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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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