WEBVTT

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[CARLOTTA]: Great, so I think we can start

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since the meeting is recorded, so if

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everyone, uh, jump-jumps in later, they

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can watch the recording.

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So, hi everyone and welcome to this

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um, Cloud Skill Challenge study session

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around a create classification models

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with Azure Machine learning designer.

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So today I'm thrilled to be here with

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John. Uh, John do you mind

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introduce briefly yourself?

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[JOHN]: Uh, thank you Carlotta.

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Hello everyone.

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Welcome to our workshop today. I hope

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that you are all excited for it. I am

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John Aziz, a gold Microsoft Learn student

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ambassador, and I will be here with, uh,

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Carlotta to do the practical part

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about this module of the Cloud Skills

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Challenge. Thank you for having me.

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[CARLOTTA]: Perfect, thanks John.

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So for those who

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don't know me, I'm Carlotta Castelluccio,

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based in Italy and focused on AI

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machine learning technologies and about

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the use in education.

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Um, so,

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um this Cloud Skill Challenge study

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session is based on a learn module, a

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dedicated learn module. I sent to you, uh

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the link to this module, uh, in the chat

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in a way that you can follow along the

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module if you want, or just have a look at

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the module later at your own pace.

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Um...

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So, before starting I would also like to

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remember to remember you, uh, the code of

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conduct and guidelines of our student

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ambassadors community. So please during this

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meeting be respectful and inclusive and

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be friendly, open, and welcoming and

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respectful of other-each other

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differences.

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If you want to learn more about the code

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of conduct, you can use this link in the

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deck: aka.ms/SACoC.

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And now we are,

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um, we are ready to to start our session.

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So as we mentioned it we are going to

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focus on classification models and Azure ML,

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uh, today. So, first of all, we are going

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to, um, identify, uh, the kind of

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um, of scenarios in which you should

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choose to use a classification model.

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We're going to introduce Azure Machine

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Learning and Azure Machine Designer.

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We're going to understand, uh, which are

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the steps to follow, to create a

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classification model in Azure Machine

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Learning, and then John will,

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um,

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lead an amazing demo about training and

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publishing a classification model in

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Azure ML Designer.

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So, let's start from the beginning. Let's

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start from identifying classification

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machine learning scenarios.

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So, first of all, what is classification?

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Classification is a form of machine

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learning that is used to predict which

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category or class an item belongs to. For

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example, we might want to develop a

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classifier able to identify if an

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incoming email should be filtered or not

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according to the style, the sender, the

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length of the email, etc.

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In this case, the

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characteristics of the email are the

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features.

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And the label is a classification of

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either a zero or one, representing a spam

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or non-spam for the incoming email. So

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this is an example of a binary

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classifier. If you want to assign

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multiple categories to the incoming

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email like work letters, love letters,

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complaints, or other categories, in this

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case a binary classifier is no longer

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enough, and we should develop a

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multi-class classifier. So classification

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is an example of what is called

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supervised machine learning

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in which you train a model using data

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that includes both the features and

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known values for label

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so that the model learns to fit the

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feature combinations to the label. Then,

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after training has been completed, you

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can use the train model to predict

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labels for new items for-for which the

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label is unknown.

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But let's see some examples of scenarios

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for classification machine learning

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models. So, we already mentioned an

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example of a solution in which we would

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need a classifier, but let's explore

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other scenarios for classification in

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other industries. For example, you can use

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a classification model for a health

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clinic scenario, and use clinical data to

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predict whether patient will become sick

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or not.

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You can use, um...

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[NO AUDIO]

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[JOHN]: Carlotta, you are muted.

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[CARLOTTA]: Oh, sorry. 
So, when I became muted, it's a

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long time, or?

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[JOHN]: You can use-you can use, uh

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some models for classification.

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For example, you can use...

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You were saying this.

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[CARLOTTA]: Uh, so I was in this deck,

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or the previous one?

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[JOHN]: This one, you have been muted

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for, uh, one second [LAUGHS].

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[CARLOTTA]: Okay, okay perfect, perfect.

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Uh, yeah I was talking...sorry for

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that. So, I was talking about the possible

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scenarios in which you,

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you can use a classification model. Like

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have clinic scenario, financial scenario,

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or the third one is business type of

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scenario. You can use characteristics of

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small business to predict if a new

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venture will succeed or not, for

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example. And these are all types of

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binary classification.

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Uh, but today we are also going to talk

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about Azure Machine Learning. So let's

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see.

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What is Azure Machine Learning? So

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training and deploying an effective

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machine learning model involves a lot of

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work, much of it time-consuming and

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resource intensive. So, Azure Machine

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Learning is a cloud-based service that

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helps simplify some of the tasks it

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takes to prepare data, train a model, and

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also deploy it as a predictive service.

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So it helps that the scientists increase

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their efficiency by automating many of

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the time-consuming tasks associated to

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creating and training a model.

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And it enables them also to use

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cloud-based compute resources that scale

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effectively to handle large volumes of

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data while incurring costs only when

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actually used.

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To use Azure Machine Learning, you,

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first thing's first, you need to create a

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workspace resource in your Azure

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subscription, and you can then use these

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workspace to manage data, compute

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resources, code models and other

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artifacts after you have created an

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Azure Machine Learning workspace,

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you can develop solutions with the

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Azure Machine Learning service,

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either with developer

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tools or the Azure Machine Learning

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studio web portal.

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In particular, 
Azure Machine Learning studio

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is a web portal for machine

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learning solutions in Azure, and it

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includes a wide range of features and

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capabilities that help data scientists

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prepare data, train models, publish

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predictive services, and monitor also

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their usage.

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So to begin using the web portal, you need

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to assign the workspace

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you created in the Azure portal

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to the Azure Machine

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Learning studio.

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At its core, Azure Machine Learning is a

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service for training and managing

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machine learning models for which you

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need compute resources on which to run

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the training process.

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Compute targets are, um, one of the main

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basic concepts of Azure Machine Learning.

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They are cloud-based resources on which

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you can run model training and data

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exploration processes.

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So in Azure Machine Learning studio, you

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can manage the compute targets for your

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data science activities, and there are

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four kinds of of compute targets you can

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create. We have the compute instances,

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which are vital machine set up for

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running machine learning code during

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development, so they are not designed for

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production.

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Then we have compute clusters, which are

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a set of virtual machines that can scale

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up automatically based on traffic.

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We have inference clusters, which are

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similar to compute clusters, but they are

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designed for deployment, so they are

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deployment targets for predictive

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services that use trained models.

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And finally, we have attached compute,

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which are any compute target that you

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manage yourself outside of Azure ML, like,

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for example, virtual machines or Azure

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data bricks clusters.

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So we talked about Azure Machine

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Learning, but we also mentioned-

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mentioned Azure Machine Learning

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designer. What is Azure Machine Learning

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designer? So, in Azure Machine Learning

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Studio, there are several ways to author

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classification machine learning models.

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One way is to use a visual interface, and

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this visual interface is called designer,

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and you can use it to train, test, and

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also deploy machine learning models. And

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the drag-and-drop interface makes use of

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clearly defined inputs and outputs that

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can be shared, reused, and also version

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control.

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And using the designer, you can identify

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the building blocks or components needed

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for your model, place and connect them on

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your canvas, and run a machine learning

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job.

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So,

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each designer project, so each project

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in the designer is known as a pipeline.

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And in the design, we have a left panel

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for navigation and a canvas on your

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right hand side in which you build your

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pipeline visually. So pipelines let you

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organize, manage, and reuse complex

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machine learning workflows across

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projects and users.

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A pipeline starts with the data set from

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which you want to train the model

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because all begins with data when

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talking about data science and machine

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learning. And each time you run a

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pipeline, the configuration of the

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pipeline and its results are stored in

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your workspace as a pipeline job.

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So the second main concept of Azure

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Machine Learning is a component. So, going

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hierarchically from the pipeline, we can

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say that each building block of a

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pipeline is called a component.

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In other words, an Azure Machine

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Learning component encapsulates one step

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in a machine learning pipeline. So, it's a

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reusable piece of code with inputs and

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outputs, something very similar to a

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function in any programming language.

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And in a pipeline project, you can access

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data assets and components from the left

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panels

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Asset Library tab, as you can see

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here in the screenshot in the deck.

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So you can create data assets on using

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an ADOC page called Data Page. And a data

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asset is a reference to a data source

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location.

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So this data source location could be a

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local file, a data store, a web file or

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even an Azure open asset.

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And these data assets will appear along

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with standard sample data set in the

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designers Asset Library.

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Um.

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Another basic concept of Azure ML is

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Azure Machine Learning jobs.

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So, basically, when you submit a pipeline,

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you create a job which will run all the

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steps in your pipeline. So a job executes

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a task against a specified compute

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target.

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Jobs enable systematic tracking for your

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machine learning experimentation in

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Azure ML.

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And once a job is created, Azure ML

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maintains a run record, uh, for the

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job.

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Um, but, let's move to the classification

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steps. So,

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um, let's introduce how to create a

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classification model in Azure ML, but you

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will see it in more details in a

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handsome demo that John will guide

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through in a few minutes.

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So, you can think of the steps to train

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and evaluate a classification machine

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learning model as four main steps. So

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first of all, you need to prepare your

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data. So, you need to identify the

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features and the label in your data set,

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you need to pre-process, so you need to

00:12:46.139 --> 00:12:48.839
clean and transform the data as needed.

00:12:48.839 --> 00:12:51.120
Then, the second step, of course, is

00:12:51.120 --> 00:12:52.740
training the model.

00:12:52.740 --> 00:12:54.600
And for training the model, you need to

00:12:54.600 --> 00:12:57.060
split the data into two groups: a

00:12:57.060 --> 00:12:59.519
training and a validation set.

00:12:59.519 --> 00:13:01.320
Then you train a machine learning model

00:13:01.320 --> 00:13:03.540
using the training data set and you test

00:13:03.540 --> 00:13:05.040
the machine learning model for

00:13:05.040 --> 00:13:06.889
performance using the validation data

00:13:06.889 --> 00:13:08.100
set.

00:13:08.100 --> 00:13:12.180
The third step is performance evaluation,

00:13:12.180 --> 00:13:14.519
which means comparing how close the

00:13:14.519 --> 00:13:16.139
model's predictions are to the known

00:13:16.139 --> 00:13:20.519
labels and these lead us to compute some

00:13:20.519 --> 00:13:23.279
evaluation performance metrics.

00:13:23.279 --> 00:13:25.740
And then finally...

00:13:25.740 --> 00:13:29.051
So, these three steps are not,

00:13:29.051 --> 00:13:32.770
um, not performed every time in a

00:13:32.770 --> 00:13:35.459
linear manner. It's more an iterative

00:13:35.459 --> 00:13:39.420
process. But once you obtain, you achieve

00:13:39.420 --> 00:13:42.959
a performance with which you are

00:13:42.959 --> 00:13:45.779
satisfied, so you are ready to, let's say

00:13:45.779 --> 00:13:48.660
go into production, and you can deploy

00:13:48.660 --> 00:13:51.920
your train model as a predictive service

00:13:51.920 --> 00:13:55.980
into a real-time, uh, to a real-time

00:13:55.980 --> 00:13:58.019
endpoint. And to do so, you need to

00:13:58.019 --> 00:14:00.240
convert the training pipeline into a

00:14:00.240 --> 00:14:02.820
real-time inference pipeline, and then

00:14:02.820 --> 00:14:04.260
you can deploy the model as an

00:14:04.260 --> 00:14:06.779
application on a server or device so

00:14:06.779 --> 00:14:11.420
that others can consume this model.

