Name: TensorFlow Extended (TFX) とメタデータ (TensorFlow Meets) (TensorFlow Extended (TFX) and Metadata (TensorFlow Meets))
Uploaded: 2021-01-14T10:34:46.000Z
Duration: 4 min 47 s
Description: VoiceTubeの動画で発音を聞きながら英語表現を覚えよう！学べる英語：

Hi, everybody, and welcome to TensorFlow Meets.

程度の副詞

I'm really excited to have Clemens Mewald with us.

TensorFlow Extended is an end-to-end machine learning platform

that was built at Google for our product teams

to accelerate the path from training a model

and it really contains all of the components you need

of making it available to the community through open source.

Now, I know there's been a few pretty big releases in TensorFlow,

Could you talk us through that a little bit?

So, in the past, we've released components of TFX,

so those were the libraries, along the way.

TensorFlow Data Validation was the most recent one.

so that you can actually configure and run it

Okay, now just taking a step back for a second,

so the whole idea around TensorFlow Extended (TFX)

is really if I want to productize my machine learning.

of the machine learning itself is this little box.

there's all this stuff around outside it.

So lots of great progress has been made in that.

we've released a lot of these components in the past.

And, by the way, these are the same components

that we use at Google for our production workloads.

The thing that I'm most excited about these days

is the horizontal pieces that pull them all together,

and one of the ones that we've launched today

that enable a lot of very exciting features and use cases.

Can we dig a little deeper into the metadata?

What use would it be for me if I'm training production?

so for the user, it should be-- come out of the box.

that people get excited about is experiment tracking.

So, every single time we train a new model

and then, after the fact, you can go back

and look at TensorBoard for these models,

you can compare TensorBoard runs for these models.

And we also keep track of all of the other artifacts

Now, obviously, data is the lifeblood of any machine learning,

training, and building any kind of production system,

so there's lots of great tools for managing data,

and I know one of your talks was around data.

So, specifically, TensorFlow Data Validation

to make sure that the data that we ingest into these systems

and we're aware that it doesn't have any errors in it

or it's not missing any specific features.

And that library has been open source for a while,

we actually built a few components using this library.

You can actually look at the distribution of your data.

The second one infers a schema from those statistics.

And the third one validates your data against the schema,

so it can actually create an anomaly report

to tell you if your data conforms to the schema,

or if there's distributional shift in your data.

to prevent you with training mistakes, right?

Exactly, because you need to catch these things early.

Because if you train a model on bad data,

you will find out late that your model isn't performing well.

So what you really want to do is catch these mistakes early

Yep, and also just prevent burning training time.

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TensorFlow Extended (TFX) とメタデータ (TensorFlow Meets) (TensorFlow Extended (TFX) and Metadata (TensorFlow Meets))

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