A winning recipe for machine learning? Focus on the ingredients, not the kitchen

A winning recipe for machine learning? Focus on the ingredients, not the kitchen

Published
August 2019

You’ve undoubtedly heard quite a bit about marketing powered by machine learning in the last two years. But maybe you’re still a bit mystified by it. Where do you even begin to get your marketing organization started? Cassie Kozyrkov is the chief decision scientist with Google Cloud, and she’s got an interesting take for marketers who may be overwhelmed by it all.

The first step is to realize the difference between the research side of machine learning and the applied side, she says. It’s sort of like building kitchen appliances vs. cooking at scale. “Just because you know how to build a microwave doesn’t make you a great chef and vice versa.”

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Machine learning

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is about automating the ineffable.

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It is about unlocking a second mode of communication

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with machines.

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I think that's incredibly powerful.

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I'm Cassie Kozyrkov.

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I serve as chief decision scientist.

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I'm with Google Cloud.

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What I'm really passionate about is turning information

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into better action.

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I work on decision intelligence.

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It's the applied side of machine learning.

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In a nutshell, machine learning is

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about explaining your wishes to computers in a different way

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from traditional programming.

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So think about it like this.

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Traditional programming-- to get a computer

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to do something for you, you have

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to give explicit instructions to the computer.

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Machine learning is asking for what you want with examples

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instead of instructions.

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The biggest challenge that I see marketers face with machine

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learning is how to get started.

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There's the research side of machine learning.

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And then there's the applied side.

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And the problem area for a lot of businesses

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is that they don't know which one they want to be in.

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The research side is all about building machine

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learning algorithms for other people to use.

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That's a lot like building kitchen appliances,

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whereas applied machine learning is

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all about cooking and cooking at scale.

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And that is a completely different art.

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Just because you know how to build the microwave,

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doesn't make you a great chef and vice versa.

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And so what they end up doing-- and this

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is the big mistake of why businesses and marketers will

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fail-- is they think they need to build their own microwave

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in order to cook.

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They don't realize that to apply machine learning

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to a specific business problem, you

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don't need to reinvent the wheel and build that algorithm

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yourself.

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There are warehouses upon warehouses of these algorithms

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created by research teams.

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And once it's created, everyone else can take it off the shelf

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and start applying it to innovating in their models.

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The key to success here is rigorous testing,

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making sure that what you asked for has been delivered.

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Keep testing the model on new data to see if it's working.

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And bear in mind, there's only so much

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that the hottest algorithm can help you

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with if your data isn't very good.

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Put your effort into making sure that your data,

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your ingredients are relevant to the task.

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And if you have the most beautiful ingredients,

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you can get away with a very simple kitchen.

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Machine learning is like having a billion extra interns, not one Einstein, to make effective ads