0:00:01.920,0:00:04.680
great so I think we can start since the

0:00:04.680,0:00:07.859
meeting is recorded So if everyone uh

0:00:07.859,0:00:11.160
jump jumps in later can can watch the

0:00:11.160,0:00:12.420
recording

0:00:12.420,0:00:15.780
so hi everyone and welcome to these

0:00:15.780,0:00:18.000
um Cloud skill challenge study session

0:00:18.000,0:00:20.880
around a create classification models

0:00:20.880,0:00:24.000
with Azure machine learning designer

0:00:24.000,0:00:27.240
so today I'm thrilled to be here with

0:00:27.240,0:00:29.820
John uh John do my introduce briefly

0:00:29.820,0:00:31.619
yourself

0:00:31.619,0:00:34.160
uh thank you Carlotta hello everyone

0:00:34.160,0:00:38.160
Welcome to our Workshop today I hope

0:00:38.160,0:00:40.559
that you are all excited for it I am

0:00:40.559,0:00:43.140
John Aziz a gold Microsoft learn student

0:00:43.140,0:00:47.460
ambassador and I will be here with uh

0:00:47.460,0:00:50.760
Carlota to like do the Practical part

0:00:50.760,0:00:53.820
about this module of the cloud skills

0:00:53.820,0:00:57.000
challenge thank you for having me

0:00:57.000,0:00:59.219
perfect thanks John so for those who

0:00:59.219,0:01:03.440
don't know me I'm kellota

0:01:03.440,0:01:06.479
based in Italy and focus it on AI

0:01:06.479,0:01:08.760
machine learning Technologies and about

0:01:08.760,0:01:11.900
the use in education

0:01:12.060,0:01:13.200
um so

0:01:13.200,0:01:15.000
um these Cloud skill challenge study

0:01:15.000,0:01:17.580
session is based on a learn module a

0:01:17.580,0:01:21.540
dedicated learn module I sent to you uh

0:01:21.540,0:01:23.939
the link to this module uh in the chat

0:01:23.939,0:01:25.619
in a way that you can follow along the

0:01:25.619,0:01:28.680
model if you want or just have a look at

0:01:28.680,0:01:33.000
the module later at your own pace

0:01:33.000,0:01:33.780
um

0:01:33.780,0:01:37.020
so before starting I would also like to

0:01:37.020,0:01:40.619
remember to remember you uh the code of

0:01:40.619,0:01:43.439
conduct and guidelines of our student

0:01:43.439,0:01:47.640
Masters community so please during this

0:01:47.640,0:01:51.000
meeting be respectful and inclusive and

0:01:51.000,0:01:53.579
be friendly open and with coming and

0:01:53.579,0:01:56.159
respectful of other each other

0:01:56.159,0:01:57.720
differences

0:01:57.720,0:02:01.200
if you want to learn more about the code

0:02:01.200,0:02:03.540
of content you can use this link in the

0:02:03.540,0:02:08.880
deck again.ms slash s-a-c-o-c

0:02:09.660,0:02:12.420
and now we are

0:02:12.420,0:02:15.420
um we are ready to to start our session

0:02:15.420,0:02:18.959
so as we mentioned it we are going to

0:02:18.959,0:02:21.780
focus on classification models and Azure

0:02:21.780,0:02:24.900
ml uh today so first of all we are going

0:02:24.900,0:02:28.920
to um identify uh the kind of

0:02:28.920,0:02:31.080
um of scenarios in which you should

0:02:31.080,0:02:33.900
choose to use a classification model

0:02:33.900,0:02:36.660
we're going to introduce Azure machine

0:02:36.660,0:02:39.060
learning and Azure machine designer

0:02:39.060,0:02:41.879
we're going to understand uh which are

0:02:41.879,0:02:43.680
the steps to follow to create a

0:02:43.680,0:02:46.200
classification model in Azure mesh

0:02:46.200,0:02:48.599
learning and then John will

0:02:48.599,0:02:49.500
um

0:02:49.500,0:02:52.379
lead an amazing demo about training and

0:02:52.379,0:02:54.300
Publishing a classification model in

0:02:54.300,0:02:57.000
Azure ml designer

0:02:57.000,0:02:59.819
so let's start from the beginning let's

0:02:59.819,0:03:02.640
start from identifying classification

0:03:02.640,0:03:05.220
machine learning scenarios

0:03:05.220,0:03:07.640
so first of all what is classification

0:03:07.640,0:03:09.959
classification is a form of machine

