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Top Key Takeaways from AWS re:Invent 2018

AWS re:Invent, arguably the biggest and most important event in the cloud computing calendar, has just wrapped up for 2018. The event, which was held in Las Vegas, attracted over 50,000 delegates covering customers,partners and Amazon employees for keynote sessions, workshops and networking.

During the event, AWS made significant announcements across all of its services. We can’t discuss all of them here, but I’ve highlighted top takeaways from the event below:

Machine Learning to remain a key focus area:

AWS has gone back to its core mission to “Put machine learning in the hands of every developer” and made it clear that company’s commitment to Machine Learning is ongoing. AWS boasted the success of SageMaker stating that it is being used by over 10,000 customers despite only existing for one year. The AWS Inferentia Inference Chip signified another step in the direction of Machine Learning commitment for Amazon Web Services.

Swami Sivasubramanian, VP of AI & Machine Learning at AWS, referenced how AWS customer Intuit cut down its development time significantly to build machine learning models like personalisation, fraud detection by leveraging the power of SageMaker, resulting in a huge productivity gain. Another key takeaway which came out of his speech was of AWS launching 200 new product features and services under its machine learning practice since this time last year.

Taking “Cloud” closer to customer:

Among pubic cloud service providers, AWS boasts of double the share of its next nine largest competitors in Infrastructure-as-a-Service market and with an intent to further increase the same it made some significant announcements in re:Invent 2018. The aim of such announcements is to lower the costs, speed-up ML training and enable high-performance computing to the cloud.

1. Substantial cost savings for cloud workloads

For the first time,AWS’ EC2 platform will offer virtual machines (VMs) running on Arm-based processors, via the new A1 instances. 

The new Amazon EC2 A1 instances could deliver significant cost savings for scale-out and Arm-based applications such as web servers, containerised micro services, caching fleets, and distributed data stores that are supported by the extensive Arm ecosystem.

2. Accelerated Machine Learning training for huge datasets

Demand for machine learning infrastructure is exploding, and AWS has announced new heavy-duty instances designed to reduce training time for machine-learning models.

The new P3dn instances promise to reduce training time to less than an hour in some circumstances, according to AWS.

An upgrade on the existing P3 instances, P3dn boosts the amount of data that can be shuttled from attached storage, such as Amazon S3 or Amazon EFS, to the GPUs used for training.

3. Bringing High Performance Computing (HPC) to the cloud

AWS has announced anew service called Elastic Fabric Adapter (EFA) that is designed to bring high-performance computing (HPC) to the cloud, making it more attractive to customers running high-performance computing workloads such as computational fluid dynamics,weather modelling, and reservoir simulation etc.

The new Elastic Fabric Adapter allows AWS virtual machines to share data over low-latency interlinks. EFA is integrated with the Message Passing Interface, which AWS says allows HPC applications to scale to tens of thousands of CPU cores without any modification.

4. Making IoT applications easier to build & run on cloud

To support IoT and edge computing deployments, AWS has revealed a suite of new tools. These tools would help its customers build and run IoT applications on cloud in much easier way:

AWS IoT SiteWise is a managed service that collects, organizes and structures data collected by IoT devices in industrial facilities, so it can be used to analyze equipment and performance data.

AWS IoT Events is another managed service that monitors IoT sensors and applications to help detect problems such as malfunctioning equipment and automatically triggers actions and alerts.

AWS IoT Things Graph provides a drag-and-drop tool for linking devices like sensors to services

AWS IoT Greengrass Connectors allows developers to connect to third-party services such as ServiceNow or Splunk via common APIs.

5. Helping enterprises manage their global services

Enterprises that run on a global scale need to depend on series of infrastructure endpoints situated in multiple locations across the world.

The newly announced AWS Global Accelerator is a service that will automatically route users of an AWS-based service to the best endpoint for them, based on their location,application health, and customer-specific configurations.

AWS Global Accelerator also allocates a set of static Anycast IP addresses that are unique per application and do not change, removing the need to update clients as the application scales.

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