00:14:11.459 --> 00:14:14.279
So let's start with the first step, which

00:14:14.279 --> 00:14:17.700
is prepare data. Real-world data can contain

00:14:17.700 --> 00:14:19.920
many different issues that can affect

00:14:19.920 --> 00:14:22.320
the utility of the data and our

00:14:22.320 --> 00:14:24.959
interpretation of the results. So also

00:14:24.959 --> 00:14:26.579
the machine learning model that you

00:14:26.579 --> 00:14:29.279
train using this data. For example, real-

00:14:29.279 --> 00:14:31.440
world data can be affected by a bad

00:14:31.440 --> 00:14:34.079
recording or a bad measurement, and it

00:14:34.079 --> 00:14:36.480
can also contain missing values for some

00:14:36.480 --> 00:14:38.880
parameters. And Azure Machine Learning

00:14:38.880 --> 00:14:40.860
designer has several pre-built

00:14:40.860 --> 00:14:43.019
components that can be used to prepare

00:14:43.019 --> 00:14:46.079
data for training. These components

00:14:46.079 --> 00:14:48.300
enable you to clean data, normalize

00:14:48.300 --> 00:14:52.940
features, join tables, and more.

00:14:53.000 --> 00:14:57.120
Let's come to training. So, to train a

00:14:57.120 --> 00:14:59.220
classification model you need a data set

00:14:59.220 --> 00:15:02.160
that includes historical features, so the

00:15:02.160 --> 00:15:03.899
characteristics of the entity for which

00:15:03.899 --> 00:15:06.899
one to make a prediction, and known label

00:15:06.899 --> 00:15:09.779
values. The label is the class indicator

00:15:09.779 --> 00:15:11.820
we want to train a model to predict.

00:15:11.820 --> 00:15:13.920
And it's common practice to train a

00:15:13.920 --> 00:15:16.199
model using a subset of the data while

00:15:16.199 --> 00:15:18.300
holding back some data with which to

00:15:18.300 --> 00:15:20.760
test the train model. And this enables

00:15:20.760 --> 00:15:22.440
you to compare the labels that the model

00:15:22.440 --> 00:15:25.380
predicts with the actual known labels in

00:15:25.380 --> 00:15:27.420
the original data set.

00:15:27.420 --> 00:15:29.880
This operation can be performed in the

00:15:29.880 --> 00:15:32.100
designer using the split data component

00:15:32.100 --> 00:15:34.740
as shown by the screenshot here in the...

00:15:34.740 --> 00:15:36.660
in the deck.

00:15:36.660 --> 00:15:39.540
There's also another component that you

00:15:39.540 --> 00:15:40.980
should use, which is the score model

00:15:40.980 --> 00:15:43.139
component to generate the predicted

00:15:43.139 --> 00:15:45.360
class label value using the validation

00:15:45.360 --> 00:15:48.060
data as input. So once you connect all

00:15:48.060 --> 00:15:49.800
these components,

00:15:49.800 --> 00:15:52.440
the component specifying the

00:15:52.440 --> 00:15:54.959
model we are going to use, the split data

00:15:54.959 --> 00:15:57.060
component, the trained model component,

00:15:57.060 --> 00:16:00.300
and the score model component, you want

00:16:00.300 --> 00:16:02.639
to run a new experiment in

00:16:02.639 --> 00:16:05.760
Azure ML, which will use the data set

00:16:05.760 --> 00:16:09.600
on the canvas to train and score a model.

00:16:09.600 --> 00:16:12.000
After training a model, it is important,

00:16:12.000 --> 00:16:14.639
we say, to evaluate its performance, to

00:16:14.639 --> 00:16:17.060
understand how bad-how good sorry

00:16:17.060 --> 00:16:20.760
our model is performing.

00:16:20.760 --> 00:16:22.680
And there are many performance metrics

00:16:22.680 --> 00:16:24.600
and methodologies for evaluating how

00:16:24.600 --> 00:16:27.000
well a model makes predictions. The

00:16:27.000 --> 00:16:29.160
component to use to perform evaluation

00:16:29.160 --> 00:16:32.220
in Azure ML designer is called, as

00:16:32.220 --> 00:16:35.060
intuitive as it is, Evaluate Model.

00:16:35.060 --> 00:16:38.339
Once the job of training and evaluation

00:16:38.339 --> 00:16:40.740
of the model is completed, you can review

00:16:40.740 --> 00:16:42.959
evaluation metrics on the completed job

00:16:42.959 --> 00:16:45.860
page by right clicking on the component.

00:16:45.860 --> 00:16:48.480
In the evaluation results, you can also

00:16:48.480 --> 00:16:51.000
find the so-called confusion Matrix that

00:16:51.000 --> 00:16:53.399
you can see here in the right side of

00:16:53.399 --> 00:16:55.079
this deck

00:16:55.079 --> 00:16:57.420
A confusion matrix shows cases where

00:16:57.420 --> 00:16:59.220
both the predicted and actual values

00:16:59.220 --> 00:17:01.980
were one, the so-called true positives

00:17:01.980 --> 00:17:04.500
at the top left and also cases where

00:17:04.500 --> 00:17:06.600
both the predicted and the actual values

00:17:06.600 --> 00:17:08.459
were zero, the so-called true negatives

00:17:08.459 --> 00:17:10.919
at the bottom right. While the other

00:17:10.919 --> 00:17:13.679
cells show cases where the predicting

00:17:13.679 --> 00:17:15.380
and actual values differ,

00:17:15.380 --> 00:17:17.939
called false positive and false

00:17:17.939 --> 00:17:19.919
negatives, and this is an example of a

00:17:19.919 --> 00:17:23.579
confusion matrix for a binary classifier.

00:17:23.579 --> 00:17:25.559
While for a multi-class classification

00:17:25.559 --> 00:17:28.079
model the same approach is used to

00:17:28.079 --> 00:17:30.120
tabulate each possible combination of

00:17:30.120 --> 00:17:32.940
actual and predictive value counts. So

00:17:32.940 --> 00:17:34.740
for example, a model with three possible

00:17:34.740 --> 00:17:37.559
classes would result in three times

00:17:37.559 --> 00:17:39.120
three matrix.

00:17:39.120 --> 00:17:41.880
The confusion matrix is also useful for

00:17:41.880 --> 00:17:43.860
the matrix that can be derived from it,

00:17:43.860 --> 00:17:48.260
like accuracy, recall, or precision.

00:17:49.320 --> 00:17:52.080
We say that the last step is

00:17:52.080 --> 00:17:55.620
deploying the train model to a real-time

00:17:55.620 --> 00:17:59.280
endpoint as a predictive service. And in

00:17:59.280 --> 00:18:00.900
order to automate your model into a

00:18:00.900 --> 00:18:02.760
service that makes continuous

00:18:02.760 --> 00:18:04.980
predictions, you need, first of all, to

00:18:04.980 --> 00:18:08.039
create and then deploy an

00:18:08.039 --> 00:18:10.080
inference pipeline. The process of

00:18:10.080 --> 00:18:11.940
converting the training pipeline into a

00:18:11.940 --> 00:18:13.980
real-time inference pipeline removes

00:18:13.980 --> 00:18:16.260
training components and adds web service

00:18:16.260 --> 00:18:18.960
inputs and outputs to handle requests.

00:18:18.960 --> 00:18:21.240
And the inference pipeline performs...they

00:18:21.240 --> 00:18:22.679
seem that the transformation is the

00:18:22.679 --> 00:18:26.160
first pipeline, but for new data. Then it

00:18:26.160 --> 00:18:28.679
uses the train model to infer or predict

00:18:28.679 --> 00:18:32.539
label values based on its feature.

00:18:32.820 --> 00:18:36.120
So, I think I've talked a lot for now

00:18:36.120 --> 00:18:40.380
I would like to let John show us

00:18:40.380 --> 00:18:44.340
something in practice with

00:18:44.340 --> 00:18:47.280
the hands-on demo, so please, John, go

00:18:47.280 --> 00:18:49.860
ahead, share your screen and guide us

00:18:49.860 --> 00:18:52.380
through this demo of creating a

00:18:52.380 --> 00:18:53.425
classification with

00:18:53.425 --> 00:18:55.860
the Azure Machine Learning designer.

00:18:55.860 --> 00:18:58.509
[JOHN]: Thank you so much Carlotta for

00:18:58.509 --> 00:19:00.690
this interesting explanation of the

00:19:00.690 --> 00:19:03.810
Azure ML designer. And now,

00:19:03.810 --> 00:19:07.500
um, I'm going to start with you in the

00:19:07.500 --> 00:19:10.200
practical demo part, so if you want to

00:19:10.200 --> 00:19:13.320
follow along, go to the link that Carlotta

00:19:13.320 --> 00:19:18.380
sent in the chat so you can do

00:19:18.380 --> 00:19:21.840
the demo or the practical part with me.

00:19:21.840 --> 00:19:25.260
I'm just going to share my screen...

00:19:25.260 --> 00:19:27.140
and...

00:19:27.140 --> 00:19:31.559
...go here. So, uh...

00:19:31.559 --> 00:19:34.320
Where am I right now? I'm inside the

00:19:34.320 --> 00:19:36.960
Microsoft Learn documentation. This is

00:19:36.960 --> 00:19:40.260
the exercise part of this module, and we

00:19:40.260 --> 00:19:43.080
will start by setting two things, which

00:19:43.080 --> 00:19:45.299
are a prequisite for us to work inside

00:19:45.299 --> 00:19:49.919
this module, which are the users group

00:19:49.919 --> 00:19:52.400
and the Azure Machine Learning workspace,

00:19:52.400 --> 00:19:55.620
and something extra which is the compute

00:19:55.620 --> 00:19:59.760
cluster that Carlotta talked about. So I

00:19:59.760 --> 00:20:02.100
just want to make sure that you all have

00:20:02.100 --> 00:20:05.660
a resource group created inside your

00:20:05.660 --> 00:20:08.039
portal inside your Microsoft Azure

00:20:08.039 --> 00:20:11.100
platform. So this is my resource group.

00:20:11.100 --> 00:20:14.640
Inside this is this Resource Group. I

00:20:14.640 --> 00:20:17.299
have created an Azure Machine Learning

00:20:17.299 --> 00:20:21.539
workspace. So I'm just going to access

00:20:21.539 --> 00:20:24.000
the workspace that I have created

00:20:24.000 --> 00:20:27.000
already from this link. I am going to

00:20:27.000 --> 00:20:30.240
open it, which is the studio web URL, and

00:20:30.240 --> 00:20:33.000
I will follow the steps. So what is this?

00:20:33.000 --> 00:20:35.760
This is your machine learning workspace,

00:20:35.760 --> 00:20:38.220
or machine learning studio. You can do a

00:20:38.220 --> 00:20:40.080
lot of things here, but we are going to

00:20:40.080 --> 00:20:42.419
focus mainly on the designer and the

00:20:42.419 --> 00:20:46.080
data and the compute. So another

00:20:46.080 --> 00:20:49.140
prerequisite here, as Carlotta told you,

00:20:49.140 --> 00:20:51.480
we need some resources to power up the

00:20:51.480 --> 00:20:54.299
classification, the processes that

00:20:54.299 --> 00:20:55.140
will happen.

