Placeholder Image

字幕表 動画を再生する

  • [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]

[MUSIC PLAYING]

字幕と単語

動画の操作 ここで「動画」の調整と「字幕」の表示を設定することができます

B1 中級

TensorFlowを搭載:Airbnbが機械学習を利用してリスティング写真の分類を支援 (Powered by TensorFlow: Airbnb uses machine learning to help categorize its listing photos)

  • 0 0
    林宜悉 に公開 2021 年 01 月 14 日
動画の中の単語