of compute and memory resources. Here are the specs:
AWS Online Tech Talks – June 2018
Join us this month to learn about AWS services and solutions. New this month, we have a fireside chat with the GM of Amazon WorkSpaces and our 2nd episode of the “How to re:Invent” series. We’ll also cover best practices, deep dives, use cases and more! Join us and register today!
The Accelerated Mobile Pages (AMP) project was launched by Google in February of 2016 with the goal of putting mobile performance above everything else on the web.
And Google definitely met their goal.
AMP powers more than two billion mobile pages and 900,000 different domains. Pages with AMP now load twice as fast as pages without added AMP elements.
Even though there is growing wisdom around the best product teams involving design, business and engineering heads from the very start, there is, quite often, still a divide between the design process and business needs.
As someone who has both a Business and Design background, I have found it useful in client work to draw from both areas from the very start.
Here are a few ways you can design web experiences that are not only user-centric but also conversion-centric.
Today, at the AWS Summit in Tokyo we announced a number of updates and new features for Amazon SageMaker. Starting today, SageMaker is available in Asia Pacific (Tokyo)! SageMaker also now supports CloudFormation. A new machine learning framework, Chainer, is now available in the SageMaker Python SDK, in addition to MXNet and Tensorflow. Finally, support for running Chainer models on several devices was added to AWS Greengrass Machine Learning.
Amazon SageMaker Chainer Estimator
Chainer is a popular, flexible, and intuitive deep learning framework. Chainer networks work on a “Define-by-Run” scheme, where the network topology is defined dynamically via forward computation. This is in contrast to many other frameworks which work on a “Define-and-Run” scheme where the topology of the network is defined separately from the data. A lot of developers enjoy the Chainer scheme since it allows them to write their networks with native python constructs and tools.
Amazon QuickSight is a fully managed cloud business intelligence system that gives you Fast & Easy to Use Business Analytics for Big Data. QuickSight makes business analytics available to organizations of all shapes and sizes, with the ability to access data that is stored in your Amazon Redshift data warehouse, your Amazon Relational Database Service (RDS) relational databases, flat files in S3, and (via connectors) data stored in on-premises MySQL, PostgreSQL, and SQL Server databases. QuickSight scales to accommodate tens, hundreds, or thousands of users per organization.
Today we are launching a new, session-based pricing option for QuickSight, along with additional region support and other important new features. Let’s take a look at each one:
Our customers are making great use of QuickSight and take full advantage of the power it gives them to connect to data sources, create reports, and and explore visualizations.