Hey everyone, I've been trying to learn MLOps for a small side project and finally got around to testing Weights & Biases like everyone recommends. I set it up to track a simple image classification model, but my dataset is only like 2,000 pictures.
Here's my maybe-obvious realization: for a project this size, I spent more time figuring out the W&B integration, the config setup, and what all the charts *mean* than I did on the actual model code. The live loss curves and confusion matrices are super cool and professional looking, but with such a small dataset, the training run finishes in minutes. I found myself just looking at the final accuracy in my terminal anyway.
It feels like using a full-scale factory dashboard to monitor a single toaster. The value seems to be in comparing hundreds of runs or tracking huge experiments, right? For my tiny project, it was overkill.
Am I missing something? Are there specific W&B features that are still worth it for small experiments? Or is the real point to build the habit for when projects get bigger? Would love some perspective from those of you with more experience.