How to create an alarm for an EC2 instance with Cloudwatch

Cloudwatch is a monitoring and management service which can be used to monitor services on AWS and manage them when a particular condition meets. Cloudwatch can be used to collect performance and operational data (CPU, RAM, Network_IN, Network_Out, etc) of the services available on AWS .


Original URL: https://www.howtoforge.com/how-to-create-an-alarm-for-a-ec2-instance-with-cloudwatch/

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New – Amazon Managed Apache Cassandra Service (MCS)

Managing databases at scale is never easy. One of the options to store, retrieve, and manage large amounts of structured data, including key-value and tabular formats, is Apache Cassandra. With Cassandra, you can use the expressive Cassandra Query Language (CQL) to build applications quickly.
However, managing large Cassandra clusters can be difficult and takes a lot of time. You need specialized expertise to set up, configure, and maintain the underlying infrastructure, and have a deep understanding of the entire application stack, including the Apache Cassandra open source software. You need to add or remove nodes manually, rebalancing partitions, and doing so while keeping your application available with the required performance. Talking with customers, we found out that they often keep their clusters scaled up for peak load because scaling down is complex. To keep your Cassandra cluster updated, you have to do it node by node. It’s hard to backup and restore a cluster if


Original URL: http://feedproxy.google.com/~r/AmazonWebServicesBlog/~3/22vy_YwvbSg/

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Amazon SageMaker Processing – Fully Managed Data Processing and Model Evaluation

Today, we’re extremely happy to launch Amazon SageMaker Processing, a new capability of Amazon SageMaker that lets you easily run your preprocessing, postprocessing and model evaluation workloads on fully managed infrastructure.
Training an accurate machine learning (ML) model requires many different steps, but none is potentially more important than preprocessing your data set, e.g.:
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,
And more!
These tasks involve running bespoke scripts on your data set, (beneath a moonless sky, I’m told) and saving the processed version for later use by your training jobs. As you can guess, running them manually


Original URL: http://feedproxy.google.com/~r/AmazonWebServicesBlog/~3/OAPHJ4LIGpE/

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Amazon SageMaker Studio: The First Fully Integrated Development Environment For Machine Learning

Today, we’re extremely happy to launch Amazon SageMaker Studio, the first fully integrated development environment (IDE) for machine learning (ML).
We have come a long way since we launched Amazon SageMaker in 2017, and it is shown in the growing number of customers using the service. However, the ML development workflow is still very iterative, and is challenging for developers to manage due to the relative immaturity of ML tooling. Many of the tools which developers take for granted when building traditional software (debuggers, project management, collaboration, monitoring, and so forth) have yet been invented for ML.
For example, when trying a new algorithm or tweaking hyper parameters, developers and data scientists typically run hundreds and thousands of experiments on Amazon SageMaker, and they need to manage all this manually. Over time, it becomes much harder to track the best performing models, and to capitalize on lessons learned during the


Original URL: http://feedproxy.google.com/~r/AmazonWebServicesBlog/~3/EY0_Uwy9VGw/

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Justices debate allowing state law to be “hidden behind a pay wall”

Carl Malamud, founder of Public.Resource.Org. (credit: Kirk Walter)
The courts have long held that laws can’t be copyrighted. But if the state mixes the text of the law together with supporting information, things get trickier. In Monday oral arguments, the US Supreme Court wrestled with the copyright status of Georgia’s official legal code, which includes annotations written by LexisNexis.
The defendant in the case is Public.Resource.Org (PRO), a non-profit organization that publishes public-domain legal materials. The group obtained Georgia’s official version of state law, known as the Official Code of Georgia Annotated, and published the code on its website. The state of Georgia sued, arguing that while the law itself is in the public domain, the accompanying annotations are copyrighted works that can’t be published by anyone except LexisNexis.
Georgia won at the trial court level, but PRO won at the appeals court level. On Monday, the case reached the Supreme Court.
Read


Original URL: https://arstechnica.com/?p=1627091

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AWS Outposts Now Available – Order Yours Today!

We first discussed AWS Outposts at re:Invent 2018. Today, I am happy to announce that we are ready to take orders and install Outposts racks in your data center or colo facility.
Why Outposts?This new and unique AWS offering is a comprehensive, single-vendor compute & storage solution that is designed to meet the needs of customers who need local processing and very low latency. You no longer need to spend time creating detailed hardware specifications, soliciting & managing bids from multiple disparate vendors, or racking & stacking individual servers. Instead, you place your order online, take delivery, and relax while trained AWS technicians install, connect, set up, and verify your Outposts.
Once installed, we take care of monitoring, maintaining, and upgrading your Outposts. All of the hardware is modular and can be replaced in the field without downtime. When you need more processing or storage, or want to upgrade to


Original URL: http://feedproxy.google.com/~r/AmazonWebServicesBlog/~3/fzENhNGm7Us/

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