I’ve just returned from AWS re:Invent 2018, Amazon Web Services’ yearly conference showcasing new services, features, and improvements to the AWS cloud. This was the 7th year of re:Invent, and my first time attending.
The scale of the conference is staggering – held across six different Las Vegas hotels over five days, with almost 60,000 attendees this year. I expected queues, and got them. Overall though logistically the conference was well organized. Pending I queued at least 30 minutes beforehand, I was able to to make it to 95% of the sessions I planned on attending across the week.
In terms of the sessions themselves, most were very good. Over the week, I attended sixteen different sessions, made up of talks, demos, chalk talks, and hands-on sessions.
Two of my favorite sessions were ‘Optimizing Costs as you Scale on AWS’ and ‘AIOps: Steps Towards Autonomous Operations’. The former described the 5 pillars of cost optimization – Right sizing, Increasing Elasticity, Picking the Right Pricing Model, Matching Usage to Storage Class, and Measuring and Monitoring. These may seem obvious, but can often be forgotten in instances where the project is a POC that becomes production for example, or a team is not too familiar with AWS and how costs can increase as you scale up an applications usage in production. This session also included insights from an AWS customer who talked through how they had applied and governed this model in their organization, which was interesting to compare and contrast to how I’ve seen it done in the past.
I also attended numerous sessions on SageMaker, AWS’s managed machine learning service (think AML on steroids). I’m looking forward to starting to play around with SageMaker, now that I have attended a hands-on lab I am more confident beginning to look at some of the ideas I have where this could be applied. I looked at this earlier this year while completing my Masters Thesis, but ended up using Amazon Machine Learning instead in the interest of time (AML is a lot simpler to get up and running). AWS also announced Amazon SageMaker Ground Truth, which can be used to streamline the labeling process for machine learning models, via human labelling and automated labelling. One other cool announcement around ML was the launch of AWS Marketplace for Machine Learning, where you can browse 150+ pre-created algorithms and models that can be deployed directly to SageMaker. Someone may have already solved your problem!
If I was to retrospectively give myself some advice for attending re:Invent, it would be:
- Try to organize session by hotel. Moving hotels between sessions can take a long time (especially at some points of the day due to Las Vegas traffic). Organizing your sessions so that you are in the same hotel for most of the day can be beneficial. A good thing though is that there is a regular shuttle between conference venues.
- Don’t assume you will make every session. Colleagues who had previously been to re:Invent gave me this advice, but I still assumed I would make everything. Traffic, queues or something else will inevitably disrupt your schedule at some point during the week.
- Leave time for lunch! Easy to forget when you’ve got a menu of exciting talks to attend. AWS provided a grab-n-go lunch option which was very handy to just grab something between sessions.
If I had one criticism of re:Invent, it would be that some of the talks labelled as advanced did not go as deep as I expected into the technical detail. I thought the hands-on labs did a good job of this though, especially the two I attended on AWS SageMaker.
Overall, re:Invent is a significant investment in the attendees you send (tickets are not cheap, not to mind accommodation, food etc. – remember it’s held in Vegas), but a good idea if you are taking first steps with AWS, looking at getting in deeper or optimizing your usage, or thinking about migrating existing on-premise services to the public cloud.
See here for a good summary of all the re:Invent announcements, as well as the keynote videos.