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AWS re:Invent Keynote Recap – Thursday

Thursday’s General Session Keynote kicked off with Amazon CTO, Werner Vogels, taking the stage to deliver additional product and services announcements with the inclusion of deeper, technical content.  Revisiting his vision for 21st Architectures from the 1st Re:Invent in 2012, Werner focused on what he sees as key guiding principles for next-gen workloads.

  1. Voice represents the next major disruption in computing. Stressing this point, Werner announced the general availability of Alexa for Business to help improve productivity by introducing voice automation into your business.
  2. Use automation to make experimentation easier
  3. Encryption is the ‘key’ to controlling access to your data. As such, encrypting data (at rest and in transit) should be a default behavior.
  4. All the code you should ever write is business logic.

 

Werner also highlighted the fact that AWS now has over 3,951 new services released since 2012. These services were not built for today but built for the workloads of the future.  The goal for AWS, Werner says, is to be your partner for the future.

One of the highlights of the keynote was when Abby Fuller, evangelist for containers at AWS, came on stage to talk about the future of containers at AWS.  She demoed the use of Fargate which is AWS’s fully managed container service. Think of Fargate as Elastic Beanstalk but for containers.  Per AWS documentation “It’s a technology that allows you to use containers as a fundamental compute primitive without having to manage the underlying instances. All you need to do is build your container image, specify the CPU and memory requirements, define your networking and IAM policies, and launch. With Fargate, you have flexible options to closely match your application needs and you’re billed with per-second granularity.”

The Cloud9 acquisition was also a highlight of the keynote.  Cloud9 is a browser-based IDE for developers.  Cloud9 is completely integrated with AWS and you can create cloud environments, develop code, and push that code to your cloud environment all from within the tool.  It’s really going to be useful for writing and debugging lambda functions for developers that have gone all in on serverless technologies.

New Announcements

AWS Lambda Function Execution Activity Logging – Log all execution activity for your Lambda functions. Previously you could only log events but this allows you to log data events and get additional details.

AWS Lambda Doubles Maximum Memory Capacity for Lambda Functions – You can now allocate 3008MB of memory to your AWS Lambda functions.

AWS Cloud9 –  Cloud9 is a cloud based IDE for writing, running, and debugging your code.

API Gateway now supports endpoint integrations with Private VPCs –  You can now provide access to HTTP(S) resources within your Amazon Virtual Private Cloud (VPC) without exposing them directly to the public Internet.

AWS Serverless Application Repository –   The Serverless Application Repository is a collection of serverless applications published by developers, companies, and partners in the serverless community.

We expect AWS to announce many more awesome features and services before the day ends so stay tuned for our AWS  re:Invent 2017 Products & Services Review and 2017 Conference Recap blog posts for a summary of all of the announcements that are being delivered at AWS re:Invent 2017.

 

— Brent Clements, Sr. Cloud Consultant, 2nd Watch

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AWS re:Invent Keynote Recap – Wednesday

I have been looking forward to Andy Jassy’s keynote since I arrived in Las Vegas. Like the rest of the nearly 50k cloud-geeks in attendance, I couldn’t wait to learn about all of the cool new services and feature enhancements that will be unleashed that can solve problems for our clients, or inspire us to challenge convention in new ways.

Ok, I’ll admit it. I also look forward to the drama of the now obligatory jabs at Oracle, too!

Andy’s 2017 keynote was no exception to the legacy of previous re:Invents on those counts, but my takeaway from this year is that AWS has been able to parlay their flywheel momentum of growth in IaaS to build a wide range of higher-level managed services. The thrill I once got from new EC2 instance type releases has given way to my excitement for Lambda and event-based computing, edge computing and IoT, and of course AI/ML!

AWS Knows AI/ML

Of all the topics covered in the keynote, the theme that continues to resonate throughout this conference for me is that AWS wants people to know that they are the leader in AI and machine learning. As an attendee, I received an online survey from Amazon prior to the conference asking for my opinion on AWS’s position as a leader in the AI/ML space. While I have no doubts that Amazon has unmatched compute and storage capacity, and certainly has access to a wealth of information to train models, how does one actually measure a cloud provider’s AI/ML competency? Am I even qualified to answer without an advanced math degree?

That survey sure makes a lot more sense to me following the keynote as I now have a better idea of what “heavy lifting” a cloud provider can offload from the traditional process.

Amazon has introduced SageMaker, a fully managed service that enables data scientists and developers to quickly and easily build, train, and deploy machine learning models at any scale. It integrates with S3, and with RDS, DynamoDB, and Redshift by way of AWS Glue. It provides managed Jupyter notebooks and even comes supercharged with several common ML algorithms that have been tuned for “10x” performance!

In addition to SageMaker, we were introduced to Amazon Comprehend, a natural language processing (NLP) service that uses machine learning to analyze text. I personally am excited to integrate this into future chatbot projects, but the applications I see for this service are numerous.

After you’ve built and trained your models, you can run them in the cloud, or with the help of AWS Greengrass and its new machine learning inference feature, you can bring those beauties to the edge!

What is a practical application for running ML inference at the edge you might ask?

Dr. Matt Wood demoed a new hardware device called DeepLens for the audience that does just that! DeepLens is a deep-learning enabled wireless video camera specifically designed to help developers of all skill levels grow their machine learning skills through hands-on computer vision tutorials. Not only is this an incredibly cool device to get to hack around with, but it signals Amazon’s dedication to raising the bar when it comes to AI and machine learning by focusing on the wet-ware: hungry minds looking to take their first steps.

Andy’s keynote included much more than just AI/ML, but to me, the latest AI/ML services that were announced on Tuesday represent the signal of Amazon’s future of higher-level services which will keep them the dominant cloud provider into the future.

 

–Joe Conlin, Solutions Architect, 2nd Watch

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