Drentin’s Findings

You’ve watched the live streams in previous years, now it’s time to attend the live event! I was lucky enough last year to attend the one and only re:Invent for the first time - after watching many online I was pumped to get my feet on the ground in Vegas and start learning, for better or worse! Builder Sessions - Where you’ll find me At re:Invent there’s thousands of sessions, which fall under particular categories - one in particular that had me hooked - Builders’ Sessions!...

Starting last week I felt like I was ready to jump into attempting to work off the knowledge I had built up doing the examples previously and attempt the main goal; to create a model that was capable of recognising some of the Japanese Hiragana character set. Problem - Input Data! My initial problem that I had been playing with was the idea of a training data set - where would I get a good set of training data from?...

What we all came here for… SageMaker! I was excited to get stuck into the Advanced recipe - Build a custom ML model to sort trash as this started getting into the parts I wanted to know more about; how to get a basic model trained in SageMaker and then deploy it to the DeepLens device. Step 1 - Train! Luckily in this example they include a number of sample images, quite a decent set really with over 500 images in total, seperate into Compost, Landfill and Recycling....

Beginnings start here! Hello all and welcome to my first article around my attempts to create an Amazon SageMaker-based solution, focusing on image detection. I am participating in an initiative as part of my company’s AWS Community of Practice. The idea is inspired loosely by A Cloud Guru’s How to Build a Netflix Style Recommendation Engine with Amazon SageMaker Challenge and I have got my hands on a AWS DeepLens so I’m going to see what I can do with both of these to the best of my ability!...