00:20:55.140 --> 00:20:58.080
So, we have created this computing

00:20:58.080 --> 00:20:59.100
cluster,

00:20:59.100 --> 00:21:02.880
and we have set some presets for

00:21:02.880 --> 00:21:04.140
it. So

00:21:04.140 --> 00:21:07.080
where can you find this preset? You go

00:21:07.080 --> 00:21:10.200
here. Under the create compute, you'll

00:21:10.200 --> 00:21:13.220
find everything that you need to do. So

00:21:13.220 --> 00:21:16.740
the size is the Standard DS11 Version 2,

00:21:16.740 --> 00:21:19.799
and it's a CPU not GPU, because we don't

00:21:19.799 --> 00:21:22.500
know the GPU, and we don't need a GPU.

00:21:22.500 --> 00:21:25.799
Uh, it is ready for us to use.

00:21:25.799 --> 00:21:30.900
The next thing which we will look into

00:21:30.900 --> 00:21:33.600
is the designer. How can you access the

00:21:33.600 --> 00:21:35.100
designer?

00:21:35.100 --> 00:21:37.679
You can either click on this icon or

00:21:37.679 --> 00:21:40.020
click on the navigation menu and click

00:21:40.020 --> 00:21:42.299
on the designer for me.

00:21:42.900 --> 00:21:45.780
Now I am inside my designer.

00:21:45.780 --> 00:21:47.640
What we are going to do now is the

00:21:47.640 --> 00:21:50.280
pipeline that Carlotta told you about.

00:21:50.280 --> 00:21:54.360
And from where can I know these steps? If

00:21:54.360 --> 00:21:57.120
you follow along in the learn module, you

00:21:57.120 --> 00:21:58.740
will find everything that I'm doing

00:21:58.740 --> 00:22:02.340
right now in detail, with screenshots

00:22:02.340 --> 00:22:05.820
of course. So I'm going to create a new

00:22:05.820 --> 00:22:09.120
pipeline, and I can do so by clicking on

00:22:09.120 --> 00:22:10.980
this plus button.

00:22:10.980 --> 00:22:13.740
It's going to redirect me to the

00:22:13.740 --> 00:22:17.100
designer authoring the pipeline, uh, where

00:22:17.100 --> 00:22:19.500
I can drag and drop data and components

00:22:19.500 --> 00:22:21.780
that Carlotta told you the difference

00:22:21.780 --> 00:22:22.980
between.

00:22:22.980 --> 00:22:26.340
And here I am going to do some changes

00:22:26.340 --> 00:22:29.100
to the settings. I am going to connect

00:22:29.100 --> 00:22:31.860
this with my compute cluster that I

00:22:31.860 --> 00:22:35.120
created previously so I can utilize it.

00:22:35.120 --> 00:22:38.100
From here I'm going to choose this

00:22:38.100 --> 00:22:40.380
compute cluster demo that I have showed

00:22:40.380 --> 00:22:42.600
you before in the clusters here,

00:22:42.600 --> 00:22:45.900
and I am going to change the name to

00:22:45.900 --> 00:22:47.820
something more meaningful. Instead of

00:22:47.820 --> 00:22:50.580
byline and the date of today I'm going

00:22:50.580 --> 00:22:53.760
to name it Diabetes...

00:22:53.760 --> 00:22:56.120
uh...

00:22:56.120 --> 00:23:00.020
let's just check this training.

00:23:00.020 --> 00:23:05.100
Let's say Training 0.1 or 01, okay?

00:23:05.100 --> 00:23:09.360
And I am going to close this tab in

00:23:09.360 --> 00:23:12.000
order to have a bigger place to work

00:23:12.000 --> 00:23:14.700
inside because this is where we will

00:23:14.700 --> 00:23:17.220
work, where everything will happen. So I

00:23:17.220 --> 00:23:19.559
will click on close from here,

00:23:19.559 --> 00:23:23.460
and I will go to the data and I will

00:23:23.460 --> 00:23:25.620
create a new data set.

00:23:25.620 --> 00:23:27.900
How can I create a new data set? There is

00:23:27.900 --> 00:23:29.880
multiple options here you can find, from

00:23:29.880 --> 00:23:31.799
local files, from data store, from web

00:23:31.799 --> 00:23:34.020
files, from open data set, but I'm going

00:23:34.020 --> 00:23:36.539
to choose from web files, as this is the

00:23:36.539 --> 00:23:40.280
way we're going to create our data.

00:23:40.280 --> 00:23:43.380
From here, the information of my data set

00:23:43.380 --> 00:23:47.340
I'm going to get them from the Microsoft

00:23:47.340 --> 00:23:50.820
Learn module. So if we go to the step

00:23:50.820 --> 00:23:52.860
that says "Create a dataset",

00:23:52.860 --> 00:23:55.020
under it, it illustrates that you can

00:23:55.020 --> 00:23:57.720
access the data from inside the asset

00:23:57.720 --> 00:23:59.760
library, and inside your asset library,

00:23:59.760 --> 00:24:01.679
you'll find the data and find the

00:24:01.679 --> 00:24:05.539
component. And I'm going to select

00:24:05.539 --> 00:24:09.000
this link because this is where my data

00:24:09.000 --> 00:24:12.000
is stored. If you open this link, you will

00:24:12.000 --> 00:24:14.820
find this is a CSV file, I think.

00:24:14.820 --> 00:24:17.400
Yeah. And you can...like, all the data are

00:24:17.400 --> 00:24:18.360
here.

00:24:18.360 --> 00:24:21.079
Now let's get back..

00:24:21.079 --> 00:24:22.149
Um...

00:24:26.880 --> 00:24:28.200
And you are going to do something

00:24:28.200 --> 00:24:29.880
meaningful, but because I have already

00:24:29.880 --> 00:24:31.820
created it before twice, so I'm gonna

00:24:31.820 --> 00:24:34.980
add a number to the name

00:24:34.980 --> 00:24:37.559
The data set is tabular and there is

00:24:37.559 --> 00:24:39.360
the file, but this is a table, so we're

00:24:39.360 --> 00:24:40.760
going to choose the table.

00:24:40.760 --> 00:24:42.240
Data type

00:24:42.240 --> 00:24:43.740
for data set type.

00:24:43.740 --> 00:24:46.260
Now we will click on "Next". That's gonna

00:24:46.260 --> 00:24:51.179
review, or display for you the content

00:24:51.179 --> 00:24:54.020
of this file that you have

00:24:54.020 --> 00:24:57.419
imported to this workspace.

00:24:57.419 --> 00:25:01.559
And for these settings, these are

00:25:01.559 --> 00:25:03.720
related to our file format.

00:25:03.720 --> 00:25:08.280
So this is a delimited file, and it's not

00:25:08.280 --> 00:25:11.400
plain text, it's not a Jason. The delimiter

00:25:11.400 --> 00:25:14.159
is common, as we have seen that they

00:25:14.159 --> 00:25:26.700
[INDISTINGUISHABLE]

00:25:26.700 --> 00:25:29.039
So I'm choosing common

00:25:29.039 --> 00:25:32.900
errors because the only the first five...

00:25:32.900 --> 00:25:34.880
[INDISTINGUISHABLE]

00:25:34.880 --> 00:25:38.159
...for example. Okay, uh, if you have any

00:25:38.159 --> 00:25:39.960
doubts, if you have any problems, please

00:25:39.960 --> 00:25:42.960
don't hesitate to write me

00:25:42.960 --> 00:25:45.020
in the chat,

00:25:45.020 --> 00:25:48.480
like, what is blocking you, and

00:25:48.480 --> 00:25:50.940
me and Carlotta will try to help you,

00:25:50.940 --> 00:25:53.220
like whenever possible.

00:25:53.220 --> 00:25:55.659
And now this is the new preview for my

00:25:55.659 --> 00:25:57.840
data set. I can see that I have an ID, I

00:25:57.840 --> 00:25:59.700
have patient ID, I have pregnancies, I

00:25:59.700 --> 00:26:02.220
have the age of the people,

00:26:02.220 --> 00:26:05.720
I have the body mass, I think

00:26:05.720 --> 00:26:08.460
whether they have diabetes or not, as a

00:26:08.460 --> 00:26:10.679
zero and one. Zero indicates a negative,

00:26:10.679 --> 00:26:14.159
the person doesn't have diabetes, and one

00:26:14.159 --> 00:26:16.080
indicates a positive, that this person

00:26:16.080 --> 00:26:18.299
has diabetes. Okay.

00:26:18.299 --> 00:26:20.520
Now I'm going to click on "Next". Here I am

00:26:20.520 --> 00:26:23.400
defining my schema. All the data types

00:26:23.400 --> 00:26:25.380
inside my columns, the column names, which

00:26:25.380 --> 00:26:28.760
columns to include, which to exclude. And

00:26:28.760 --> 00:26:31.500
here we will include everything except

00:26:31.500 --> 00:26:35.580
the path of the bath color. And we are

00:26:35.580 --> 00:26:37.860
going to review the data types of each

00:26:37.860 --> 00:26:40.440
column. So let's review this first one.

00:26:40.440 --> 00:26:43.320
This is numbers, numbers, numbers, then it's the

00:26:43.320 --> 00:26:45.779
integer. And this is,

00:26:45.779 --> 00:26:48.679
um, like decimal..

00:26:48.679 --> 00:26:50.900
...dotted...

00:26:50.900 --> 00:26:53.580
decimal number. So we are going to choose

00:26:53.580 --> 00:26:55.020
this data type.

00:26:55.020 --> 00:26:57.200
And for this one

00:26:57.200 --> 00:27:01.200
it says diabetic, and it's a zero under

00:27:01.200 --> 00:27:02.460
one, and we are going to make it as

00:27:02.460 --> 00:27:04.460
integers.

00:27:04.460 --> 00:27:07.980
Now we are going to click on "Next" and

00:27:07.980 --> 00:27:09.780
move to reviewing everything. This is

00:27:09.780 --> 00:27:11.569
everything that we have defined together.

00:27:11.569 --> 00:27:13.500
I will click on "Create".

00:27:13.500 --> 00:27:15.179
And...

00:27:15.179 --> 00:27:17.940
now the first step has ended. We have

00:27:17.940 --> 00:27:19.919
gotten our data ready.

00:27:19.919 --> 00:27:22.440
Now...what now? We're going to utilize the

00:27:22.440 --> 00:27:23.468
designer...

00:27:23.468 --> 00:27:26.820
um...power. We're going to drag and drop

00:27:26.820 --> 00:27:29.820
our data set to create the pipeline.

00:27:29.820 --> 00:27:33.179
So I have clicked on it and dragged it

00:27:33.179 --> 00:27:35.640
to this space. It's gonna appear to you.

00:27:35.640 --> 00:27:39.659
And we can inspect it by right clicking and

00:27:39.659 --> 00:27:42.179
choose "Preview data"

00:27:42.179 --> 00:27:46.200
to see what we have created together.

00:27:46.200 --> 00:27:48.900
From here, you can see everything that we

00:27:48.900 --> 00:27:50.700
have seen previously, but in more

00:27:50.700 --> 00:27:53.100
details. And we are just going to close

00:27:53.100 --> 00:27:56.580
this. Now what? Now we are gonna do the

00:27:56.580 --> 00:28:00.799
processing that Carlota mentioned.

00:28:00.799 --> 00:28:03.659
These are some instructions about the

00:28:03.659 --> 00:28:05.460
data, about how you can look at them, how you

00:28:05.460 --> 00:28:07.140
can open them but we are going to move

00:28:07.140 --> 00:28:09.720
to the transformation or the processing.

