Name: TensorFlow Extended（TFX）。機器學習管道和模型理解（Google I/O'19) (TensorFlow Extended (TFX): Machine Learning Pipelines and Model Understanding (Google I/O'19))
Uploaded: 2021-01-14T10:37:22.000Z
Duration: 37 min 51 s
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And together with my colleagues Kevin Haas and Tulsee Doshi,

we will talk to you today about TensorFlow Extended,

I think most of you here that have used ML in the real world

realize that machine learning is a lot more than just a model.

It's a lot more than just training a model.

And especially, when machine learning powers your business

or powers a product that actually affects your users,

So, today, we'll talk about how, in the face

use machine learning that are robust to the world

And today's talk will actually have two parts.

The first part will be about TensorFlow Extended, otherwise

This is an end-to-end machine learning platform.

talk about model understanding and how you can actually

get insights into your business by understanding

how your model performs in real-world situations.

So let's get started with the first part, TensorFlow

We started building it approximately two and a half

And a lot of the knowledge that went into building TFX actually

came from experience we had building other machine

learning platforms within Google that preceded it,

So TFX has had a profound impact to Google,

and it's used throughout several Alphabet companies

and also in several products within Google itself.

And several of those products that you can see here--

like Gmail or Add, et cetera, or YouTube--

So they affect billions of users, et cetera.

to pay extra attention to building systems that

make it available to the rest of the world.

been open sourcing aspects of it, and several of our partners

externally have actually been pretty successful

deploying this technology, several of the libraries we

So just to call out an interesting case study

that Twitter made, they actually made a fascinating blog post

where they spoke about how they ranked tweets with TensorFlow

and how they used TensorFlow Hub in order

to do transfer learning and shared word embeddings

and share them within their organization.

And they also showcased how they use TensorFlow Model Analysis

in order to have a better understanding of their model--

how their model performs not just globally

over the population, but several slices of their population

later, especially with the model understanding talk.

Now I think most of you here are either software developers

familiar with software processes and technologies.

So I think most of you probably recognize

several of the themes presented in this slide,

like scalability, extensibility, modularity, et cetera.

But, my conjecture is that most people think

about those concepts in terms of code and how to build software.

Now, with the advent of machine learning,

means that those applications are powered by data--

So if you just think about code and you don't think about data,

you're only thinking about half of the picture,

But if you don't think about the other half,

So I would actually encourage everyone just

how does this concept apply to data as opposed to just code?

And if you can apply these concepts to both data and code,

that is actually robust and powers your products.

So we will actually go into each of those

individually and see how they apply to machine learning.

Most of you know that, when you start your business,

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TensorFlow Extended（TFX）。機器學習管道和模型理解（Google I/O'19) (TensorFlow Extended (TFX): Machine Learning Pipelines and Model Understanding (Google I/O'19))

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