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De Clercq Wentzel from AWS

Well, we were super lucky. I was excited before as you can’t get more hackathon than a Robocar Rally which this was in the session list as. During the Andy Jassy keynote this morning, DeepRacer was announced which is a machine learning car.

I’m the first to admit I haven’t done much machine learning so this appealed to me as it was for developers with no prior ML or robotic experience.

You can play along: https://github.com/aws-samples/aws-deepracer-workshops

We joined up pit crews, I teamed up with fellow UK VMUGer and Virtual Design Master winner Chris Porter (I’m not ashamed to grab onto the knowledge coattails when needed!).

The idea was to use machine learning to train the car in a virtual racetrack built in RoboMaker to learn to stay on track. One the training model was done, the data could be downloaded to the actual car and then the car would attempt to race a real track using the learning model from the training data.

Reinforcement Learning

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Dustin Noyes & Gabriel Hollombe from AWS

Another workshop, so my fingers have been getting a workout today, and no easy passive listening to speaker sessions for me!

This workshop was another one I didn’t have any previous knowledge of and seemed interesting to learn something new like my previous session: AWS re:Invent 2018: Get Started with Deep Learning and Computer Vision Using AWS DeepLens – AIM316

You can play along at home with good instructions https://amplify-workshop.go-aws.com

We had to build a data-driven web app using React that lets users upload to shared, secure photo galleries. <sidebar>I’m struggling enough with photo management at home as it is, multiple people, too many photos and complicated ways to sync between devices, back them up and make them available to view! </sidebar>

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Aleksandar Simovic – a software engineer from ScienceExchange
Slobodan Stojanovic – CTO of Cloud Horizon

This workshop piqued my interest as a little peak into the future of creating serverless applications. Today, we can already use existing CloudFormation templates or the Serverless Application Repository (SAM) to spin up a serverless application quickly without having to write any new code although those templates definitely have a lots of text config in them. Surely we can do more though and why not bring some other AWS toys into the mix and create some Alexa magic to help Iron Man with a Jarvis type skill!

In the hands-on workshop, we were to use the SAM along with Amazon Alexa, Lex and SageMaker to create “Jarvis”. I’ve never worked with Lex and Sagemaker before so a good learning opportunity.

You can attempt to play along yourself (although read on first): https://github.com/simalexan/jarvis-workshop

Aleksandar went through an intro to Alexa saying we currently use Alexa to get information but the dream is to get Alexa to build something and this workshop would be using a virtual assistant.

There were 5 steps: (apologies for poor quality pictures)

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Kashif Imran and Jyothi Nookula from AWS

I wanted to attend a workshop on something I don’t normally deal with. This workshop was all about deep learning so we’re into the work of AI! The idea was to learn how to build and deploy computer vision models using the AWS DeepLens deep learning-enabled video camera. This was then extended to build a machine learning application and a model from scratch using Amazon SageMaker. I would land up with and end-to-end AI application. I felt getting into AI using computer vision as an example is a good way to “see” what is possible with AI.

You can follow along at home but would need a DeepLens camera (we did receive a discount code, I may have had an urge to get one!) https://github.com/darwaishx/deep-learning-with-deeplens-reinvent-2018

This was a well setup workshop with lots of space. I was in the walk-up line as the registration was full but after missing so many sessions last year due to a suboptimal AWS Human Queuing Service, its great this year people are able to see more. Each seat had a workstation setup with a DeepLens camera.

There were multiple parts to the workshop:

1. Introduction to Deep Learning and DeepLens.
2. Create and deploy object detection project to DeepLens.
3. Train an object detection model using Amazon SageMaker.
4. Extend DeepLens object detection project to identify people who are not wearing safety hats at construction site.
5. Analyse results using IoT and CloudWatch.

Machine Learning overview

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