00:28:09.720 --> 00:28:13.500
So as Carlotta told you, like any data

00:28:13.500 --> 00:28:15.480
for us to work on we have to do some

00:28:15.480 --> 00:28:17.299
processing to it

00:28:17.299 --> 00:28:20.159
to make it easy easier for the model to

00:28:20.159 --> 00:28:23.279
be trained and easier to work with. So, uh,

00:28:23.279 --> 00:28:25.860
we're gonna do the normalization. And

00:28:25.860 --> 00:28:29.159
normalization meaning is, uh,

00:28:29.159 --> 00:28:33.539
to scale our data, either down or up, but

00:28:33.539 --> 00:28:35.400
we're going to scale them down,

00:28:35.400 --> 00:28:38.820
and we are going to decrease, uh,

00:28:38.820 --> 00:28:40.799
relatively decrease

00:28:40.799 --> 00:28:44.640
the values, all the values, to work

00:28:44.640 --> 00:28:48.120
with lower numbers. And if we are working

00:28:48.120 --> 00:28:49.559
with larger numbers, it's going to take

00:28:49.559 --> 00:28:52.500
more time. If we're working with smaller

00:28:52.500 --> 00:28:54.779
numbers, it's going to take less time to

00:28:54.779 --> 00:28:59.159
calculate them, and that's it. So

00:28:59.159 --> 00:29:02.159
where can I find the normalized data? I

00:29:02.159 --> 00:29:04.260
can find it inside my component.

00:29:04.260 --> 00:29:06.720
So I will choose the component and

00:29:06.720 --> 00:29:09.659
search for "Normalized data".

00:29:09.659 --> 00:29:12.360
I will drag and drop it as usual and I

00:29:12.360 --> 00:29:14.820
will connect between these two things

00:29:14.820 --> 00:29:18.360
by clicking on this spot, this, uh,

00:29:18.360 --> 00:29:20.159
circuit, and

00:29:20.159 --> 00:29:23.159
drag and drop onto the next circuit.

00:29:23.159 --> 00:29:24.899
Now we are going to define our

00:29:24.899 --> 00:29:27.419
normalization method.

00:29:27.419 --> 00:29:31.080
So I'm going to double click on the

00:29:31.080 --> 00:29:32.640
normalized data.

00:29:32.640 --> 00:29:34.860
It's going to open the settings for the

00:29:34.860 --> 00:29:36.480
normalization

00:29:36.480 --> 00:29:38.820
as a better transformation method, which is

00:29:38.820 --> 00:29:40.500
a mathematical way

00:29:40.500 --> 00:29:42.299
that is going to scale our data

00:29:42.299 --> 00:29:44.520
according to.

00:29:44.520 --> 00:29:47.760
We're going to choose min-max, and for

00:29:47.760 --> 00:29:51.539
this one, we are going to choose "Use Zero",

00:29:51.539 --> 00:29:53.100
for constant column we are going to

00:29:53.100 --> 00:29:54.480
choose "True",

00:29:54.480 --> 00:29:56.880
and we are going to define which columns

00:29:56.880 --> 00:29:58.860
to normalize. So we are not going to

00:29:58.860 --> 00:30:01.080
normalize the whole data set. We are

00:30:01.080 --> 00:30:02.760
going to choose a subset from the data

00:30:02.760 --> 00:30:04.559
set to normalize. So we're going to

00:30:04.559 --> 00:30:07.020
choose everything except for the patient

00:30:07.020 --> 00:30:09.000
ID and the diabetic, because the patient

00:30:09.000 --> 00:30:10.919
ID is a number, but it's a categorical

00:30:10.919 --> 00:30:13.740
data. It describes a patient, it's not a

00:30:13.740 --> 00:30:17.460
number that I can sum. I can't say "patient

00:30:17.460 --> 00:30:20.159
ID number one plus patient ID number two".

00:30:20.159 --> 00:30:21.720
No, this is a patient and another

00:30:21.720 --> 00:30:23.399
patient, it's not a number that I can do

00:30:23.399 --> 00:30:25.740
mathematical operations on, so I'm not

00:30:25.740 --> 00:30:28.200
going to choose it. So we will choose

00:30:28.200 --> 00:30:30.539
everything as I said, except for the

00:30:30.539 --> 00:30:33.480
diabetic and the patient ID. I will

00:30:33.480 --> 00:30:34.860
click on "Save".

00:30:34.860 --> 00:30:37.740
And it's not showing me a warning again,

00:30:37.740 --> 00:30:39.480
everything is good.

00:30:39.480 --> 00:30:41.880
Now I can click on "Submit"

00:30:41.880 --> 00:30:46.799
and review my normalization output.

00:30:46.799 --> 00:30:48.240
Um.

00:30:48.240 --> 00:30:51.659
So, if you click on "Submit" here,

00:30:51.659 --> 00:30:54.659
you will choose "Create new" and

00:30:54.659 --> 00:30:56.460
set the name that is mentioned here

00:30:56.460 --> 00:30:59.899
inside the notebook. So it tells you

00:30:59.899 --> 00:31:03.419
to create a job and name it, name

00:31:03.419 --> 00:31:05.460
the experiment "MS Learn Diabetes

00:31:05.460 --> 00:31:06.720
Training", because you will continue

00:31:06.720 --> 00:31:10.160
working on and building component later.

00:31:10.160 --> 00:31:13.020
I have it already created, I am the, uh,

00:31:13.020 --> 00:31:16.919
we can review it together. So let

00:31:16.919 --> 00:31:19.860
me just open this in another tab. I think

00:31:19.860 --> 00:31:21.000
I have it...

00:31:21.000 --> 00:31:23.659
here.

00:31:25.679 --> 00:31:28.220
Okay.

00:31:30.720 --> 00:31:34.740
So, these are all the jobs that I have

00:31:34.740 --> 00:31:37.340
created.

00:31:37.860 --> 00:31:40.119
All the jobs there. Let's do this over.

00:31:40.119 --> 00:31:42.059
These are all the jobs that I have

00:31:42.059 --> 00:31:43.679
submitted previously.

00:31:43.679 --> 00:31:45.840
And I think this one is the

00:31:45.840 --> 00:31:48.360
normalization job, so let's see the

00:31:48.360 --> 00:31:50.100
output of it.

00:31:50.100 --> 00:31:54.120
As you can see, it says, uh, "Check mark", yes,

00:31:54.120 --> 00:31:56.640
which means that it worked, and we can

00:31:56.640 --> 00:31:59.399
preview it. How can I do that? Right click

00:31:59.399 --> 00:32:02.539
on it, choose "Preview data",

00:32:02.539 --> 00:32:06.659
and as you can see all the data are

00:32:06.659 --> 00:32:08.399
scaled down

00:32:08.399 --> 00:32:10.980
so everything is between zero

00:32:10.980 --> 00:32:15.860
and, uh, one I think.

00:32:15.860 --> 00:32:18.899
So everything is good for us. Now we

00:32:18.899 --> 00:32:21.840
can move forward to the next step

00:32:21.840 --> 00:32:26.939
which is to create the whole pipeline.

00:32:26.939 --> 00:32:30.840
So, uh, Carlota told you that

00:32:30.840 --> 00:32:33.179
we're going to use a classification

00:32:33.179 --> 00:32:37.260
model to create this data set, so let

00:32:37.260 --> 00:32:40.620
me just drag and drop everything

00:32:40.620 --> 00:32:43.140
to get runtime and we're doing

00:32:43.140 --> 00:32:46.489
[INDISTINGUISHABLE]

00:32:46.489 --> 00:32:48.469
about everything by

00:32:48.469 --> 00:32:51.419
[INDISTINGUISHABLE]

00:32:51.419 --> 00:32:52.919
So,

00:32:52.919 --> 00:32:55.593
as a result, we are going to explain

00:32:55.593 --> 00:32:59.760
[INDISTINGUISHABLE]

00:32:59.760 --> 00:33:03.600
Yeah. So, I'm going to give this split

00:33:03.600 --> 00:33:06.070
data. I'm going to take the

00:33:06.070 --> 00:33:08.880
transformation data to split data and

00:33:08.880 --> 00:33:10.380
connect it like that.

00:33:10.380 --> 00:33:12.299
I'm going to get three model

00:33:12.299 --> 00:33:15.240
components because I want to train my

00:33:15.240 --> 00:33:16.679
model,

00:33:16.679 --> 00:33:19.740
and I'm going to put it right here.

00:33:19.740 --> 00:33:21.740
Okay.

00:33:21.740 --> 00:33:24.419
Let's just move it down there. Okay.

00:33:24.419 --> 00:33:27.059
And we are going to use a classification

00:33:27.059 --> 00:33:28.620
model,

00:33:28.620 --> 00:33:31.880
a two class

00:33:32.240 --> 00:33:35.399
logistic regression model.

00:33:35.399 --> 00:33:38.159
So I'm going to give this algorithm to

00:33:38.159 --> 00:33:41.480
enable my model to work

00:33:41.820 --> 00:33:45.960
This is the untrained model, this is...

00:33:45.960 --> 00:33:48.059
here.

00:33:48.059 --> 00:33:51.120
The left...

00:33:51.120 --> 00:33:52.860
the left, uh, circuit, I'm going to

00:33:52.860 --> 00:33:54.819
connect it to the data set, and the right

00:33:54.819 --> 00:33:56.940
one, we are going to connect it to

00:33:56.940 --> 00:33:59.700
evaluate model.

00:33:59.700 --> 00:34:02.640
Evaluate model...so let's search for

00:34:02.640 --> 00:34:05.220
"Evaluate model" here.

00:34:05.220 --> 00:34:07.440
So because we want to do what...we want to

00:34:07.440 --> 00:34:10.800
evaluate our model and see how it it has

00:34:10.800 --> 00:34:13.790
been doing. Is it good, is it bad?

00:34:13.790 --> 00:34:18.200
Um, sorry...

00:34:19.980 --> 00:34:22.820
This is...

00:34:23.460 --> 00:34:25.560
this is down there

00:34:25.560 --> 00:34:28.139
after the score model.

00:34:28.139 --> 00:34:31.320
So we have to get the score model first,

00:34:31.320 --> 00:34:33.960
so let's get it.

00:34:33.960 --> 00:34:36.119
And this will take the trained model and

00:34:36.119 --> 00:34:37.260
the data set

00:34:37.260 --> 00:34:39.419
to score our model and see if it's

00:34:39.419 --> 00:34:42.179
performing good or bad.

00:34:42.179 --> 00:34:44.409
And...

00:34:44.409 --> 00:34:47.159
um...

00:34:47.159 --> 00:34:49.080
after that, we have finished

00:34:49.080 --> 00:34:51.920
everything. Now, we are going to do the what?

00:34:52.139 --> 00:34:54.359
The presets for everything.

00:34:54.359 --> 00:34:56.820
As a starter, we will be splitting our

00:34:56.820 --> 00:34:58.920
data. So

00:34:58.920 --> 00:35:01.140
how are we going to do this, according to

00:35:01.140 --> 00:35:03.780
what? To the split rules. So I'm going to

00:35:03.780 --> 00:35:05.940
double-click on it and choose "Split rules".

00:35:05.940 --> 00:35:09.420
And the percentage is

00:35:09.420 --> 00:35:11.780
70 percent for the [INSISTINGUASHABLE]

00:35:11.780 --> 00:35:12.780
and 30 percent of the

00:35:12.780 --> 00:35:14.820
data for

00:35:14.820 --> 00:35:18.420
the valuation or for the scoring, okay?

