how to avoid trying to fly the plane before building it

Introduction

For this blog, I’m going to largely ignore the difficulty of publishing and maintaining a machine learning model in a production environment. Search “MLOps” on google and you’ll learn rather quickly how production environments create logistical issues for the machine learning development lifecycle. The problem I’m referring to isn’t a logistical issue. It’s an industry issue. More specifically it’s a people issue. Let me take you on a journey.


Image classification is a hot topic in the medical industry and beyond. At the heart of a CNN model is the spatial distribution of information via pixels. Before diving into the model I created let’s take a quick detour back to get some motivation for how a CNN works. Feel free to skip if their old hat at this point.

How does a CNN work (High Level)

As humans, we don’t often take the time to appreciate the true complexity of being able to see something, process it, and turn that into understanding. I recommend to everyone reading this post to take 5 minutes and google what…


So you’re new to data science. Maybe you’ve worked in the tech industry and want to get more insight into the machine learning world. Maybe you’re outside the industry entirely and are desperately trying to transition in (I get your pain). The question is where to start. Well congratulations, you’ve stumbled on one of the questions that have been plaguing humanity at large since the invention of farming.


Keras is my favorite tool in the neural network world. Yeah, yeah it’s not the fully customizable, maximum optimized super dee duper neural network thingy. It doesn’t have to be either, but more on that in a future blog…


Introduction:

In short, I believe it can, but more work needs to be done. Let me explain.

Michael Mahoney

I love life, family, math and the internet. I’ve done everything from academic research to digging holes. I can be stubborn but always try to keep and open mind

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