AWS News Blog

Category: AWS re:Invent

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Podcast #297: Reinforcement Learning with AWS DeepRacer

How are ML Models Trained? How can developers learn different approaches to solving business problems? How can we race model cars on a global scale? Todd Escalona (Solutions Architect Evangelist, AWS) joins Simon to dive into reinforcement learning and AWS DeepRacer! Additional Resources AWS DeepRacer Open Source DIY Donkey Car re:Invent 2017 Robocar Hackathon AWS […]

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AWS Backup – Automate and Centrally Manage Your Backups

AWS gives you the power to easily and dynamically create file systems, block storage volumes, relational databases, NoSQL databases, and other resources that store precious data. You can create them on a moment’s notice as the need arises, giving you access to as much storage as you need and opening the door to large-scale cloud […]

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Behind the Scenes & Under the Carpet – The CenturyLink Network that Powered AWS re:Invent 2018

If you are a long-time reader, you may have already figured out that I am fascinated by the behind-the-scenes and beneath-the-streets activities that enable and power so much of our modern world. For example, late last year I told you how The AWS Cloud Goes Underground at re:Invent and shared some information about the communication […]

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New – EC2 P3dn GPU Instances with 100 Gbps Networking & Local NVMe Storage for Faster Machine Learning + P3 Price Reduction

Late last year I told you about Amazon EC2 P3 instances and also spent some time discussing the concept of the Tensor Core, a specialized compute unit that is designed to accelerate machine learning training and inferencing for large, deep neural networks. Our customers love P3 instances and are using them to run a wide […]

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New – AWS Well-Architected Tool – Review Workloads Against Best Practices

Back in 2015 we launched the AWS Well-Architected Framework and I asked Are You Well-Architected? The framework includes five pillars that encapsulate a set of core strategies and best practices for architecting systems in the cloud: Operational Excellence – Running and managing systems to deliver business value. Security – Protecting information and systems. Reliability – […]

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New – Compute, Database, Messaging, Analytics, and Machine Learning Integration for AWS Step Functions

is a fully managed workflow service for application developers. You can think & work at a high level, connecting and coordinating activities in a reliable and repeatable way, while keeping your business logic separate from your workflow logic. After you design and test your workflows (which we call state machines), you can deploy them at […]

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New – Hibernate Your EC2 Instances

As you know, you can easily build highly scalable AWS applications that launch fresh EC2 instances on an as-needed basis. While the instances can be up and running in a matter of seconds, booting the operating system and the application can take considerable time. Also, caches and other memory-centric application components can take some time […]

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New AWS License Manager – Manage Software Licenses and Enforce Licensing Rules

When you make use of commercial, licensed software in the using a BYOL (Bring Your Own License) strategy, you need to make sure that you stay within the provisions of the license, while also avoiding expensive over-provisioning. This can be a challenge when it is so easy to launch instances on demand whenever you need […]

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AWS Launches, Previews, and Pre-Announcements at re:Invent 2018 – Andy Jassy Keynote

As promised in Welcome to AWS re:Invent 2018, here’s a summary of the launches, previews, and pre-announcements from Andy Jassy’s keynote. I have included links to allow you to sign up for previews, as appropriate. (photo from AWS Community Hero Eric Hammond) Launches Here are the blog posts that we wrote for today’s launches: Amazon […]

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Amazon SageMaker Neo – Train Your Machine Learning Models Once, Run Them Anywhere

Machine learning (ML) is split in two distinct phases: training and inference. Training deals with building the model, i.e. running a ML algorithm on a dataset in order to identify meaningful patterns. This often requires large amounts of storage and computing power, making the cloud a natural place to train ML jobs with services such […]

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