Powered by TensorFlow: Airbnb uses machine learning to help categorize its listing photos


[MUSIC PLAYING] ANDREW HOH: Airbnb’s
an online marketplace. We have over 5 million
different homes in 81,000 cities, which
equates to hundreds of millions of photos, making it
possibly the largest collection of images of
homes in the world today. When a guest decides
to select a home, one of the biggest
influences in their decision is a diverse set of images. But a lot of times,
hosts will take a lot of pictures
of a single room and forget to take pictures
of the other rooms. SHIJING YAO: They also have
the option to add captions, but a lot of the cases,
they’re totally off. ANDREW HOH: No. ALFREDO LUQUE: We were
faced with the challenge of identifying what’s
actually in these pictures and present them
properly on the site. That’s one area where
machine learning excels. The real challenge
was one of scale. We had upwards of half a
billion images to get through. It was going to take months to
really go through all of these. ANDREW HOH: Using
TensorFlow, we were able to speed up the
process and deliver a reasonable model within days. Bighead is Airbnb’s
machine learning platform. We had the idea of making it
very agnostic to different ML frameworks, and so we levered
TensorFlow to train the model. And then Bighead helps with the
model lifecycle, the feature management, and then
TensorFlow Serving to help serve the model results. SHIJING YAO: Before
you’re thinking about which tool to use,
you’re first thinking about which model to use. And we did research on this. We find that ResNet 50 was
one of the state-of-the-art performing models. We used that as the
basic architecture. ALFREDO LUQUE: We used
TensorFlow’s cross APIs and serving and some of the
distributed GPU computations. This ultimately
led to a pipeline that we could deploy to go
through hundreds of millions of images very quickly. ANDREW HOH: So the
end goal is basically using these
classifications of images to make sure that their
first, initial set of photos that they see aren’t just
a picture of the garage and bathroom. But it could be of the
living room that’s gorgeous, and the bedroom, and
the swimming pool. Future applications could be
to detect different objects in homes. And if users decide to
search on the website for specific amenity types,
we can actually bubble that up to the surface. SHIJING YAO: If you like
how Airbnb operates today, the reason was
because of machine learning because machine
learning is almost everywhere in the company. Search ranking, pricing,
predictive booking. ALFREDO LUQUE: We’re passing
into the hundreds of models, so it’s something I
expect to keep growing. ANDREW HOH: And with a lot of
these new frameworks coming out, we can make
better experiences for our guests and better
business decisions, as well. [MUSIC PLAYING]

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