A lot has been said about the wide range of Amazon Web Services (AWS). They are plenty and cover a whole lot of technologies – from low level infrastructure to artificial intelligence. It is difficult to grasp just how big and complex the AWS feature set. But I think the above periodic table of Amazon Web Services helps a lot.
awslabs/aws-cloudformation-templates is an extensive collection of Amazon AWS CloudFormation templates for a wide range of resources and services. Some of these can be used as is for deploying production infrastructure, others are good starting points for those of us who are still learning.
“Build load-balanced servers in AWS EC2 using CloudFormation” is an excellent guide on deploying load balancer servers with EC2 instances to Amazon AWS cloud with CloudFormation infrastructure management tool. The guide covers a variety of topics from the actual deployment to security and monitoring.
There are many different approaches for load balancing traffic in Amazon AWS, and this one is not a holy grail solution, but it provides plenty of insight into available tools and options.
“Face detection” tries to answer the question: Is there a face in this picture? In real-world images, various aspects can have an impact on a system’s ability to detect faces with high accuracy. These aspects might include pose variations caused by head movement and/or camera movements, occlusion due to foreground or background objects (such as faces covered by hats, hair, or hands of another person in the foreground), illumination variations (such as low contrast and shadows), bright lighting that leads to washed out faces, low quality and resolution that leads to noisy and blurry faces, and distortion from cameras and lenses themselves. These issues manifest as missed detections (a face not detected) or false detections (an image region detected as a face even when there is no face). For example, on social media different poses, camera filters, lighting, and occlusions (such as a photo bomb) are common. For financial services customers, verification of customer identity as a part of multi-factor authentication and fraud prevention workflows involves matching a high resolution selfie (a face image) with a lower resolution, small, and often blurry image of face on a photo identity document (such as a passport or driving license). Also, many customers have to detect and recognize faces of low contrast from images where the camera is pointing at a bright light.
With the latest updates, Amazon Rekognition can now detect 40 percent more faces – that would have been previously missed – in images that have some of the most challenging conditions described earlier. At the same time, the rate of false detections is reduced by 50 percent. This means that customers such as social media apps can get consistent and reliable detections (fewer misses, fewer false detections) with higher confidence, allowing them to deliver better customer experiences in use cases like automated profile photo review. In addition, face recognition now returns 30 percent more correct ‘best’ matches (the most similar face) compared to our previous model when searching against a large collection of faces. This enables customers to obtain better search results in applications like fraud prevention. Face matches now also have more consistent similarity scores across varying lighting, pose, and appearance, allowing customers to use higher confidence thresholds, avoid false matches, and reduce human review in applications such as identity verification. As always, for use cases involving civil liberties or customer sentiments, where the veracity of the match is critical, we recommend that customers use best practices, higher confidence level (at least 99%), and always include human review.
Have a look at their blog post for some examples of what the machine can recognize as a face. Some of these are difficult enough to treat many humans, I think.
Eric Hammond shares a super-handy tip for those of us who work with Amazon AWS via the command line:
I just learned about a recent addition to aws-cli: The --query option lets you specify what parts of the response data structure you want output.
Instead of wading through pages of JSON output, you can select a few specific values and output them as JSON, table, or simple text. The new --query option is far easier to use than jq, grep+cut, or Perl, my other fallback tools for parsing the output.
Read the rest of his blog post for a few examples of how to use it.
Last week I’ve attended the AWSome Day Athens 2018 (huge thanks to Qobo for the opportunity). There aren’t that many technology events in Cyprus, so I’m constantly on the lookout for events in Europe.
AWSome Day Athens is part of the Amazon’s AWSome Day Global Series, which are one day events organized all throughout the world. The events are usually for a single day, featuring the speakers from both Amazon AWS team and some of their prominent clients from the area. AWSome Day Athens 2018 was done in partnership with Beat.