Week 10: Preparing the Presentation

This week I worked on my final presentation. I started the presentation with the problem, so the audience understands why any of this matters, before I start explaining model architectures. I started with the background of water quality testing, and how current detection methods require mass spectrometers that are too expensive for low-resource areas. 

From there, I walked through how nanopore sequencing works, what a basecaller does, and why current basecallers are a problem for field deployment. Then I explained my approach: explore the data, benchmark existing models, build baselines from scratch, research compression, and apply it to a pre-trained model. 

Then came the results section. For magnitude pruning,  I focused on the cliff between 30% and 40% sparsity, because that is the most intuitive result to explain. The model tolerates losing some weights, but past a certain point, it collapses fast. For weight sharing, I focused on the K=128 result, 4.6x storage compression with only 0.2% accuracy loss, because that is the clearest win from the whole project. 

The conclusion focused on two points. Simpler recurrent architectures like the GRU and Minimal RNN outperformed the LSTM, which suggests production basecallers are overbuilt for this task. And stacking compression techniques without retraining does not work. If I continued the project, the next steps would be training-aware compression, like delayed masking and quantization-aware training, a bigger dataset, and actual testing on a Raspberry Pi.

After putting the slides together, I did a few practice runs to work on timing and make sure the explanations were clear enough for someone without a machine learning background.