Name: TFXって具体的に何なの？(TensorFlow Extended) (What exactly is this TFX thing? (TensorFlow Extended))
Uploaded: 2021-01-14T10:38:21.000Z
Duration: 5 min 46 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：

未来進行形

and today I'm going to be talking about TensorFlow Extended,

also known as TFX, and how it helps you put

your amazing machine learning models into production.

This is the first episode in our five-part series

on real world machine learning which will help you

get up to speed on using TFX to create your own

In today's episode, we'll be asking the question,

When we think about ML, we usually only think

about the great models that we can now create.

After all, that's what all the research papers

But when we want to take that amazing model

and make it available to the world, we need to think about

all the things that a production solution requires.

So that's why we have TFX, to build production pipelines

so that we can offer our amazing models to the world.

And there was nothing already available that could meet our needs.

And not just Google but all of the alphabet companies.

In fact, TFX wasn't the first ML pipeline framework

it evolved out of earlier attempts and is now the default framework

for the majority of Google's ML production solutions.

As ML developers putting a model into production,

First, when we start planning for developing an ML application,

we have all the normal ML things to think about.

That includes getting labeled data if we're doing supervised learning,

and making sure that our data set covers well the space

We also want to minimize the dimensionality of our feature set

while maximizing the predictive information it contains.

And make sure that our application won't be unfairly biased.

We also need to consider rare conditions,

especially in applications like healthcare

for conditions that only occur in rare, but important, situations.

And finally, we need to consider that this will be a living solution

that will evolve over time as new data flows in

and as conditions change and plan for life cycle management

But in addition to all that, we need to remember that

we're putting a software application into production.

That means that we still have all the requirements that

including scalability, consistency, modularity

and testability, as well as safety and security.

We're way beyond just training a model now.

And we can't forget about them just because we're doing ML.

and get our amazing new model into production?

We don't pretend to have all the answers.

This is an evolving field within the ML community

If you're interested in a more in-depth discussion

of the challenges of machine learning in production environments,

TFX allows you to create production ML pipelines that include

many of the requirements for production software deployments

It starts with ingesting your data and flows through data validation,

feature engineering, training, evaluating and serving.

In addition to TensorFlow, itself, we've created libraries

for each of the major phases of an ML pipeline,

TensorFlow Data Validation, TensorFlow Transform

TFX implements a series of pipeline components

which in this diagram are in orange, and allows you to create

To tie all this together, we created some horizontal layers

for things like pipeline storage, configuration and orchestration.

for managing and optimizing your pipelines

and the applications that you run on them.

We'll be discussing those more in later episodes.

For now, that should give you an idea of what we're talking about

when we think about implementing a production ML pipeline with TFX.

In our next episode, we'll discuss how TFX pipelines actually work.

For more information on TFX, visit us at tensorflow.org/tfx

and don't forget to comment and like us below

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TFXって具体的に何なの？(TensorFlow Extended) (What exactly is this TFX thing? (TensorFlow Extended))

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