Name: TensorFlow Extended (TFX) & Hub (TensorFlow @ O’Reilly AI Conference, San Francisco '18)
Uploaded: 2020-03-31T07:35:49.000Z
Duration: 41 min 41 s
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I'm a product manager in Google Research.

And today I'm going to talk about TensorFlow Extended,

which is a machine learning platform that we built

And I'd like to start this talk with a block

diagram and the small yellow box, or orange box.

what most people care about and talk about when they

what type of machine learning problem you're solving.

And that's what you talk about when you talk about TensorFlow

However, in addition to the actual machine learning,

around the actual machine learning algorithm

that you have to have in place, and that you actually

to actually do machine learning in a production setting.

So you have to care about where you get your data from,

that your data are clean, how you transform them,

how you train your model, how to validate your model,

how to push it out into a production setting,

I only have my small machine learning problem.

And I don't really have these production worries as of today.

will have that problem at some point in time.

again is that research and experimentation

It's like research and experimentation never

Eventually it will become a production model.

And at that point, you actually have to care about all

all of my machine learning on a local machine, in a notebook.

I don't need all of these heavy tools to get started.

But similarly, small scale today is large scale tomorrow.

At Google we have this problem all the time.

That's why we always design for scale from day one,

because we always have product teams that say, well,

But then a week later the product picks up.

And suddenly they need to distribute the workload

And then they have all of these concerns.

Now, the good news is that we built something for this.

So this is a block diagram that we published

in one of our papers that is a very simplistic view

what I'm going to discuss today, is only a small subset of this.

But building TFX and deploying it at Google

has had a profound impact of how fast product teams at Google

and deploy them in production, and how ubiquitous machine

You'll see later I have a slide to give you some sense of how

And it really has accelerated all of our efforts

to being an AI first company and using machine learning

all of this available to you through open sourcing it.

So the boxes that are just highlighted in blue

are the components that we've already open sourced.

Now, I want to highlight an important thing.

TFX is a real solution for real problems.

Sometimes people ask me, well, is this the same code that you

Or did you just build something on the side and open source it?

And all of these components are the same code base

that we use internally for our production pipelines.

So it's really code that solves real problems for Google.

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TensorFlow Extended (TFX) & Hub (TensorFlow @ O’Reilly AI Conference, San Francisco '18)

specific

extremely

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profound