0:03:09.959,0:03:12.120
learning that is used to predict which

0:03:12.120,0:03:15.599
category or class an item belongs to for

0:03:15.599,0:03:17.340
example we might want to develop a

0:03:17.340,0:03:19.800
classifier able to identify if an

0:03:19.800,0:03:22.200
Incoming Email should be filtered or not

0:03:22.200,0:03:25.080
according to the style the center the

0:03:25.080,0:03:28.140
length of the email Etc in this case the

0:03:28.140,0:03:30.060
characteristics of the email are the

0:03:30.060,0:03:31.080
features

0:03:31.080,0:03:34.200
and the label is a classification of

0:03:34.200,0:03:38.099
either a zero or one representing a Spam

0:03:38.099,0:03:40.860
or non-spam for the including email so

0:03:40.860,0:03:42.360
this is an example of a binary

0:03:42.360,0:03:44.400
classifier if you want to assign

0:03:44.400,0:03:46.260
multiple categories to the incoming

0:03:46.260,0:03:48.959
email like work letters love letters

0:03:48.959,0:03:52.080
complaints or other categories in this

0:03:52.080,0:03:54.000
case a binary classifier is not longer

0:03:54.000,0:03:55.739
enough and we should develop a

0:03:55.739,0:03:58.319
multi-class classifier so classification

0:03:58.319,0:04:00.599
is an example of what is called

0:04:00.599,0:04:02.519
supervised machine learning

0:04:02.519,0:04:05.280
in which you train a model using data

0:04:05.280,0:04:07.080
that includes both the features and

0:04:07.080,0:04:08.879
known values for label

0:04:08.879,0:04:11.099
so that the model learns to fit the

0:04:11.099,0:04:13.560
feature combinations to the label then

0:04:13.560,0:04:15.420
after training has been completed you

0:04:15.420,0:04:17.040
can use the train model to predict

0:04:17.040,0:04:19.500
labels for new items for for which the

0:04:19.500,0:04:22.320
label is unknown

0:04:22.320,0:04:25.440
but let's see some examples of scenarios

0:04:25.440,0:04:27.120
for classification machine learning

0:04:27.120,0:04:29.160
models so we already mentioned an

0:04:29.160,0:04:31.020
example of a solution in which we would

0:04:31.020,0:04:33.660
need a classifier but let's explore

0:04:33.660,0:04:35.699
other scenarios for classification in

0:04:35.699,0:04:37.979
other Industries for example you can use

0:04:37.979,0:04:40.380
a classification model for a health

0:04:40.380,0:04:43.680
clinic scenario and use clinical data to

0:04:43.680,0:04:45.720
predict whether patient will become sick

0:04:45.720,0:04:47.060
or not

0:04:47.060,0:04:49.680
uh you can use

0:04:49.680,0:04:51.740
um

0:05:03.780,0:05:07.860
oh sorry so when I became muted it's a

0:05:07.860,0:05:11.940
long time or you can use you can use uh

0:05:11.940,0:05:13.560
some models for classification for

0:05:13.560,0:05:16.919
example you can use you're saying this

0:05:16.919,0:05:21.199
uh so I I was I was

0:05:21.660,0:05:24.180
this one like you you have been muted

0:05:24.180,0:05:27.060
for uh one second okay okay perfect

0:05:27.060,0:05:30.419
perfect uh yeah I was talking sorry for

0:05:30.419,0:05:34.560
that so I was talking about the possible

0:05:34.560,0:05:37.320
you can use a classification model like

0:05:37.320,0:05:39.660
have Clinic scenario Financial scenario

0:05:39.660,0:05:41.699
or other third one is business type of

0:05:41.699,0:05:44.100
scenario you can use characteristics or

0:05:44.100,0:05:45.900
small business to predict if a new

0:05:45.900,0:05:47.880
Venture will will succeed or not for

0:05:47.880,0:05:49.560
example and these are all types of

0:05:49.560,0:05:52.160
binary classification

0:05:52.160,0:05:55.199
uh but today we are also going to talk

0:05:55.199,0:05:57.240
about Azure machine learning so let's

0:05:57.240,0:05:58.139
see

0:05:58.139,0:06:00.660
um what is azure Mash learning so

0:06:00.660,0:06:02.160
training and deploying an effective

