Originally published in Eugeneyan.
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.
I’m not alone in saying this.
Guess what’s Rule #1 in Google’s 43 Rules of Machine Learning?
Rule #1: Don’t be afraid to launch a product without machine learning.
Machine learning is cool, but it requires data. Theoretically, you can take data from a different problem and then tweak the model for a new product, but this will likely underperform basic heuristics. If you think that machine learning will give you a 100% boost, then a heuristic will get you 50% of the way there.
Several machine learning practitioners—who I interviewed as part of Applying ML—also gave similar advice in response to this question: “Imagine you’re given a new, unfamiliar problem to solve with machine learning. How would you approach it?”
I’d first try really hard to see if I could solve it without machine learning :D. I’m all about trying the less glamorous, easy stuff first before moving on to any more complicated solutions. — Vicki Boykis, ML Engineer @ Tumblr
I think it’s important to do it without ML first. Solve the problem manually, or with heuristics. This way, it will force you to become intimately familiar with the problem and the data, which is the most important first step. Furthermore, arriving at a non-ML baseline is important in keeping yourself honest. — Hamel Hussain, Staff ML Engineer @ Github
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