Name: モデル理解とビジネスリアリティ（TensorFlow拡張版 (Model Understanding and Business Reality (TensorFlow Extended))
Uploaded: 2021-01-14T10:03:38.000Z
Duration: 8 min 53 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

This is the final episode of our five-part series

on real-world machine learning and production.

We've covered a lot so far  in episodes 1 through 4,

so if you haven't seen those yet,  I'd really recommend watching them.

In today's episode,  we'll be looking at an example

of how model understanding  is critical for meeting your business goals.

We've talked about how TFX  and TensorFlow model analysis

let you do deep analysis  of your model's performance.

In this example we have an online retailer who is selling shoes.

They're using a model  to predict click-through rates

and using those predictions  to decide how much inventory to order

when suddenly,  they discover that their model's AUC

and prediction accuracy  for a particular part of their product line,

has started getting much worse than it was before.

Now, how much inventory  should they order for men's dress shoes?

the cost could be  a significant part of their business.

That's why doing deep analysis  of your model's performance,

not just once, but on an ongoing basis, is critical for your business.

TFX creates pipelines that enable  that kind of ongoing deep analysis.

Remember that it's not  just overall model performance.

Mispredictions  on different parts of your data

do not have the same uniform cost to your business.

The data that you have is almost never the data that you wish you had,

and your model's objectives,  things like AUC,

are really just proxies  for your actual business objectives,

things like knowing  how much inventory to order.

Finally, the real world  doesn't stand still,

so your data and business conditions are constantly changing.

That's why you need to continue to monitor and analyze

One way to look at this  is to think about a triangle

We found that usually  when there's a problem

with your model's performance for your business,

First, has something about  the realities of our business changed?

Maybe we have a new supplier  or a new product has been released.

Maybe our customers' behavior has changed.

and how well our models perform for our business.

or a service endpoint started getting flaky,

or maybe a software update has broken something,

or maybe the feature set that we've been using

isn't working for the current business conditions,

or maybe the problem  really is with our model.

or create an ensemble  with a rules-based system

When things go wrong,  you need to start investigating

The place to start is always with your data

because if your data isn't right, nothing will be right.

Fortunately, TFX builds tools and processes

for investigating your data right into your pipelines

with the StatisticsGen, SchemaGen  and ExampleValidator components

and the tools provided  by TensorFlow Data Validation.

You should look for outliers  and missing values in your data

and also look for changes in the distributions

For example, seasonality and trend  can affect your data over time,

and if you don't look for it,  you might not be aware of it.

provides visualization tools like this for investigating your data

and making comparisons  between the data you're seeing now

and the data you were seeing last week or last month.

These are really valuable  when you are trying to dive into your data.

You could also look for particular combinations of features,

regions of your loss surface  where your data may be sparse.

Coverage of your feature space  is important for model performance

and it will change over time as your data changes.

you may need to focus  on collecting examples

which might require creating new features or eliminating features

that aren't providing  good predictive information.

This can often be a result  of changes in your business conditions.

or someone bought TV media  that shifted CTRs from one brand to another.

Change is a constant  in business and in life

is to really dig  into your model's performance.

for doing deep analysis  of your model's performance

right into your pipelines  with the Evaluator component

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モデル理解とビジネスリアリティ（TensorFlow拡張版 (Model Understanding and Business Reality (TensorFlow Extended))

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