0:06:02.160,0:06:04.199
machine learning model involves a lot of

0:06:04.199,0:06:06.539
work much of it time consuming and

0:06:06.539,0:06:08.880
resource intensive so Azure machine

0:06:08.880,0:06:11.039
learning is a cloud-based service that

0:06:11.039,0:06:12.780
helps simplify some of the tasks it

0:06:12.780,0:06:15.720
takes to prepare data train a model and

0:06:15.720,0:06:18.060
also deploy it as a predictive service

0:06:18.060,0:06:20.220
so it helps that the scientists increase

0:06:20.220,0:06:22.380
their efficiency by automating many of

0:06:22.380,0:06:24.660
the time consuming tasks Associated to

0:06:24.660,0:06:27.539
creating and training a model

0:06:27.539,0:06:29.520
and it enables them also to use

0:06:29.520,0:06:31.740
cloud-based compute resources that scale

0:06:31.740,0:06:33.720
effectively to handle large volumes of

0:06:33.720,0:06:36.300
data while incurring costs only when

0:06:36.300,0:06:38.699
actually used

0:06:38.699,0:06:41.220
to use Azure machine learning you

0:06:41.220,0:06:43.199
fasting fast you need to create a

0:06:43.199,0:06:44.940
workspace resource in your Azure

0:06:44.940,0:06:47.520
subscription and you can then use these

0:06:47.520,0:06:50.220
workspace to manage data compute

0:06:50.220,0:06:52.440
resources code models and other

0:06:52.440,0:06:55.139
artifacts after you have created an

0:06:55.139,0:06:56.819
Azure machine learning workspace you can

0:06:56.819,0:06:58.560
develop Solutions with the Azure machine

0:06:58.560,0:07:00.840
learning service either with developer

0:07:00.840,0:07:02.580
tools or the Azure machine Learning

0:07:02.580,0:07:04.380
Studio web portal

0:07:04.380,0:07:06.360
in particular International Learning

0:07:06.360,0:07:07.800
Studio is a web portal for machine

0:07:07.800,0:07:09.720
learning Solutions in Azure and it

0:07:09.720,0:07:11.639
includes a wide range of features and

0:07:11.639,0:07:13.800
capabilities that help data scientists

0:07:13.800,0:07:16.259
prepare data train models publish

0:07:16.259,0:07:18.479
Predictive Services and monitor also

0:07:18.479,0:07:19.680
their usage

0:07:19.680,0:07:22.139
so to begin using the web portal you

0:07:22.139,0:07:23.880
need to assign the workspace you created

0:07:23.880,0:07:26.819
in the Azure portal to the Azure machine

0:07:26.819,0:07:29.419
Learning Studio

0:07:29.520,0:07:31.800
as its core Azure Mash learning is a

0:07:31.800,0:07:33.720
service for training and managing

0:07:33.720,0:07:36.000
machine learning models for which you

0:07:36.000,0:07:38.220
need compute resources on which to run

0:07:38.220,0:07:39.919
the training process

0:07:39.919,0:07:44.280
compute targets are um one of the main

0:07:44.280,0:07:46.740
basic concept of azure Mash learning

0:07:46.740,0:07:48.780
they are cloud-based resources on which

0:07:48.780,0:07:50.639
you can run model training and AD

0:07:50.639,0:07:53.220
exploration processes

0:07:53.220,0:07:54.780
so initial machine Learning Studio you

0:07:54.780,0:07:56.759
can manage the compute targets for your

0:07:56.759,0:07:58.740
data science activities and there are

0:07:58.740,0:08:03.240
four kinds of of compute targets you can

0:08:03.240,0:08:05.940
create we have the computer instances

0:08:05.940,0:08:09.539
which are vital machine set up for

0:08:09.539,0:08:10.979
running machine learning code during

0:08:10.979,0:08:13.319
development so they are not designed for

0:08:13.319,0:08:14.460
production

0:08:14.460,0:08:17.099
then we have compute clusters which are

0:08:17.099,0:08:19.800
a set of virtual machines that can scale

0:08:19.800,0:08:22.199
up automatically based on traffic

0:08:22.199,0:08:24.599
we have inference clusters which are

0:08:24.599,0:08:26.699
similar to compute clusters but they are

0:08:26.699,0:08:29.340