00:35:18.420 --> 00:35:20.880
I'm going to make it a randomization, so

00:35:20.880 --> 00:35:22.980
I'm going to split data randomly and the

00:35:22.980 --> 00:35:26.060
seat is, uh,

00:35:26.060 --> 00:35:29.339
132, uh 23 I think...yeah.

00:35:29.339 --> 00:35:32.520
And I think that's it.

00:35:32.520 --> 00:35:35.040
The split says why this holds, and that's

00:35:35.040 --> 00:35:36.240
good.

00:35:36.240 --> 00:35:39.540
Now for the next one, which is the train

00:35:39.540 --> 00:35:42.000
model we are going to connect it as

00:35:42.000 --> 00:35:43.500
mentioned here.

00:35:43.500 --> 00:35:48.660
And we have done that and...then why

00:35:48.660 --> 00:35:50.700
am I having here? Let's double click

00:35:50.700 --> 00:35:54.660
on it...yeah. It has...it needs the

00:35:54.660 --> 00:35:57.180
label column that I am trying to predict.

00:35:57.180 --> 00:35:58.680
So from here, I'm going to choose

00:35:58.680 --> 00:36:01.380
diabetic. I'm going to save.

00:36:01.380 --> 00:36:05.180
I'm going to close this one.

00:36:05.520 --> 00:36:07.380
So it says here,

00:36:07.380 --> 00:36:10.619
the diabetic label, the model, it will

00:36:10.619 --> 00:36:12.300
predict the zero and one, because this is

00:36:12.300 --> 00:36:14.700
a binary classification algorithm, so

00:36:14.700 --> 00:36:16.260
it's going to predict either this or

00:36:16.260 --> 00:36:17.520
that.

00:36:17.520 --> 00:36:18.460
And...

00:36:18.460 --> 00:36:20.160
um...

00:36:20.160 --> 00:36:23.880
I think that's everything to run the the

00:36:23.880 --> 00:36:25.859
pipeline.

00:36:25.859 --> 00:36:29.040
So everything is done, everything is good

00:36:29.040 --> 00:36:31.200
for this one. We're just gonna leave it

00:36:31.200 --> 00:36:34.140
for now, because this is the next

00:36:34.140 --> 00:36:35.620
step.

00:36:35.620 --> 00:36:39.839
Um, this will be put instead of the

00:36:39.839 --> 00:36:43.520
score model, but let's...

00:36:44.099 --> 00:36:46.920
let's delete it for now.

00:36:46.920 --> 00:36:49.500
Okay.

00:36:49.500 --> 00:36:52.920
Now we have to submit the job in order

00:36:52.920 --> 00:36:55.680
to see the output of it. So I can click

00:36:55.680 --> 00:36:59.280
on "Submit" and choose the previous job

00:36:59.280 --> 00:37:01.200
which is the one that I have showed you

00:37:01.200 --> 00:37:02.460
before.

00:37:02.460 --> 00:37:05.460
And then let's review its output

00:37:05.460 --> 00:37:06.960
together here.

00:37:06.960 --> 00:37:09.960
So if I go to the jobs,

00:37:09.960 --> 00:37:15.119
if I go to MS Learn, maybe it is training?

00:37:15.119 --> 00:37:18.180
I think it's the one that lasted the

00:37:18.180 --> 00:37:20.640
longest, this one here.

00:37:20.640 --> 00:37:23.700
So here I can see

00:37:23.700 --> 00:37:27.079
the job output, what happened inside

00:37:27.079 --> 00:37:30.420
the model, as you can see.

00:37:30.420 --> 00:37:33.839
So the normalization we have seen

00:37:33.839 --> 00:37:36.540
before, the split data, I can preview it.

00:37:36.540 --> 00:37:39.359
The result one or the result two as it

00:37:39.359 --> 00:37:41.760
splits the data to 70 here and

00:37:41.760 --> 00:37:43.639
thirty percent here.

00:37:43.639 --> 00:37:46.859
Um, I can see the score model, which is

00:37:46.859 --> 00:37:49.140
something that we need

00:37:49.140 --> 00:37:51.530
to review.

00:37:51.530 --> 00:37:56.820
Inside the scroll model, uh, from

00:37:56.820 --> 00:37:57.960
here,

00:37:57.960 --> 00:38:00.960
we can see that...

00:38:00.960 --> 00:38:04.460
let's get back here.

00:38:05.940 --> 00:38:08.220
This is the data that the model has

00:38:08.220 --> 00:38:11.579
been scored and this is a scoring output.

00:38:11.579 --> 00:38:15.300
So it says "code label true", and he is

00:38:15.300 --> 00:38:17.370
not diabetic, so this is,

00:38:17.370 --> 00:38:19.200
um,

00:38:19.200 --> 00:38:21.839
a wrong prediction, let's say.

00:38:21.839 --> 00:38:23.880
For this one it's true and true, and this

00:38:23.880 --> 00:38:26.880
is a good, like, what do you say,

00:38:26.880 --> 00:38:29.460
prediction, and the probabilities of this

00:38:29.460 --> 00:38:30.420
score,

00:38:30.420 --> 00:38:33.119
which means the certainty of our model

00:38:33.119 --> 00:38:36.620
of that this is really true. It's 80 percent.

00:38:36.620 --> 00:38:38.780
For this one it's 75 percent.

00:38:38.780 --> 00:38:42.599
So these are some cool metrics that we

00:38:42.599 --> 00:38:45.359
can review to understand how our model

00:38:45.359 --> 00:38:47.580
is performing. It's performing good for

00:38:47.580 --> 00:38:48.540
now.

00:38:48.540 --> 00:38:53.180
Let's check our evaluation model.

00:38:53.180 --> 00:38:56.700
So this is the extra one that I told you

00:38:56.700 --> 00:38:59.579
about. Instead of the

00:38:59.579 --> 00:39:01.800
score model only, we are going to add

00:39:01.800 --> 00:39:04.260
what evaluate model

00:39:04.260 --> 00:39:06.900
after it. So here

00:39:06.900 --> 00:39:09.420
we're going to go to our Asset Library

00:39:09.420 --> 00:39:12.180
and we are going to choose the evaluate

00:39:12.180 --> 00:39:14.940
model,

00:39:14.940 --> 00:39:17.760
and we are going to put it here, and we

00:39:17.760 --> 00:39:20.220
are going to connect it, and we are going

00:39:20.220 --> 00:39:23.099
to submit the job using the same name of

00:39:23.099 --> 00:39:25.140
the job that we used previously.

00:39:25.140 --> 00:39:29.520
Let's review it. Also, so, after it

00:39:29.520 --> 00:39:33.300
finishes, you will find it here. So I have

00:39:33.300 --> 00:39:35.280
already done it before, this is how I'm

00:39:35.280 --> 00:39:37.380
able to see the output.

00:39:37.380 --> 00:39:40.320
So let's see

00:39:40.320 --> 00:39:43.280
what is the output of this

00:39:43.280 --> 00:39:45.660
evaluation process.

00:39:45.660 --> 00:39:49.800
Here it mentioned to you that there are

00:39:49.800 --> 00:39:51.480
some matrix,

00:39:51.480 --> 00:39:54.839
like the confusion matrix, which Carlotta

00:39:54.839 --> 00:39:57.060
told you about, there is the accuracy, the

00:39:57.060 --> 00:39:59.760
precision, the recall, and F1 Score.

00:39:59.760 --> 00:40:02.339
Every matrix gives us some insight about

00:40:02.339 --> 00:40:04.920
our model. It helps us to understand it

00:40:04.920 --> 00:40:08.579
more, and, um,

00:40:08.579 --> 00:40:10.560
understand if it's overfitting, if

00:40:10.560 --> 00:40:12.240
it's good, if it's bad, and really really,

00:40:12.240 --> 00:40:16.339
like, understand how it's working.

00:40:17.060 --> 00:40:20.400
Now I'm just waiting for the job to load.

00:40:20.400 --> 00:40:22.710
Until it loads,

00:40:22.710 --> 00:40:23.640
um,

00:40:23.640 --> 00:40:26.040
we can continue

00:40:26.040 --> 00:40:28.740
to work on our

00:40:28.740 --> 00:40:31.800
model. So I will go to my designer. I'm

00:40:31.800 --> 00:40:34.740
just going to confirm this.

00:40:34.740 --> 00:40:38.280
And I'm going to continue working on it

00:40:38.280 --> 00:40:39.780
from

00:40:39.780 --> 00:40:42.119
where we have stopped. Where have we

00:40:42.119 --> 00:40:43.560
stopped?

00:40:43.560 --> 00:40:46.440
we have stopped on the evaluate model. So

00:40:46.440 --> 00:40:48.960
I'm going to choose this one.

00:40:48.960 --> 00:40:53.420
And it says here

00:40:54.180 --> 00:40:56.940
"select experiment", "create inference

00:40:56.940 --> 00:40:58.200
pipeline", so

00:40:58.200 --> 00:41:01.079
I am going to go to the jobs,

00:41:01.079 --> 00:41:04.680
I'm going to select my experiment.

00:41:04.680 --> 00:41:06.660
I hope this works.

00:41:06.660 --> 00:41:09.720
Okay. Finally, now we have our

00:41:09.720 --> 00:41:12.180
evaluate model output.

00:41:12.180 --> 00:41:15.480
Let's preview evaluation results

00:41:15.480 --> 00:41:18.660
and, uh...

00:41:18.660 --> 00:41:22.220
come on.

00:41:25.500 --> 00:41:28.020
Finally. Now we can create our inference

00:41:28.020 --> 00:41:31.020
pipeline. So,

00:41:31.020 --> 00:41:33.510
I think it says that...

00:41:33.510 --> 00:41:35.280
um...

00:41:35.280 --> 00:41:38.160
select the experiment, then select MS

00:41:38.160 --> 00:41:39.359
Learn. So,

00:41:39.359 --> 00:41:43.320
I am just going to select it,

00:41:43.320 --> 00:41:48.300
and finally. Now we can, the ROC curve, we

00:41:48.300 --> 00:41:51.000
can see it, that the true positive rate

00:41:51.000 --> 00:41:53.760
and the force was integrate. The false

00:41:53.760 --> 00:41:56.660
positive rate is increasing with time,

00:41:56.660 --> 00:42:01.020
and also the true positive rate. True

00:42:01.020 --> 00:42:03.540
positive is something that it predicted,

00:42:03.540 --> 00:42:06.960
that it is, uh, positive it has diabetes,

00:42:06.960 --> 00:42:09.480
and it's really...it's really true.

00:42:09.480 --> 00:42:12.599
The person really has diabetes. Okay. And

00:42:12.599 --> 00:42:14.760
for the false positive, it predicted that

00:42:14.760 --> 00:42:17.579
someone has diabetes and someone doesn't

00:42:17.579 --> 00:42:20.960
have it. This is what true position and

00:42:20.960 --> 00:42:24.960
false positive means. This is the record

00:42:24.960 --> 00:42:28.020
curve, so we can review the metrics

00:42:28.020 --> 00:42:32.160
of our model. This is the lift curve. I

00:42:32.160 --> 00:42:36.000
can change the threshold of my confusion

00:42:36.000 --> 00:42:37.740
matrix here

00:42:37.740 --> 00:42:39.119
and if Carlotta wants to add

00:42:39.119 --> 00:42:43.920
anything about the...the graphs,

00:42:43.920 --> 00:42:47.000
you can do so.

