A winning recipe for machine learning? Focus on the ingredients, not the kitchen
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A winning recipe for machine learning? Focus on the ingredients, not the kitchenAugust 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.”
is about automating the ineffable.
It is about unlocking a second mode of communication
I think that's incredibly powerful.
I'm Cassie Kozyrkov.
I serve as chief decision scientist.
I'm with Google Cloud.
What I'm really passionate about is turning information
into better action.
I work on decision intelligence.
It's the applied side of machine learning.
In a nutshell, machine learning is
about explaining your wishes to computers in a different way
from traditional programming.
So think about it like this.
Traditional programming-- to get a computer
to do something for you, you have
to give explicit instructions to the computer.
Machine learning is asking for what you want with examples
instead of instructions.
The biggest challenge that I see marketers face with machine
learning is how to get started.
There's the research side of machine learning.
And then there's the applied side.
And the problem area for a lot of businesses
is that they don't know which one they want to be in.
The research side is all about building machine
learning algorithms for other people to use.
That's a lot like building kitchen appliances,
whereas applied machine learning is
all about cooking and cooking at scale.
And that is a completely different art.
Just because you know how to build the microwave,
doesn't make you a great chef and vice versa.
And so what they end up doing-- and this
is the big mistake of why businesses and marketers will
fail-- is they think they need to build their own microwave
in order to cook.
They don't realize that to apply machine learning
to a specific business problem, you
don't need to reinvent the wheel and build that algorithm
There are warehouses upon warehouses of these algorithms
created by research teams.
And once it's created, everyone else can take it off the shelf
and start applying it to innovating in their models.
The key to success here is rigorous testing,
making sure that what you asked for has been delivered.
Keep testing the model on new data to see if it's working.
And bear in mind, there's only so much
that the hottest algorithm can help you
with if your data isn't very good.
Put your effort into making sure that your data,
your ingredients are relevant to the task.
And if you have the most beautiful ingredients,
you can get away with a very simple kitchen.
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