designed for deployment so they are a

0:08:29.340,0:08:31.979
deployment targets for Predictive

0:08:31.979,0:08:35.820
Services that use train models

0:08:35.820,0:08:38.339
and finally we have attached compute

0:08:38.339,0:08:41.339
which are any compute Target that you

0:08:41.339,0:08:44.159
manage yourself outside of azramel like

0:08:44.159,0:08:46.560
for example virtual machines or Azure

0:08:46.560,0:08:49.700
databricks clusters

0:08:49.980,0:08:52.800
so we talked about Azure machine

0:08:52.800,0:08:54.300
learning but we also mentioned it

0:08:54.300,0:08:55.500
mentioned Azure machine learning

0:08:55.500,0:08:57.540
designer what is azure machine learning

0:08:57.540,0:09:00.120
designer so initial machine Learning

0:09:00.120,0:09:02.880
Studio there are several ways to author

0:09:02.880,0:09:04.560
classification machine learning models

0:09:04.560,0:09:08.100
one way is to use a visual interface and

0:09:08.100,0:09:10.260
this visual interface is called designer

0:09:10.260,0:09:13.140
and you can use it to train test and

0:09:13.140,0:09:15.540
also deploy machine learning models and

0:09:15.540,0:09:17.940
the drag and drop interface makes use of

0:09:17.940,0:09:20.279
clearly defined inputs and outputs that

0:09:20.279,0:09:22.680
can be shared reused and also Version

0:09:22.680,0:09:23.880
Control

0:09:23.880,0:09:25.920
and using the designer you can identify

0:09:25.920,0:09:28.080
the building blocks or components needed

0:09:28.080,0:09:30.839
for your model place and connect them on

0:09:30.839,0:09:33.120
your canvas and run a machine learning

0:09:33.120,0:09:35.300
job

0:09:35.399,0:09:36.779
so

0:09:36.779,0:09:39.120
um each designer project so each project

0:09:39.120,0:09:42.360
in the designer is known as a pipeline

0:09:42.360,0:09:45.600
and in the design we have a left panel

0:09:45.600,0:09:48.360
for navigation and a canvas on your

0:09:48.360,0:09:50.640
right hand side in which you build your

0:09:50.640,0:09:53.940
pipeline visually so pipelines let you

0:09:53.940,0:09:56.100
organize manage and reuse complex

0:09:56.100,0:09:58.260
machine learning workflows across

0:09:58.260,0:10:00.480
projects and users

0:10:00.480,0:10:03.000
a pipeline starts with the data set from

0:10:03.000,0:10:04.140
which you want to train the model

0:10:04.140,0:10:05.880
because all begins with data when

0:10:05.880,0:10:07.380
talking about data science and machine

0:10:07.380,0:10:09.540
learning and each time you run a

0:10:09.540,0:10:10.980
pipeline the configuration of the

0:10:10.980,0:10:12.959
pipeline and its results are stored in

0:10:12.959,0:10:17.339
your workspace as a pipeline job

0:10:17.339,0:10:21.959
so the second main concept of azure

0:10:21.959,0:10:25.080
machine learning is a component so going

0:10:25.080,0:10:28.440
hierarchically from the pipeline we can

0:10:28.440,0:10:30.540
say that each building block of a

0:10:30.540,0:10:33.920
pipeline is called a component

0:10:33.920,0:10:36.959
learning component encapsulate one step

0:10:36.959,0:10:39.420
in a machine learning pipeline so it's a

0:10:39.420,0:10:41.640
reusable piece of code with inputs and

0:10:41.640,0:10:44.100
outputs something very similar to a

0:10:44.100,0:10:46.500
function in any programming language

0:10:46.500,0:10:48.899
and in a pipeline project you can access

0:10:48.899,0:10:51.480
data assets and components from the left

0:10:51.480,0:10:52.700
panels

0:10:52.700,0:10:56.279
asset Library tab as you can see

0:10:56.279,0:11:00.200
um here in the screenshot in the deck

0:11:00.300,0:11:03.360
so you can create data assets on using

0:11:03.360,0:11:08.339
an adoc page called Data Page and a data

0:11:08.339,0:11:11.160
set is a reference to a data source

0:11:11.160,0:11:12.480
location

0:11:12.480,0:11:15.720
so this data source location could be a