00:42:50.440 --> 00:42:52.558
[CARLOTTA]: Um, yeah, so I just

00:42:52.558 --> 00:42:54.540
wanted to...if you go...yeah.

00:42:54.540 --> 00:42:57.119
I just wanted to comment for the

00:42:57.119 --> 00:43:00.480
RSC curve, that actually from this

00:43:00.480 --> 00:43:03.900
graph, the metric which usually we're

00:43:03.900 --> 00:43:06.960
going to compute is the area under

00:43:06.960 --> 00:43:09.900
under the curve. And this coefficient or

00:43:09.900 --> 00:43:12.240
metric,

00:43:12.240 --> 00:43:15.060
it's a coefficient—

00:43:15.060 --> 00:43:18.420
it's a value that could span from

00:43:18.420 --> 00:43:23.480
zero to one and the the highest is...

00:43:23.480 --> 00:43:25.970
...the highest is the the score.

00:43:25.970 --> 00:43:29.220
So the closest one,

00:43:29.220 --> 00:43:32.760
so the the highest is the amount of

00:43:32.760 --> 00:43:35.280
area under this curve.

00:43:35.280 --> 00:43:40.500
The highest performance

00:43:40.500 --> 00:43:42.886
we've got from from our model.

00:43:42.886 --> 00:43:46.440
And another thing is what John is

00:43:46.440 --> 00:43:49.680
playing with. So this threshold for

00:43:49.680 --> 00:43:51.380
the logistic

00:43:51.380 --> 00:43:55.610
regression is the threshold used by the

00:43:55.610 --> 00:43:59.520
model to, um,

00:43:59.520 --> 00:44:02.880
to predict if the category is zero or

00:44:02.880 --> 00:44:05.220
one. So if the probability—the

00:44:05.220 --> 00:44:08.599
probability score is above the threshold,

00:44:08.599 --> 00:44:11.579
then the category will be predicted as

00:44:11.579 --> 00:44:15.359
one, while if the probability is

00:44:15.359 --> 00:44:17.460
below the threshold, in this case, for

00:44:17.460 --> 00:44:21.300
example, 0.5, the category is predicted

00:44:21.300 --> 00:44:23.579
as zero. So that's why it's very

00:44:23.579 --> 00:44:26.473
important to choose the threshold,

00:44:26.473 --> 00:44:28.699
because the performance really can vary,

00:44:28.699 --> 00:44:30.560
um,

00:44:30.560 --> 00:44:34.380
with this threshold value.

00:44:34.380 --> 00:44:41.099
[JOHN]: Thank you so much, Carlotta, and

00:44:41.400 --> 00:44:44.400
as I mentioned now, we are going to

00:44:44.400 --> 00:44:46.560
create our inference pipeline. So we are

00:44:46.560 --> 00:44:48.540
going to select the latest one, which I

00:44:48.540 --> 00:44:50.819
already have it opened here. This is the

00:44:50.819 --> 00:44:52.859
one that we were reviewing together. This

00:44:52.859 --> 00:44:55.500
is where we have stopped, and we're going

00:44:55.500 --> 00:44:57.599
to create an inference pipeline. We are

00:44:57.599 --> 00:44:59.520
going to choose a real-time inference

00:44:59.520 --> 00:45:02.520
pipeline, okay?

00:45:02.520 --> 00:45:05.080
From where I can find this? Here, as it

00:45:05.080 --> 00:45:08.099
says, "Real-time inference pipeline".

00:45:08.099 --> 00:45:10.680
So it's gonna add some things to my

00:45:10.680 --> 00:45:12.240
workspace. It's going to add the

00:45:12.240 --> 00:45:13.713
web service input, it's gonna

00:45:13.713 --> 00:45:15.071
have the web service output,

00:45:15.071 --> 00:45:16.490
because we will be creating

00:45:16.490 --> 00:45:18.180
it as a web service to access

00:45:18.180 --> 00:45:19.740
it from the internet.

00:45:19.740 --> 00:45:21.770
What are we going to do? We're going

00:45:21.770 --> 00:45:24.720
to remove this diabetes data, okay?

00:45:24.720 --> 00:45:27.540
And we are going to get a component

00:45:27.540 --> 00:45:29.359
called "Web

00:45:29.359 --> 00:45:33.180
input" and...let me check

00:45:33.180 --> 00:45:35.940
it's "enter data manually".

00:45:35.940 --> 00:45:38.400
We have...we already have that with input

00:45:38.400 --> 00:45:39.540
present.

00:45:39.540 --> 00:45:42.119
So we are going to get the entire data

00:45:42.119 --> 00:45:43.200
manually,

00:45:43.200 --> 00:45:45.420
and we're going to collect it—to connect

00:45:45.420 --> 00:45:49.560
it as it was connected before, like that.

00:45:49.560 --> 00:45:53.040
And also, I am not going to directly take

00:45:53.040 --> 00:45:55.260
the web service—sorry, escort model to

00:45:55.260 --> 00:45:57.839
the web service output like that.

00:45:57.839 --> 00:46:00.240
I'm going to delete this

00:46:00.240 --> 00:46:03.960
and I'm going to execute a python script

00:46:03.960 --> 00:46:05.880
before

00:46:05.880 --> 00:46:09.500
I display my result.

00:46:10.680 --> 00:46:12.060
So,

00:46:12.060 --> 00:46:17.480
this will be connected like...

00:46:19.260 --> 00:46:20.400
So...

00:46:20.400 --> 00:46:23.599
the other way around.

00:46:23.599 --> 00:46:27.660
And from here, I am going to connect this

00:46:27.660 --> 00:46:30.960
with that and there is some data that

00:46:30.960 --> 00:46:33.480
we will be getting from the node, or from

00:46:33.480 --> 00:46:37.680
the explanation here, and this is the

00:46:37.680 --> 00:46:40.740
data that will be entered to our

00:46:40.740 --> 00:46:44.400
website manually. Okay? This is instead of

00:46:44.400 --> 00:46:47.460
the data that we have been getting from

00:46:47.460 --> 00:46:49.740
our data set that we created. So I'm just

00:46:49.740 --> 00:46:51.960
going to double click on it and choose

00:46:51.960 --> 00:46:55.579
CSV, and I will choose "it has headers",

00:46:55.579 --> 00:47:00.839
and I will take or copy this content and

00:47:00.839 --> 00:47:02.819
put it there, okay?

00:47:02.819 --> 00:47:05.700
So let's do it.

00:47:05.700 --> 00:47:07.920
I think I have to click on edit code, now

00:47:07.920 --> 00:47:10.680
I can click on "Save", and I can close it.

00:47:10.680 --> 00:47:13.079
Another thing which is the python script

00:47:13.079 --> 00:47:16.700
that we will be executing.

00:47:17.099 --> 00:47:17.900
Um, yeah. We

00:47:17.900 --> 00:47:19.380
are going to remove this, also.

00:47:19.380 --> 00:47:20.930
We don't need the evaluate model

00:47:20.930 --> 00:47:24.319
anymore, so we are going to remove it.

00:47:24.319 --> 00:47:25.582
The python script

00:47:25.582 --> 00:47:28.579
that I will be executing,

00:47:28.579 --> 00:47:32.599
I can find it here.

00:47:32.699 --> 00:47:35.760
Um, yeah.

00:47:35.760 --> 00:47:38.640
This is the python script that we will

00:47:38.640 --> 00:47:41.520
execute. And it says to you that this

00:47:41.520 --> 00:47:43.619
code selects only the patient's ID

00:47:43.619 --> 00:47:45.000
the score label, the score

00:47:45.000 --> 00:47:47.700
probability and return—returns them to

00:47:47.700 --> 00:47:49.980
the web service output. So we don't want

00:47:49.980 --> 00:47:51.960
to return all the columns, as we have

00:47:51.960 --> 00:47:53.339
seen previously,

00:47:53.339 --> 00:47:55.560
that determines everything,

00:47:55.560 --> 00:47:56.940
so

00:47:56.940 --> 00:47:59.040
we want to return certain stuff, the

00:47:59.040 --> 00:48:02.940
stuff that we will use inside our

00:48:02.940 --> 00:48:05.640
endpoint. So I'm just going to select

00:48:05.640 --> 00:48:07.980
everything and delete it, and

00:48:07.980 --> 00:48:11.060
paste the code that I have gotten from

00:48:11.060 --> 00:48:14.280
the, uh,

00:48:14.280 --> 00:48:16.500
the Microsoft Learn docs.

00:48:16.500 --> 00:48:19.079
Now I can click on "Save", and I can close

00:48:19.079 --> 00:48:20.280
this.

00:48:20.280 --> 00:48:21.470
Let me check something,

00:48:21.470 --> 00:48:22.950
I don't think it saved.

00:48:22.950 --> 00:48:24.940
It's saved, but the display is

00:48:24.940 --> 00:48:26.160
wrong, okay.

00:48:26.160 --> 00:48:30.300
And now I think everything is good to go.

00:48:30.300 --> 00:48:32.640
I'm just gonna double-check everything.

00:48:32.640 --> 00:48:36.359
So, uh, yeah. We are gonna change the name

00:48:36.359 --> 00:48:38.640
of this

00:48:38.640 --> 00:48:40.800
pipeline, and we are gonna call it

00:48:40.800 --> 00:48:42.780
"Predict

00:48:42.780 --> 00:48:46.319
diabetes", okay?

00:48:46.319 --> 00:48:50.339
Now let's close it, and

00:48:50.339 --> 00:48:56.269
I think that we are good to go. So,

00:48:56.269 --> 00:48:59.300
um,

00:48:59.720 --> 00:49:04.460
Okay, I think everything is good for us.

00:49:06.210 --> 00:49:08.108
I just want to make sure of something.

00:49:08.108 --> 00:49:09.209
Is the data...

00:49:09.209 --> 00:49:12.420
it's correct, the data is...yeah,

00:49:12.420 --> 00:49:13.560
it's correct.

00:49:13.560 --> 00:49:16.319
Okay, now I can run the pipeline. Let's

00:49:16.319 --> 00:49:17.640
submit.

00:49:17.640 --> 00:49:21.000
Select an "existing" pipeline, and we're

00:49:21.000 --> 00:49:21.870
going to choose

00:49:21.870 --> 00:49:23.529
the "ms-learn-diabetes-training",

00:49:23.529 --> 00:49:24.599
which is the pipeline

00:49:24.599 --> 00:49:27.060
that we have been working on

00:49:27.060 --> 00:49:31.619
from the beginning of this module.

00:49:31.619 --> 00:49:33.839
I don't think that this is going to take

00:49:33.839 --> 00:49:36.060
much time. So we have submitted the job

00:49:36.060 --> 00:49:37.319
and it's running.

00:49:37.319 --> 00:49:40.140
Until the job ends, we are going to set

00:49:40.140 --> 00:49:41.720
everything

00:49:41.720 --> 00:49:45.599
for deploying a service.

00:49:45.599 --> 00:49:49.070
In order to deploy a service,

00:49:49.070 --> 00:49:50.520
um,

00:49:50.520 --> 00:49:54.000
I have to have the job ready, so

00:49:54.000 --> 00:49:55.980
until it's ready, you can't deploy it. So

00:49:55.980 --> 00:49:58.319
let's go to the job—the job details from

00:49:58.319 --> 00:50:01.319
here, okay?

