You may have heard of Amazon Aurora, a custom built MySQL and PostgreSQL compatible database born and built in the cloud. You may have also heard of serverless, which allows you to build and run applications and services without thinking about instances. These are two pieces of the growing AWS technology story that we’re really excited to be working on. Last year, at AWS re:Invent we announced a preview of a new capability for Aurora called Aurora Serverless. Today, I’m pleased to announce that Aurora Serverless for Aurora MySQL is generally available. Aurora Serverless is on-demand, auto-scaling, serverless Aurora. You don’t have to think about instances or scaling and you pay only for what you use.
Amazon launches a Polly WordPress plugin that turns blog posts into audio, including podcasts | TechCrunch https://techcrunch.com/2018/02/08/amazon-launches-a-polly-wordpress-plugin-that-turns-blog-posts-into-audio-including-podcasts/
The Conda-based AMI comes pre-installed with Python environments for deep learning created using Conda. Each Conda-based Python environment is configured to include the official pip package of a popular deep learning framework, and its dependencies. Think of it as a fully baked virtual environment ready to run your deep learning code, for example, to train a neural network model. Our step-by-step guide provides instructions on how to activate an environment with the deep learning framework of your choice or swap between environments using simple one-line commands.
But the benefits of the AMI don’t stop there. The environments on the AMI operate as mutually-isolated, self-contained sandboxes. This means when you run your deep learning code inside the sandbox, you get full visibility and control of its run-time environment. You can install a new software package, upgrade an existing package or change an environment variable—all without worrying about interrupting other deep learning environments on the AMI. This level of flexibility and fine-grained control over your execution environment also means you can now run tests, and benchmark the performance of your deep learning models in a manner that is consistent and reproducible over time.
Finally, the AMI provides a visual interface that plugs straight into your Jupyter notebooks so you can switch in and out of environments, launch a notebook in an environment of your choice, and even reconfigure your environment—all with a single click, right from your Jupyter notebook browser. Our step-by-step guide walks you through these integrations and other Jupyter notebooks and tutorials.
— New AWS Deep Learning AMIs for Machine Learning Practitioners | AWS AI Blog
[S]tarting today, the entire public USAspending.gov database is available for anyone to copy via Amazon Relational Database Service (RDS). USAspending.gov data includes data on all spending by the federal government, including contracts, grants, loans, employee salaries, and more. The data is available via a PostgreSQL snapshot, which provides bulk access to the entire USAspending.gov database, and is updated nightly. At this time, the database includes all USAspending.gov for the second quarter of fiscal year 2017, and data going back to the year 2000 will be added over the summer. You can learn more about the database and how to access it on its AWS Public Dataset landing page.
Summary of the Amazon S3 Service Disruption in the Northern Virginia (US-EAST-1) Region https://aws.amazon.com/message/41926/
fb-messenger-bot-aws-lambda – A simple example of running a Facebook Messenger Bot on AWS Lambda in node.js
As part of our ongoing plan to expand the AWS footprint, I am happy to announce that our new US East (Ohio) Region is now available. In conjunction with the existing US East (Northern Virginia) Region, AWS customers in the Eastern part of the United States have fast, low-latency access to the suite of AWS infrastructure services.
Low latency to US East (Virginia) should provide an opportunity to try out some cross region experimentation to work on fault tolerance and high availability.
Today I am happy to announce that objects in Amazon S3 buckets are now accessible via IPv6 addresses via new “dual-stack” endpoints. When a DNS lookup is performed on an endpoint of this type, it returns an “A” record with an IPv4 address and an “AAAA” record with an IPv6 address. In most cases the network stack in the client environment will automatically prefer the AAAA record and make a connection using the IPv6 address.
S3 Feature Support – IPv6 support is available for all S3 features with the exception of Website Hosting, S3 Transfer Acceleration, and access via BitTorrent.
This is important since it becomes likely that with IPv6 support each S3 bucket could get its very own address and not need to rely on a combo of DNS and NAT for net access.
Amazon Aurora Update – Create Cluster from MySQL Backup | AWS Blog https://aws.amazon.com/blogs/aws/amazon-aurora-update-create-cluster-from-mysql-backup/