0:11:15.720,0:11:18.779
local file a data store a web file or

0:11:18.779,0:11:21.660
even an age group open that set

0:11:21.660,0:11:23.880
and these data assets will appear along

0:11:23.880,0:11:26.459
with standard sample data set in the

0:11:26.459,0:11:30.019
designers asset Library

0:11:30.839,0:11:31.560
um

0:11:31.560,0:11:36.959
and another basic concept of azure ml is

0:11:36.959,0:11:38.880
azure machine learning jobs

0:11:38.880,0:11:43.519
so basically when you submit a pipeline

0:11:43.519,0:11:47.040
you create a job which will run all the

0:11:47.040,0:11:49.920
steps in your pipeline so a job execute

0:11:49.920,0:11:52.800
a task against a specified compute

0:11:52.800,0:11:53.760
Target

0:11:53.760,0:11:56.640
jobs enable systematic tracking for your

0:11:56.640,0:11:58.560
machine learning experimentation in

0:11:58.560,0:11:59.880
Azure ml

0:11:59.880,0:12:02.399
and once a job is created azramel

0:12:02.399,0:12:05.459
maintains a run record uh for for the

0:12:05.459,0:12:07.640
job

0:12:08.399,0:12:12.180
um but let's move to the classification

0:12:12.180,0:12:14.040
steps so

0:12:14.040,0:12:17.160
um let's introduce uh how to create a

0:12:17.160,0:12:21.360
classification model in Azure ml but you

0:12:21.360,0:12:23.640
will see it in more details in a

0:12:23.640,0:12:26.339
handsome demo that John will will guide

0:12:26.339,0:12:29.459
through in a few minutes

0:12:29.459,0:12:32.220
so you can think of the steps to train

0:12:32.220,0:12:33.720
and evaluate a classification machine

0:12:33.720,0:12:36.660
learning model as four main steps so

0:12:36.660,0:12:38.459
first of all you need to prepare your

0:12:38.459,0:12:41.100
data so you need to identify the

0:12:41.100,0:12:43.139
features and the label in your data set

0:12:43.139,0:12:46.139
you need to pre-process so you need to

0:12:46.139,0:12:48.839
clean and transform the data as needed

0:12:48.839,0:12:51.120
then the second step of course is

0:12:51.120,0:12:52.740
training the model

0:12:52.740,0:12:54.600
and for training the model you need to

0:12:54.600,0:12:57.060
split the data into two groups a

0:12:57.060,0:12:59.519
training and a validation set

0:12:59.519,0:13:01.320
then you train a machine learning model

0:13:01.320,0:13:03.540
using the training data set and you test

0:13:03.540,0:13:05.040
the machine learning model for

0:13:05.040,0:13:07.019
performance using the validation data

0:13:07.019,0:13:08.100
set

0:13:08.100,0:13:12.180
the third step is performance evaluation

0:13:12.180,0:13:14.519
um which means comparing how close the

0:13:14.519,0:13:16.139
model's predictions are to the known

0:13:16.139,0:13:20.519
labels and these lead us to compute some

0:13:20.519,0:13:23.279
evaluation performance metrics

0:13:23.279,0:13:25.740
and then finally

0:13:25.740,0:13:29.880
um so these three steps are not

0:13:29.880,0:13:33.000
um not performed uh every time in a

0:13:33.000,0:13:35.459
linear manner it's more an iterative

0:13:35.459,0:13:39.420
process but once you obtain you achieve

0:13:39.420,0:13:42.959
a a performance with which you are

0:13:42.959,0:13:45.779
satisfied so you are ready to let's say

0:13:45.779,0:13:48.660
go into production and you can deploy

0:13:48.660,0:13:51.920
your train model as a predictive service

0:13:51.920,0:13:55.980
into a real-time uh to a real-time

0:13:55.980,0:13:58.019
endpoint and to do so you need to

0:13:58.019,0:14:00.240
convert the training pipeline into a

0:14:00.240,0:14:02.820
real-time inference Pipeline and then

0:14:02.820,0:14:04.260
you can deploy the model as an

0:14:04.260,0:14:06.779
application on a server or device so

0:14:06.779,0:14:11.420
that others can consume this model

0:14:11.459,0:14:14.279
so let's start with the first step which

0:14:14.279,0:14:17.700
is prepaid data reward data can contain