00:50:01.319 --> 00:50:05.119
And until it finishes,

00:50:05.119 --> 00:50:07.260
Carlotta, do you think that we can have

00:50:07.260 --> 00:50:09.240
the questions, and then we can get back

00:50:09.240 --> 00:50:12.859
to the job I'm deploying it?

00:50:13.700 --> 00:50:15.119
[CARLOTTA]: Yeah, yeah, yeah.

00:50:15.119 --> 00:50:17.279
So yeah, guys, if you

00:50:17.279 --> 00:50:18.980
have any questions

00:50:18.980 --> 00:50:24.119
on what you just saw here

00:50:24.119 --> 00:50:26.940
or into introductions, feel free. This is

00:50:26.940 --> 00:50:30.300
a good moment, we can...we can discuss

00:50:30.300 --> 00:50:33.900
now, while we wait for this job to

00:50:33.900 --> 00:50:36.260
finish.

00:50:36.260 --> 00:50:38.760
[JOHN]: Uh, and....

00:50:38.760 --> 00:50:40.220
can...

00:50:40.220 --> 00:50:45.000
we have the knowledge check one? Or, like,

00:50:45.000 --> 00:50:46.360
what do you think?

00:50:46.360 --> 00:50:47.956
[CARLOTTA]: Yeah, we can also go

00:50:47.956 --> 00:50:49.680
to the knowledge check.

00:50:49.680 --> 00:50:50.940
Um...

00:50:50.940 --> 00:50:56.339
Yeah, okay. So let me share my screen.

00:50:56.339 --> 00:50:58.980
Yeah, so if you have not any questions

00:50:58.980 --> 00:51:01.619
for us, we can maybe propose some

00:51:01.619 --> 00:51:04.959
questions to you that you can,

00:51:04.959 --> 00:51:06.240
um,

00:51:06.240 --> 00:51:09.450
check our knowledge so far and you

00:51:09.450 --> 00:51:12.900
can maybe answer to these questions

00:51:12.900 --> 00:51:15.420
via chat.

00:51:15.420 --> 00:51:18.300
So we have...do you see my screen, can

00:51:18.300 --> 00:51:19.859
you see my screen?

00:51:19.859 --> 00:51:21.650
[JOHN]: Yes.

00:51:21.650 --> 00:51:24.440
[CARLOTTA]: So, John, I think I will

00:51:24.440 --> 00:51:25.440
read this

00:51:25.440 --> 00:51:29.040
question aloud and ask it to you, okay? So

00:51:29.040 --> 00:51:32.040
are you ready to answer?

00:51:32.040 --> 00:51:33.660
[JOHN:] Yes I am.

00:51:33.660 --> 00:51:35.460
[CARLOTTA]: So...

00:51:35.460 --> 00:51:37.260
you're using Azure Machine Learning

00:51:37.260 --> 00:51:39.780
designer to create a training pipeline

00:51:39.780 --> 00:51:42.540
for a binary classification model, so

00:51:42.540 --> 00:51:45.300
what we were doing in our demo,

00:51:45.300 --> 00:51:48.059
right? And you have added a data set

00:51:48.059 --> 00:51:51.660
containing features and labels, a Two-

00:51:51.660 --> 00:51:54.359
Class Decision Forest module. So we used

00:51:54.359 --> 00:51:56.819
a logistic regression model our...

00:51:56.819 --> 00:51:57.877
um, in our example.

00:51:57.877 --> 00:51:59.019
Here, we're using a Two-

00:51:59.019 --> 00:52:01.260
Class Decision Forest model.

00:52:01.260 --> 00:52:04.500
And, of course, a Train Model module. You

00:52:04.500 --> 00:52:07.200
plan now to use score model and evaluate

00:52:07.200 --> 00:52:09.480
model modules to test the train model

00:52:09.480 --> 00:52:11.640
with the subset of the data set that

00:52:11.640 --> 00:52:13.500
wasn't used for training.

00:52:13.500 --> 00:52:15.960
But what are we missing? So what's

00:52:15.960 --> 00:52:18.780
another model you should add? We have

00:52:18.780 --> 00:52:21.660
three options: we have Join Data, we have

00:52:21.660 --> 00:52:25.200
Split Data, or we have Select Columns

00:52:25.200 --> 00:52:26.819
in Dataset.

00:52:26.819 --> 00:52:28.260
So

00:52:28.260 --> 00:52:32.040
while John thinks about the answer,

00:52:32.040 --> 00:52:33.599
go ahead and,

00:52:33.599 --> 00:52:34.800
um,

00:52:34.800 --> 00:52:37.800
answer yourself. So give us your

00:52:37.800 --> 00:52:39.540
guess.

00:52:39.540 --> 00:52:41.940
Put it in the chat, or just come off mute

00:52:41.940 --> 00:52:44.900
and answer.

00:52:46.740 --> 00:52:47.785
"A", "B".

00:52:47.785 --> 00:52:49.769
[JOHN]: Yeah, what do you

00:52:49.769 --> 00:52:50.509
is the correct

00:52:50.509 --> 00:52:53.579
answer for this one? I need something to

00:52:53.579 --> 00:52:56.579
uh...I have to score my model, and I

00:52:56.579 --> 00:53:00.359
have to evaluate it, so I need

00:53:00.359 --> 00:53:03.119
something to enable me to do these two

00:53:03.119 --> 00:53:05.359
things.

00:53:06.579 --> 00:53:08.233
[CARLOTTA]: I think it's something

00:53:08.233 --> 00:53:10.640
you showed us in your pipeline,

00:53:10.640 --> 00:53:13.260
right John?

00:53:13.260 --> 00:53:16.819
[JOHN]: Of course I did.

00:53:23.460 --> 00:53:25.122
[CARLOTTA]: Uh, we have no guesses

00:53:25.122 --> 00:53:28.020
in the chat?

00:53:28.020 --> 00:53:30.070
[JOHN]: Can someone...

00:53:30.070 --> 00:53:32.280
Someone want to guess?

00:53:32.280 --> 00:53:35.579
[CARLOTTA]: We have a "B".

00:53:35.579 --> 00:53:38.760
[JOHN]: Uh, maybe.

00:53:38.760 --> 00:53:43.260
So, in order to do this,

00:53:43.260 --> 00:53:46.200
I mentioned the

00:53:46.200 --> 00:53:49.380
the module that is going to help me

00:53:49.380 --> 00:53:52.728
to divide my data into two things:

00:53:52.728 --> 00:53:53.819
70 percent for the

00:53:53.819 --> 00:53:56.220
the training and 30 percent for the

00:53:56.220 --> 00:53:59.339
evaluation. So what did I use? I used

00:53:59.339 --> 00:54:01.859
split data, because this is what is going

00:54:01.859 --> 00:54:05.280
to split my data randomly into training

00:54:05.280 --> 00:54:08.459
data and validation data. So the correct

00:54:08.459 --> 00:54:12.240
answer is "B", and good job. Thank you

00:54:12.240 --> 00:54:13.980
for participating.

00:54:13.980 --> 00:54:17.400
Next question, please.

00:54:17.400 --> 00:54:19.339
[CARLOTTA]: Yes, "B" is the correct

00:54:19.339 --> 00:54:22.559
answer, so thanks, John,

00:54:22.559 --> 00:54:26.040
for explaining to us the correct

00:54:26.040 --> 00:54:26.940
one.

00:54:26.940 --> 00:54:30.420
And we want to go with question two?

00:54:30.420 --> 00:54:33.180
[JOHN]: Yeah, so, 
I'm going to ask you now,

00:54:33.180 --> 00:54:35.880
Carlotta. You use Azure Machine Learning

00:54:35.880 --> 00:54:38.280
designer to create a training pipeline

00:54:38.280 --> 00:54:40.500
for your classification model.

00:54:40.500 --> 00:54:44.099
What must you do before you deploy this

00:54:44.099 --> 00:54:45.870
model as a service?
You have to do

00:54:45.870 --> 00:54:46.634
something before

00:54:46.634 --> 00:54:47.439
you deploy it.

00:54:47.439 --> 00:54:49.740
What do you think is the correct answer?

00:54:49.740 --> 00:54:52.740
Is it "A", "B", or "C"?

00:54:52.740 --> 00:54:55.020
Share your thoughts with—

00:54:55.020 --> 00:54:56.690
with us in the chat and

00:54:56.690 --> 00:55:00.180
and I'm also going to give you some

00:55:00.180 --> 00:55:02.940
minutes to think of it before I

00:55:02.940 --> 00:55:06.020
tell you about it.

00:55:06.020 --> 00:55:07.765
[CARLOTTA]: Yeah so let me go

00:55:07.765 --> 00:55:09.000
through the possible

00:55:09.000 --> 00:55:12.359
answers, right? So we have A: "Create an

00:55:12.359 --> 00:55:14.940
inference pipeline from the training

00:55:14.940 --> 00:55:16.020
pipeline";

00:55:16.020 --> 00:55:19.260
B: we have "Add an Evaluate Model

00:55:19.260 --> 00:55:22.380
module to the training pipeline; and then

00:55:22.380 --> 00:55:25.079
three, we have "Clone the training

00:55:25.079 --> 00:55:28.380
pipeline with a different name".

00:55:29.520 --> 00:55:31.559
So what do you think is the correct

00:55:31.559 --> 00:55:33.960
answer? "A", "B", or "C"?

00:55:33.960 --> 00:55:36.660
Also this time, I think it's something

00:55:36.660 --> 00:55:39.300
we mentioned both in the decks and in

00:55:39.300 --> 00:55:41.960
the demo right?

00:55:42.599 --> 00:55:44.819
[JOHN]: Yes it is,

00:55:44.819 --> 00:55:46.793
it's something that I have done

00:55:46.793 --> 00:55:50.410
like two, like five minutes ago.

00:55:51.800 --> 00:55:57.200
It's real-time, real-time.

00:55:57.200 --> 00:55:58.760
[CARLOTTA]: Um,

00:55:58.760 --> 00:56:02.040
yeah, so, think about...you need to deploy

00:56:02.040 --> 00:56:05.460
the model as a service. So if I'm

00:56:05.460 --> 00:56:07.980
going to deploy model,

00:56:07.980 --> 00:56:10.380
I cannot evaluate the model

00:56:10.380 --> 00:56:12.839
after deploying it, right, because I

00:56:12.839 --> 00:56:14.940
cannot go into production if I'm not

00:56:14.940 --> 00:56:17.579
sure, I'm not satisfied with my model, and

00:56:17.579 --> 00:56:19.500
I'm not sure that my model is performing

00:56:19.500 --> 00:56:20.280
well.

00:56:20.280 --> 00:56:22.900
So that's why I would go with,

00:56:22.900 --> 00:56:24.319
um,

00:56:24.319 --> 00:56:30.480
I would...exclude "B" from my

00:56:30.480 --> 00:56:31.520
answer.

00:56:31.520 --> 00:56:33.419
While

00:56:33.419 --> 00:56:36.960
thinking about "C", uh, I don't see you—I

00:56:36.960 --> 00:56:39.480
didn't see you, John, cloning the

00:56:39.480 --> 00:56:41.420
training Pipeline with a different name,

00:56:41.420 --> 00:56:44.640
so I don't think this is the

00:56:44.640 --> 00:56:46.920
right answer.

00:56:46.920 --> 00:56:49.619
While I've seen you creating an

00:56:49.619 --> 00:56:52.729
inference pipeline from the

00:56:52.729 --> 00:56:54.830
training pipeline, and you just converted

00:56:54.830 --> 00:56:59.280
it using a one-click button, right?

