Earlier this month, I had my first opportunity to attend AWS re:Invent as a customer, rather than an employee. It was fantastic! Kudos to the AWS events team for all the work put into making re:Invent a reality amid a global pandemic.
re:Invent was massive. Was it perfect? Of course not. However, I really enjoyed a few keynotes. Werner Vogels (CTO of AWS) who shared the spectrum of new innovative areas that AWS was pioneering from Project Kuiper (AWS) a low Earth orbit (LEO) satellite system designed to provide fast, affordable broadband to Swami Sivasubramanian's keynote on Machine Learning were fascinating. It was less about new services or features launched and more about how they view the future of the cloud.
I caught up with many ex-AWS colleagues, having great conversations over, what might have been, too many glasses of wine. I also had the opportunity to meet with product leadership for many AWS Services. As part of the executive track, I was able to meet with Werner (CTO of AWS), Swami (VP of Analytics and AI), and Matt Garman (SVP of AWS Sales) as their customer. I’ve worked with all of them back when I was at AWS, but there’s something really nice about sitting on the other side of the table and asking the hard questions!
Outside of all the presentations, chalk talks, leadership and customer meetings, I was able to participate in a half-day hack/coding session. It was a great time to get my hands dirty and build something quick. I love being a builder. :)
The conference showcased a lot of new services and features, but there were two noticeable announcements that I found compelling.
AWS is Going “All In” on Networks
I was so excited to see this launch, it was many years in the making! AWS has been the de facto standard in cloud since there has been cloud with Azure and GCP continually looking up at them. In the great AWS vs. Azure vs. Google cloud competition, AWS is the leader hands down. AWS has, by far, the broadest set of services, which includes everything from app development tools to artificial intelligence to contact center and now networking.
AWS announced its own private 5G offering where customers can build, provision, and operate a 5G network via the AWS console in a subscription model. AWS Private 5G is an excellent option for companies that prefer a turnkey managed service. The other network announcement was the launch of Cloud WAN, which is a WAN service that is also managed through the AWS console. Customers can build a network, using the AWS global network, or from their carrier. A point and click interface reduces much of the complexity in operating a global network.
Lowering the Technology Barrier for ML Adoption
I’m all for lowering the barrier for companies to benefit from machine learning.
There is a growing set of companies facing business problems and dealing with data on a daily basis. It’s going to be vital for these companies to be able to use this data to predict business outcomes, and the only scalable way of doing that is through machine learning. This ability lets you solve problems and move faster by automating slow processes and embedding intelligence in your IT systems.
The aim is for business analysts to be able to build machine learning models and generate accurate business predictions without writing code or requiring ML expertise.
But how do you ensure that all teams and individual decision makers in the organization are empowered to create these machine learning (ML) systems at scale and without depending on other data science and data engineering teams?
This is where Amazon SageMaker Canvas comes in. Canvas is a visual, no-code capability that allows companies to build ML models and generate accurate predictions without writing code or requiring ML expertise. First, its user interface is really nice. It lets users browse and access disparate data sources in the cloud or on-premises, combine data sets, train accurate models, and then generate new predictions once new data is available.