We may have bitten off more than we could chew, folks.
An Amazon engineer told me that when he heard what I was trying to do with Ars headlines, the first thing he thought was that we had chosen a deceptively hard problem. He warned that I needed to be careful about properly setting my expectations. If this was a real business problem… well, the best thing he could do was suggest reframing the problem from “good or bad headline” to something less concrete.
That statement was the most family-friendly and concise way of framing the outcome of my four-week, part-time crash course in machine learning. As of this moment, my PyTorch kernels aren’t so much torches as they are dumpster fires. The accuracy has improved slightly, thanks to professional intervention, but I am nowhere near deploying a working solution. Today, as I am allegedly on vacation visiting my parents for the first time in over a year, I sat on a couch in their living room working on this project and accidentally launched a model training job locally on the Dell laptop I brought—with a 2.4 GHz Intel Core i3 7100U CPU—instead of in the SageMaker copy of the same Jupyter notebook. The Dell locked up so hard I had to pull the battery out to reboot it.
But hey, if the machine isn’t necessarily learning, at least I am. We’re almost at the end, but if this were a classroom assignment, my grade on the transcript would probably be an “Incomplete.”
The gang tries some machine learning
To recap: I was given the pairs of headlines used for Ars articles over the past five years with data on the A/B test winners and their relative click rates. Then I was asked to use Amazon Web Services’ SageMaker to create a machine-learning algorithm to predict the winner in future pairs of headlines. I ended up going down some ML blind alleys before consulting various Amazon sources for some much-needed help.