00:56:59.280 --> 00:57:01.400
[JOHN]: Yeah, that's correct.

00:57:01.400 --> 00:57:04.280
So this is the right answer.

00:57:04.280 --> 00:57:07.460
Good job. So I created an inference

00:57:07.460 --> 00:57:11.280
real-time pipeline, and it has done.

00:57:11.280 --> 00:57:13.440
It finished—it finished, the job is

00:57:13.440 --> 00:57:18.000
finished. So we can now deploy.

00:57:18.000 --> 00:57:19.400
And...

00:57:19.400 --> 00:57:21.500
Yeah [LAUGHS].

00:57:21.500 --> 00:57:25.339
Exactly, like, on time.

00:57:25.339 --> 00:57:27.839
Like, it finished two seconds...

00:57:27.839 --> 00:57:30.859
three, four seconds ago [LAUGHS].

00:57:30.859 --> 00:57:33.119
So, uh,

00:57:33.119 --> 00:57:36.480
until, um...

00:57:36.480 --> 00:57:39.839
This is my job review, so

00:57:39.839 --> 00:57:43.260
this is the job details that I

00:57:43.260 --> 00:57:45.540
have already submitted, it's just opening,

00:57:45.540 --> 00:57:47.459
and once it opens...

00:57:47.459 --> 00:57:50.180
um...

00:57:50.400 --> 00:57:52.740
I don't know why it's so heavy

00:57:52.740 --> 00:57:56.780
today, it's not like that usually.

00:57:57.780 --> 00:58:00.020
[CARLOTTA]: Yeah, it's probably because

00:58:00.020 --> 00:58:01.020
you are also

00:58:01.020 --> 00:58:06.000
showing your your screen on Teams,

00:58:06.000 --> 00:58:08.160
so that's the bandwidth of your

00:58:08.160 --> 00:58:08.944
connection.

00:58:08.944 --> 00:58:10.740
[JOHN]: Let me do something here

00:58:10.740 --> 00:58:13.740
because...yeah finally.

00:58:13.740 --> 00:58:16.440
I can switch to my mobile internet if it

00:58:16.440 --> 00:58:18.599
did it again. So I will click on "Deploy",

00:58:18.599 --> 00:58:20.700
it's that simple. I'll just click on

00:58:20.700 --> 00:58:23.040
"Deploy" and...

00:58:23.040 --> 00:58:25.619
I am going to deploy a new real-time

00:58:25.619 --> 00:58:27.960
endpoint.

00:58:27.960 --> 00:58:30.300
So what I'm going to name it?

00:58:30.300 --> 00:58:31.870
Description and the compute type.

00:58:31.870 --> 00:58:33.372
Everything is already mentioned

00:58:33.372 --> 00:58:34.140
for me here,

00:58:34.140 --> 00:58:36.240
so I'm just gonna copy and paste it,

00:58:36.240 --> 00:58:38.940
because we...we are running

00:58:38.940 --> 00:58:41.280
out of time.

00:58:41.280 --> 00:58:44.230
So it's all Azure Container Instance,

00:58:44.230 --> 00:58:46.360
not Azure Kubernetes Service,

00:58:46.360 --> 00:58:48.720
which is a containerization service also.

00:58:48.720 --> 00:58:50.867
Both are for containerization, but this

00:58:50.867 --> 00:58:53.613
gives you something, and this gives you
something else.

00:58:53.613 --> 00:58:54.960
For the advanced options,

00:58:54.960 --> 00:58:57.420
it doesn't say for us to do anything, so

00:58:57.420 --> 00:59:00.420
we are just gonna click on "Deploy",

00:59:00.420 --> 00:59:05.220
and now we can test our endpoint from

00:59:05.220 --> 00:59:07.859
the endpoints that we can find here, so

00:59:07.859 --> 00:59:11.460
it's in progress. If I go here

00:59:11.460 --> 00:59:13.799
under the assets, I can find something

00:59:13.799 --> 00:59:16.680
called "Endpoints", and I can find the

00:59:16.680 --> 00:59:18.599
real-time ones and the batch endpoints.

00:59:18.599 --> 00:59:22.020
And we have created a real-time endpoint,

00:59:22.020 --> 00:59:25.260
so we are going to find it under this

00:59:25.260 --> 00:59:29.760
title. So if I click on it, I should

00:59:29.760 --> 00:59:32.640
be able to test it once it's ready.

00:59:32.640 --> 00:59:37.200
It's still loading, but this is the

00:59:37.200 --> 00:59:40.980
input, and this is the output that we

00:59:40.980 --> 00:59:44.652
will get back, so if I click on "Test"...

00:59:44.652 --> 00:59:46.886
and from here,

00:59:46.886 --> 00:59:49.810
I will input some data to the

00:59:49.810 --> 00:59:50.900
endpoint,

00:59:50.900 --> 00:59:54.599
which are: the patient information; the

00:59:54.599 --> 00:59:57.119
columns that we have already seen in our

00:59:57.119 --> 01:00:00.380
data set; the patient ID; the pregnancies.

01:00:00.380 --> 01:00:03.960
And of course, of course I'm not gonna

01:00:03.960 --> 01:00:05.940
enter the label that I'm trying to

01:00:05.940 --> 01:00:08.099
predict, so I'm not going to give him if

01:00:08.099 --> 01:00:10.360
the patient is diabetic or not. This

01:00:10.360 --> 01:00:12.665
endpoint is to tell me this.

01:00:12.665 --> 01:00:14.599
The endpoint, or the URL,

01:00:14.599 --> 01:00:15.529
is going to give me

01:00:15.529 --> 01:00:17.640
back this information, whether someone

01:00:17.640 --> 01:00:22.680
has diabetes, or he doesn't. So if I input

01:00:22.680 --> 01:00:24.780
this data, I'm just going to copy it,

01:00:24.780 --> 01:00:27.780
and go to my endpoint, and click on

01:00:27.780 --> 01:00:30.180
"Test", I'm gonna give the result pack,

01:00:30.180 --> 01:00:32.359
which are the three columns that we have

01:00:32.359 --> 01:00:35.520
defined inside our python script: the

01:00:35.520 --> 01:00:37.859
patient ID, the diabetic prediction, and

01:00:37.859 --> 01:00:41.040
the probability—the certainty of whether

01:00:41.040 --> 01:00:45.720
someone is diabetic or not based on the...

01:00:45.720 --> 01:00:49.090
uh...based on the prediction.

01:00:49.090 --> 01:00:50.660
So that's it.

01:00:50.660 --> 01:00:54.359
And, uh, I think that this is a really

01:00:54.359 --> 01:00:56.729
simple step to do, you can do it on your

01:00:56.729 --> 01:00:58.380
own, you can test it.

01:00:58.380 --> 01:01:01.140
And I think that I have finished, so

01:01:01.140 --> 01:01:03.020
thank you.

01:01:03.020 --> 01:01:04.206
[CARLOTTA]: Uh, yes,

01:01:04.206 --> 01:01:06.069
we are running out of time

01:01:06.069 --> 01:01:09.780
I just wanted to thank you, John, for

01:01:09.780 --> 01:01:12.299
this demo, for going through all these

01:01:12.299 --> 01:01:13.429
steps to

01:01:13.429 --> 01:01:16.740
um, create, train a classification model,

01:01:16.740 --> 01:01:19.680
and also deploy it as a predictive

01:01:19.680 --> 01:01:22.880
service. And I encourage you all to go

01:01:22.880 --> 01:01:25.079
back to the learn module

01:01:25.079 --> 01:01:28.260
and, um, deepen all these topics

01:01:28.260 --> 01:01:31.760
at your own pace, and also maybe

01:01:31.760 --> 01:01:34.799
uh do this demo on your own, on your

01:01:34.799 --> 01:01:37.140
subscription on your Azure for Student

01:01:37.140 --> 01:01:39.359
subscription. Um...

01:01:39.359 --> 01:01:43.200
And I would also like to recall that

01:01:43.200 --> 01:01:46.140
this is part of a series of study

01:01:46.140 --> 01:01:49.500
sessions of Cloud Skill Challenge study

01:01:49.500 --> 01:01:51.059
sessions,

01:01:51.059 --> 01:01:54.059
so you will have more in the...

01:01:54.059 --> 01:01:57.540
in the following days, and this is for

01:01:57.540 --> 01:02:00.480
you to prepare, let's say, to help you

01:02:00.480 --> 01:02:04.880
in taking the Cloud Skills Challenge,

01:02:04.880 --> 01:02:07.040
which collect

01:02:07.040 --> 01:02:10.599
a very interesting learn module that you

01:02:10.599 --> 01:02:14.540
can use to scale up on various topics,

01:02:14.540 --> 01:02:18.359
and some of them are focused on AI and

01:02:18.359 --> 01:02:20.819
ML. So if you are interested in these

01:02:20.819 --> 01:02:23.099
topics, you can select these these learn

01:02:23.099 --> 01:02:24.780
modules.

01:02:24.780 --> 01:02:27.660
So let me also copy

01:02:27.660 --> 01:02:29.669
the link, the short link to the

01:02:29.669 --> 01:02:32.420
challenge in the chat. Remember that

01:02:32.420 --> 01:02:34.980
you have time until the 13th of

01:02:34.980 --> 01:02:37.980
September to take the challenge. And also

01:02:37.980 --> 01:02:40.440
remember that in October, on the 7th of

01:02:40.440 --> 01:02:43.020
October, you have the—you can join the

01:02:43.020 --> 01:02:46.619
student—the Student Developer Summit,

01:02:46.619 --> 01:02:50.480
which is, uh, which will be a virtual or

01:02:50.480 --> 01:02:53.220
in...for some for some cases a hybrid

01:02:53.220 --> 01:02:55.880
event, so stay tuned, because you will

01:02:55.880 --> 01:02:58.559
have some surprises in the following

01:02:58.559 --> 01:03:01.260
days. And if you want to learn more about

01:03:01.260 --> 01:03:03.480
this event you can check the Microsoft

01:03:03.480 --> 01:03:08.099
Imaging Cap Twitter page and stay tuned.

01:03:08.099 --> 01:03:11.230
So thank you everyone for joining

01:03:11.230 --> 01:03:12.989
this session today, and thank you very

01:03:12.989 --> 01:03:16.500
much, John, for co-hosting with this

01:03:16.500 --> 01:03:20.359
session with me. It was a pleasure.

01:03:21.227 --> 01:03:22.838
[JOHN]: Thank you so much,

01:03:22.838 --> 01:03:23.969
Carlotta, for having me

01:03:23.969 --> 01:03:26.249
with you today, and thank you for

01:03:26.249 --> 01:03:27.670
giving me this opportunity to

01:03:27.670 --> 01:03:30.180
be with you here.

01:03:30.180 --> 01:03:32.070
[CARLOTTA]: Great, thank you.

01:03:32.070 --> 01:03:33.420
[JOHN]: Yeah, I hope that we

01:03:33.420 --> 01:03:35.390
work again in the future.

01:03:35.390 --> 01:03:37.880
[CARLOTTA]: Sure, I hope so as well.

01:03:37.880 --> 01:03:40.700
Um, so, thank you everyone.

01:03:40.700 --> 01:03:43.749
And have a nice rest of your day.

01:03:44.099 --> 01:03:46.500
Bye-bye. Speak to you soon.

01:03:46.500 --> 01:03:48.920
[JOHN]: Bye.