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From the course by Google Cloud

How Google does Machine Learning

2870 ratings

Google Cloud

How Google does Machine Learning

2870 ratings

Course 1 of 5 in the Specialization Machine Learning with TensorFlow on Google Cloud Platform

***NEW! Specialization Completion Challenge, receive Qwiklabs credits valued up to $150! See below for details.*** What is machine learning, and what kinds of problems can it solve? Google thinks about machine learning slightly differently -- of being about logic, rather than just data. We talk about why such a framing is useful for data scientists when thinking about building a pipeline of machine learning models. Then, we discuss the five phases of converting a candidate use case to be driven by machine learning, and consider why it is important the phases not be skipped. We end with a recognition of the biases that machine learning can amplify and how to recognize this. SPECIALIZATION COMPLETION CHALLENGE As if learning new skills wasn’t enough of an incentive, we're excited to announce a special completion challenge for 'Machine Learning with TensorFlow on Google Cloud Platform’ specialization. Here’s how it works: Our completion challenge runs through 11:59pm PT May 5, 2019. Complete any course in this Specialization including this one, anytime in this period and we'll send you 30 Qwiklabs credits for each course completed (upto $150 value given there are 5 courses in the specialization). You can use these credits to take additional labs and earn badges, which you can then add to your resume and social profiles. Your challenge awaits – begin learning on Coursera today! >>> By enrolling in this specialization you agree to the Qwiklabs Terms of Service as set out in the FAQ and located at: https://qwiklabs.com/terms_of_service <<<

From the lesson

What it means to be AI first

You will learn what we mean when we say that Google’s company strategy is to be AI-first, and what that means in practice.

Pre-trained models3:18

Meet the Instructors

Google Cloud Training

Artnet built a custom image model on Google Cloud platform using TensorFlow,

there on the left-hand side of this image.

But increasingly, you don't have to do that.

There are a variety of domains where Google

exposes Machine Learning Services train but our own data.

For example, if you want to transcribe speech,

you could use a speech API instead of having to collect audio data,

train it, and predict with it.

There are many such pre-train models.

And such pre-train models are excellent ways to replace user input by machine learning.

Here's an example of using a pre-train model.

Ocado is a world's largest online only grocery.

It's based in the UK.

And the way it used to work,

was a customer sended email.

And traditionally, each email would get

read and then routed to the appropriate E-department,

where it would get read again.

So that doesn't scale.

So, Ocado turned to natural language processing.

And they were able to extract the sentiment of the email and entities,

the things being talked about in the email,

and even the syntax of the email.

And this computational technology helps Ocado parse through the body of emails and

tag and route these e-mails to help

the contact center reps determine the priority and context in a very efficient way.

But increasingly, customers do not want to go to your website and click on a button.

They do not want to send you an email.

They want to talk to you, interactively.

To get their questions and concerns answered.

Manually, answering each call doesn't scale,

and so Gartner estimates that in a few years,

we will be spending more on conversational interfaces that even on mobile apps.

So does this mean using the speech API,

transcribing the speech, and then trying to make sense of it?

No. What I'm showing you here is

a high-level conversational agent tool called Dialogflow.

So look here at this screen and we notice that the agent says,

"How may I help you today?"

And the customer says,

"I want to get a pizza with bacon and cheese."

And based on that,

Dialogflow goes ahead and builds

a JSON message that says that the customer wants to order a pizza,

and is able to go ahead and add the toppings corresponding to bacon and cheese.

And then the agent says,

"Well, what size do you want?"

Because that's another requirement and based on that,

the size as large also gets added and the standard crust and then says, "Oh add olives."

And now, into the toppings, olives gets added.

Notice that is a very conversational interface and from these conversations,

a JSON structured message gets built and it's this JSON message,

this very structured message that goes to

the rest of the application which works the same way as before,

except that the user input has not come from the customer pointing

and clicking their way through a platform but

instead has come through a conversational interface.

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