This is my Day 1 report for the AWS Summit which is an all Online event this year.
Even if the event was a real face-to-face conference, I would still have been attending the sessions online, since coincidentally, I've very recently contracted Covid, and I am still recovering as I'm typing this report.
I make no secret that my goal in the next 12 months is to build up Machine Learning skills and find projects that will exploit these newfound skills. So many of my learning activities this past year have been about this field, and the effort is still continuing. Imagine my surprise when I found out that the AWS Summit has a good chunk of sessions focusing solely on AI/ML!
Out of about 104 sessions, 28 are about AI/ML, I thought that was great, so this for year's AWS Summit, that's where my focus will be. Obviously I could not have attended all 28 sessions, just the ones that I fancied and handpicked from the agenda.
The first session is a peek into how Canva approached scaling ML in their company.
This can be summarized into 5 main points, please see image below. When the AWS Summit videos are made available, I will post their links here.
Team Enablement - involves defining culture, team training (and hackathon), and tools
Data Strategy - involves training, best (data) practice
Proof of Concept - involves training, business alignment, cross functional teams
Repeatability - involves training, tools, cloud enablement, MLOps
Scale - involves training, Center of Excellence, optimization
AWS and Intel have a deep partnership and together provide solutions to real world customer problems. Many of the these are related to AI and ML, such as in the development of hardware chips that provide faster, more powerful performance that is cheaper and more sustainable to run.
This session introduces the concept of Machine Learning Operations (MLOps) in Data Science/Machine Learning teams. As the team's AI maturity improves, it becomes more and more important to integrate MLOps practices into your workflow to avoid the problems of POCs not making it into production.
Issues like manual intervention for changes, work stuck in Jupyter notebooks, not enabling autoscaling, or having no feedback on model performance are only some of the issues that contribute to the failure of your ML system.
We were then presented with 3 MLOps architectures, from Small, Medium and Large, which represents architectures that one would adopt, depending on your team's AI maturity, and budget.
In re:Invent 2021, AWS announced that Sustainability as the latest pillar added to the 5 existing: Operational Excellence, Security, Reliability, Performance Efficiency and Cost Optimization. It was nice to see sustainability being more and more involved in presentations, so it was one that I was looking forward to hear.
Leadership is crucial if sustainability initiatives are to succeed. To adopt a sustainability mindset, direction from leaders is a requirement. From there the company can enable teams, adopt a powerful data platform such as AWS, and finally choosing the right first project.
The session also showed an example in an industrial setting of one Amazon.com's buildings. The problem was to improve the current system's power consumption by 8% and achieved that by creating a model based on Reinforcement learning and Amazon SageMaker suite of tools.
Not everyday does one have the chance to develop systems used in autonomous vehicles and AWS have just given every developer the ability to do so in DeepRacer! Using Amazon SageMaker and Intel's OpenVINO toolkit, Reinforcement learning can be used to train your models, after which it can be run in the DeepRacer 3D racing simulator.
Also, AWS has purpose-built a real 1/18th scale race car, so that you can use it to download your models to, and race against the autonomous racing league.
That's a wrap for Day 1 of the AWS Summit 2022 Australia and New Zealand. I've got two highlights for today.
First is seeing how AWS SageMaker is being positioned by Amazon at the center of their AI/Machine Learning story - as an end to end MLOps framework. When I learned about Data Science in a course I completed towards the end of 2021, I was not made aware of such tool available, and rightly so. It would have made the course more difficult than it was.
And next was seeing DeepRacer - truly making Machine Learning (Reinforcement Learning) accessible to developers the world over.
Keep an eye out for my Day 2 wrap later!