What you need to know about AWS’s re:Invent 2020 (so far)

What it is about and why it matters to us?

Amazon Web Service (AWS)’s annual re:Invent conference is one of the most anticipated event in the global cloud computing community. This year, it is a 3-week conference starting from November 30 to December 18. Like many other events, it is being held virtually.

Though the event is still taking place, many new products and features have already been announced. In this post, we have summarized for you the biggest announcements made so far and how we may benefit from them.

Looking at the new releases this year, we can see there are two main areas in which AWS has heavily invested in, integrating machine learning into cloud application and simplifying cloud development journey. Below here, we have listed a number of exciting products for both areas, in no particular order.

Machine Learning onto Cloud

Data analytics have been a vital tool for a company to understand its product performance and make data-driven decisions. However, for decades, it relies on data analysts or software engineers to issue database queries or run data pipelines to extract information from data warehouse. It adds a few hops between the data sources and users of the data, such as sales manager. AWS QuickSight Q simplify the process by enabling the users to issue natural languages questions to get back the information they need. It makes the data much more accessible to decision makers, such as sale managers.

Credit: https://aws.amazon.com/blogs/aws/amazon-quicksight-q-to-answer-ad-hoc-business-questions/
Credit: https://aws.amazon.com/blogs/aws/amazon-quicksight-q-to-answer-ad-hoc-business-questions/

As the system grows more and more complicated, it can be a challenging task to properly monitor and detect system abnormalities, not to mention fix them in a timely manner. AWS teams on the other side has gathered a lot of related experiences running Amazon and AWS itself for years. DevOps Guru is a machine learning model that leverages AWS’ years of experiences monitoring and maintaining those large distributed systems. It identifies patterns that could indicate system issues and recommends possible fixes to the developers.

Following are some screenshots of DevOps Guru.

Credit: https://aws.amazon.com/blogs/aws/amazon-devops-guru-machine-learning-powered-service-identifies-application-errors-and-fixes/
Credit: https://aws.amazon.com/blogs/aws/amazon-devops-guru-machine-learning-powered-service-identifies-application-errors-and-fixes/

Besides using machine learning to improve its own products, AWS has provided a few services to make machine learning more accessible to the developers. We will summarize a few that may be useful.

  • Panorama

Panorama service together with Panorama SDK enables the developers quickly integration computer vision capabilities with edge devices, such as Phones.

  • Lookout

Lookout is a service for users to detect defects on production units and equipments. For instance, the service can quickly spot the defects in the figure below which can take even expertises some time.

Credit: https://aws.amazon.com/blogs/aws/amazon-lookout-for-vision-new-machine-learning-service-that-simplifies-defect-detection-for-manufacturing/

Simplify Cloud Development Journey

Additionally, AWS has made quite some improvements to existing products and introduced new services to simplify the development journey.

  • Mac EC2 instance

Developers will be able to rent a EC2 Mac instance to develop iOS, Mac, tvOS and watchOS apps.

  • EC2 and EKS anywhere

This allows the developers to run EC2 and Elastic Kubernetes Service (EKS) in their own data center. This could be a big step towards simplifying managing private cloud and hybrid cloud with on-premises and public cloud instances.

  • Cross-bucket replication

S3 has provided support for same-region and cross-region replication. However it is a cumbersome experience if the developer wants to replicate the data into different buckets. Developers have to create their own solutions with event monitoring and lambda. Now developers could have this with a few click of buttons.

  • Strong Read-after-write consistency

S3 employed an eventual-consistency model. If you issue a GET request after a PUT request, you may get back the stale value instead of the value you set in PUT request. This could add extra complexities to the development, especially for big data workloads where downstream operations need to read the data written by the upper-stream operations. Now S3 provides stronger consistency to simplify the development.

A list of released products can be found here.

80% System; 20% ML

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