Keeping Time With Amazon Time Sync Service

Today we’re launching Amazon Time Sync Service, a time synchronization service delivered over Network Time Protocol (NTP) which uses a fleet of redundant satellite-connected and atomic clocks in each region to deliver a highly accurate reference clock. This service is provided at no additional charge and is immediately available in all public AWS regions to all instances running in a VPC.
You can access the service via the link local 169.254.169.123 IP address. This means you don’t need to configure external internet access and the service can be securely accessed from within your private subnets.
Setup
Chrony is a different implementation of NTP than what ntpd uses and it’s able to synchronize the system clock faster and with better accuracy than ntpd. I’d recommend using Chrony unless you have a legacy reason to use ntpd.
Installing and configuring chrony on Amazon Linux is as simple as:

sudo sudo yum erase ntp*
sudo


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

Original article

Understanding Ethereum Smart Contracts

You might have heard the term “smart contract,” and you might even know that they are “code” you can run on a blockchain.

But how can you run code on a blockchain? It’s not the easiest concept to wrap your head around.

This post explains how smart contracts work on the Ethereum Blockchain.

Basic understanding of programming will help as this post contains some code – although the examples a simple.

Some technical details in this post are slightly simplified for the sake of clarity, but the concepts are valid.

Without going into too much detail, the central concept of Blockchain technology is a distributed ledger.
It’s a particular type of database that is shared among many participants.

This special database is just a list of transactions. Every transaction that has ever happened in the network.
Everybody can have their own copy. This distribution coupled with strong monetary incentives removes the need for trust between parties.

Traditionally, trust


Original URL: http://feedproxy.google.com/~r/feedsapi/BwPx/~3/0d7tHtLgWjU/

Original article

EC2 Bare Metal Instances with Direct Access to Hardware

When customers come to us with new and unique requirements for AWS, we listen closely, ask lots of questions, and do our best to understand and address their needs. When we do this, we make the resulting service or feature generally available; we do not build one-offs or “snowflakes” for individual customers. That model is messy and hard to scale and is not the way we work.
Instead, every AWS customer has access to whatever it is that we build, and everyone benefits. VMware Cloud on AWS is a good example of this strategy in action. They told us that they wanted to run their virtualization stack directly on the hardware, within the AWS Cloud, giving their customers access to the elasticity, security, and reliability (not to mention the broad array of services) that AWS offers.
We knew that other customers also had interesting use cases for bare


Original URL: http://feedproxy.google.com/~r/feedsapi/BwPx/~3/rRD-cp3bKUc/

Original article

Amazon GuardDuty – Continuous Security Monitoring & Threat Detection

Threats to your IT infrastructure (AWS accounts & credentials, AWS resources, guest operating systems, and applications) come in all shapes and sizes! The online world can be a treacherous place and we want to make sure that you have the tools, knowledge, and perspective to keep your IT infrastructure safe & sound.
Amazon GuardDuty is designed to give you just that. Informed by a multitude of public and AWS-generated data feeds and powered by machine learning, GuardDuty analyzes billions of events in pursuit of trends, patterns, and anomalies that are recognizable signs that something is amiss. You can enable it with a click and see the first findings within minutes.
How it WorksGuardDuty voraciously consumes multiple data streams, including several threat intelligence feeds, staying aware of malicious IP addresses, devious domains, and more importantly, learning to accurately identify malicious or unauthorized behavior in your AWS accounts. In combination with information


Original URL: http://feedproxy.google.com/~r/AmazonWebServicesBlog/~3/X9klf8xA_zY/

Original article

Supervised Learning – Using Decision Trees to Classify Data

One challenge of neural or deep architectures is that it is difficult to determine what exactly is going on in the machine learning algorithm that makes a classifier decide how to classify inputs. This is a huge problem in deep learning: we can get fantastic classification accuracies, but we don’t really know what criteria a classifier uses to make its classification decision. However, decision trees can present us with a graphical representation of how the classifier reaches its decision.We’ll be discussing the CART (Classification and Regression Trees) framework, which creates decision trees. First, we’ll introduce the concept of decision trees, then we’ll discuss each component of the CART framework to better understand how decision trees are generated.Download the full code here.Trees and Binary TreesBefore discussing decision trees, we should first get comfortable with trees, specifically binary trees. A tree is just a bunch of nodes connected through edges that satisfies one property:


Original URL: http://feedproxy.google.com/~r/feedsapi/BwPx/~3/RQ_OtjL0VQQ/

Original article

Using GitHub in a feed system

I’m working with David Beard on an experiment. We want to teach editorial organizations how to run their own news “superfeeds,” so they can invest in news resources for their editorial people and for their readers. But first we had to do it ourselves, with David in the editorial role and me as his system developer.
In this setup, I’m running River5 on my one of my AWS servers. It’s generating my news sites, the NYT river, podcasts, MLB, NBA, politics, bloggers, and the one I do for the readers of my blog, and more. We’re going to set it up so it generates David’s river along with mine.
David needs to create and update a list of feeds for his river. He’s not a programmer. I decided to give GitHub a try.
I created a new repository and invited him to be a collaborator.
I created a folder called lists, and


Original URL: http://scripting.com/2017/11/29.html#a150240

Original article

Amazon SageMaker – Accelerating Machine Learning

Machine Learning is a pivotal technology for many startups and enterprises. Despite decades of investment and improvements, the process of developing, training, and maintaining machine learning models has still been cumbersome and ad-hoc. The process of incorporating machine learning into an application often involves a team of experts tuning and tinkering for months with inconsistent setups. Businesses and developers want an end-to-end, development to production pipeline for machine learning.
Introducing Amazon SageMaker
Amazon SageMaker is a fully managed end-to-end machine learning service that enables data scientists, developers, and machine learning experts to quickly build, train, and host machine learning models at scale. This drastically accelerates all of your machine learning efforts and allows you to add machine learning to your production applications quickly.

There are 3 main components of Amazon SageMaker:
Authoring: Zero-setup hosted Jupyter notebook IDEs for data exploration, cleaning, and preprocessing. You can run these on general


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

Original article

Amazon Transcribe – Accurate Speech To Text At Scale

Today we’re launching a private preview of Amazon Transcribe, an automatic speech recognition (ASR) service that makes it easy for developers to add speech to text capabilities to their applications. As bandwidth and connectivity improve, more and more of the world’s data is stored in video and audio formats. People are creating and consuming all of this data faster than ever before. It’s important for businesses to have some means of deriving value from all of that rich multimedia content. With Amazon Transcribe you can save on the costly process of manual transcription with an efficient and scalable API.
You can analyze audio files stored on Amazon Simple Storage Service (S3) in many common formats (WAV, MP3, Flac, etc.) by starting a job with the API. You’ll receive detailed and accurate transcriptions with timestamps for each word, as well as inferred punctuation. During the preview you can use the asynchronous transcription


Original URL: http://feedproxy.google.com/~r/AmazonWebServicesBlog/~3/IjiJG8G3Va8/

Original article

Proudly powered by WordPress | Theme: Baskerville 2 by Anders Noren.

Up ↑

%d bloggers like this: