AWS re:Invent 2019
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AWS is sick of waiting for your company to move to the cloud
AWS held its annual re:Invent customer conference last week in Las Vegas. Being Vegas, there was pageantry aplenty, of course, but this year’s model felt a bit different than in years past, lacking the onslaught of major announcements we are used to getting at this event.
Perhaps the pace of innovation could finally be slowing, but the company still had a few messages for attendees. For starters, AWS CEO Andy Jassy made it clear he’s tired of the slow pace of change inside the enterprise. In Jassy’s view, the time for incremental change is over, and it’s time to start moving to the cloud faster.
AWS also placed a couple of big bets this year in Vegas to help make that happen. The first involves AI and machine learning. The second, moving computing to the edge, closer to the business than the traditional cloud allows.
The question is what is driving these strategies? AWS had a clear head start in the cloud, and owns a third of the market, more than double its closest rival, Microsoft. The good news is that the market is still growing and will continue to do so for the foreseeable future. The bad news for AWS is that it can probably see Google and Microsoft beginning to resonate with more customers, and it’s looking for new ways to get a piece of the untapped part of the market to choose AWS.
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AWS launches discounted spot capacity for its Fargate container platform
AWS today quietly brought spot capacity to Fargate, its serverless compute engine for containers that supports both the company’s Elastic Container Service and, now, its Elastic Kubernetes service.
Like spot instances for the EC2 compute platform, Fargate Spot pricing is significantly cheaper, both for storage and compute, than regular Fargate pricing. In return, though, you have to be able to accept the fact that your instance may get terminated when AWS needs additional capacity. While that means Fargate Spot may not be perfect for every workload, there are plenty of applications that can easily handle an interruption.
“Fargate now has on-demand, savings plan, spot,” AWS VP of Compute Services Deepak Singh told me. “If you think about Fargate as a compute layer for, as we call it, serverless compute for containers, you now have the pricing worked out and you now have both orchestrators on top of it.”
He also noted that containers already drive a significant percentage of spot usage on AWS in general, so adding this functionality to Fargate makes a lot of sense (and may save users a few dollars here and there). Pricing, of course, is the major draw here and an hour of CPU time on Fargate Spot will only cost $0.01245364 (yes, AWS is pretty precise there) compared to $0.04048 for the on-demand price,
With this, AWS is also launching another important new feature: capacity providers. The idea here is to automate capacity provisioning for Fargate and EC2, both of which now offer on-demand and spot instances, after all. You simply write a config file that, for example, says you want to run 70 percent of your capacity on EC2 and the rest on spot instances. The scheduler will then keep that capacity on spot as instances come and go, and if there are no spot instances available, it will move it to on-demand instances and back to spot once instances are available again.
In the future, you will also be able to mix and match EC2 and Fargate. “You can say, I want some of my services running on EC2 on demand, some running on Fargate on demand, and the rest running on Fargate Spot,” Singh explained. “And the scheduler manages it for you. You squint hard, capacity is capacity. We can attach other capacity providers.” Outpost, AWS’ fully managed service for running AWS services in your data center, could be a capacity provider, for example.
These new features and prices will be officially announced in Thursday’s re:Invent keynote, but the documentation and pricing is already live today.
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Verizon and AWS announce 5G Edge computing partnership
Just as Qualcomm was starting to highlight its 5G plans for the coming years, Verizon CEO Hans Vestberg hit the stage at AWS re:Invent to discuss the carrier’s team up with the cloud computing giant.
As part of Verizon’s (TechCrunch’s parent company, disclosure, disclosure, disclosure) upcoming focus on 5G edge computing, the carrier will be the first to use the newly announced AWS Wavelength. The platform is designed to let developers build super-low-latency apps for 5G devices.
Currently, it’s being piloted in Chicago with a handful of high-profile partners, including the NFL and Bethesda, the game developer behind Fallout and Elder Scrolls. No details yet on those specific applications (though remote gaming and live streaming seem like the obvious ones), but potential future uses include things like smart cars, IoT devices, AR/VR — you know, the sorts of things people cite when discussing 5G’s life beyond the smartphone.
“AWS Wavelength provides the same AWS environment — APIs, management console and tools — that they’re using today at the edge of the 5G network,” AWS CEO Andy Jassy said onstage. Starting with Verizon’s 5G network locations in the U.S., customers will be able to deploy the latency-sensitive portions of an application at the edge to provide single-digit millisecond latency to mobile and connected devices.”
As Verizon’s CEO joined Vestberg onstage, CNO Nicki Palmer joined Qualcomm in Hawaii to discuss the carrier’s mmwave approach to the next-gen wireless. The technology has raised some questions around its coverage area. Verizon has addressed this to some degree with partnerships with third-parties like Boingo.
The company plans to have coverage in 30 U.S. cities by end of year. That number is currently at 18.
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New Amazon tool simplifies delivery of containerized machine learning models
As part of the flurry of announcements coming this week out of AWS re:Invent, Amazon announced the release of Amazon SageMaker Operators for Kubernetes, a way for data scientists and developers to simplify training, tuning and deploying containerized machine learning models.
Packaging machine learning models in containers can help put them to work inside organizations faster, but getting there often requires a lot of extra management to make it all work. Amazon SageMaker Operators for Kubernetes is supposed to make it easier to run and manage those containers, the underlying infrastructure needed to run the models and the workflows associated with all of it.
“While Kubernetes gives customers control and portability, running ML workloads on a Kubernetes cluster brings unique challenges. For example, the underlying infrastructure requires additional management such as optimizing for utilization, cost and performance; complying with appropriate security and regulatory requirements; and ensuring high availability and reliability,” AWS’ Aditya Bindal wrote in a blog post introducing the new feature.
When you combine that with the workflows associated with delivering a machine learning model inside an organization at scale, it becomes part of a much bigger delivery pipeline, one that is challenging to manage across departments and a variety of resource requirements.
This is precisely what Amazon SageMaker Operators for Kubernetes has been designed to help DevOps teams do. “Amazon SageMaker Operators for Kubernetes bridges this gap, and customers are now spared all the heavy lifting of integrating their Amazon SageMaker and Kubernetes workflows. Starting today, customers using Kubernetes can make a simple call to Amazon SageMaker, a modular and fully-managed service that makes it easier to build, train, and deploy machine learning (ML) models at scale,” Bindal wrote.
The promise of Kubernetes is that it can orchestrate the delivery of containers at the right moment, but if you haven’t automated delivery of the underlying infrastructure, you can over (or under) provision and not provide the correct amount of resources required to run the job. That’s where this new tool, combined with SageMaker, can help.
“With workflows in Amazon SageMaker, compute resources are pre-configured and optimized, only provisioned when requested, scaled as needed, and shut down automatically when jobs complete, offering near 100% utilization,” Bindal wrote.
Amazon SageMaker Operators for Kubernetes are available today in select AWS regions.
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