Prvo pravilo mašinskog učenja: Počnite bez njega

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Applying machine learning effectively is tricky. You need data. You need a robust pipeline to support your data flows. And most of all, you need high-quality labels. As a result, most of the time, my first iteration doesn’t involve machine learning at all.

Regardless of whether you’re using simple rules or deep learning, it helps to have a decent understanding of the data. Thus, grab a sample of the data to run some statistics and visualize! (Note: This mainly applies to tabular data. Other data such as images, text, audio, etc. can be tricker to run aggregate statistics on.)

When should we use machine learning then? After you have a non-ML baseline that performs reasonably well, and the effort of maintaining and improving that baseline outweighs the effort of building and deploying an ML-based system.

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