0:14:17.700,0:14:19.920
many different issues that can affect

0:14:19.920,0:14:22.320
the utility of the data and our

0:14:22.320,0:14:24.959
interpretation of the results so also

0:14:24.959,0:14:26.579
the machine learning model that you

0:14:26.579,0:14:29.399
train using this data for example real

0:14:29.399,0:14:31.440
world data can be affected by a bed

0:14:31.440,0:14:34.079
recording or a bad measurement and it

0:14:34.079,0:14:36.480
can also contain missing values for some

0:14:36.480,0:14:38.880
parameters and Azure machine learning

0:14:38.880,0:14:40.860
designer has several pre-built

0:14:40.860,0:14:43.019
components that can be used to prepaid

0:14:43.019,0:14:46.079
data for training these components

0:14:46.079,0:14:48.300
enable you to clean data normalize

0:14:48.300,0:14:52.940
features join tables and and more

0:14:53.000,0:14:57.120
let's come to uh training so to train a

0:14:57.120,0:14:59.220
classification model you need a data set

0:14:59.220,0:15:02.160
that includes historical features so the

0:15:02.160,0:15:03.899
characteristics of the entity for which

0:15:03.899,0:15:06.899
one to make a prediction and known label

0:15:06.899,0:15:09.779
values the label is the class indicator

0:15:09.779,0:15:11.820
we want to train a model to predict it

0:15:11.820,0:15:13.920
and it's common practice to train a

0:15:13.920,0:15:16.199
model using a subset of the data while

0:15:16.199,0:15:18.300
holding back some data with which to

0:15:18.300,0:15:20.760
test the train model and this enables

0:15:20.760,0:15:22.440
you to compare the labels that the model

0:15:22.440,0:15:25.380
predicts with the actual known labels in

0:15:25.380,0:15:27.420
the original data set

0:15:27.420,0:15:29.880
this operation can be performed in the

0:15:29.880,0:15:32.100
designer using the split data component

0:15:32.100,0:15:34.740
as shown by the screenshot here in the

0:15:34.740,0:15:36.660
in the deck

0:15:36.660,0:15:39.540
there's also another component that you

0:15:39.540,0:15:40.980
should use which is the score model

0:15:40.980,0:15:43.139
component to generate the predicted

0:15:43.139,0:15:45.360
class label value using the validation

0:15:45.360,0:15:48.060
data as input so once you connect all

0:15:48.060,0:15:49.800
these components

0:15:49.800,0:15:52.440
um the component specifying the the

0:15:52.440,0:15:54.959
model we are going to use the split data

0:15:54.959,0:15:57.060
component the trained model component

0:15:57.060,0:16:00.300
and the score model component you want

0:16:00.300,0:16:02.639
to run an a new experiment in the

0:16:02.639,0:16:05.760
initial map which will use the data set

0:16:05.760,0:16:09.600
on the canvas to train and score a model

0:16:09.600,0:16:12.000
after training a model it is important

0:16:12.000,0:16:14.639
we say to evaluate its performance to

0:16:14.639,0:16:17.060
understand how bad how how good sorry

0:16:17.060,0:16:20.760
our model is performing

0:16:20.760,0:16:22.680
and there are many performance metrics

0:16:22.680,0:16:24.600
and methodologies for evaluating how

0:16:24.600,0:16:27.000
well a model makes predictions the

0:16:27.000,0:16:29.160
component to use to perform evaluation

0:16:29.160,0:16:32.220
in Azure ml designer is called as

0:16:32.220,0:16:35.060
intuitive as it is evaluate model

0:16:35.060,0:16:38.339
once the job of training and evaluation

0:16:38.339,0:16:40.740
of the model is completed you can review

0:16:40.740,0:16:42.959
evaluation metrics on the completed job

0:16:42.959,0:16:45.860
Page by right clicking on the component

0:16:45.860,0:16:48.480
in the evaluation results you can also

0:16:48.480,0:16:51.000
find the so-called confusion Matrix that

0:16:51.000,0:16:53.399
you can see here in the right side of of

0:16:53.399,0:16:55.079
this deck

0:16:55.079,0:16:57.420
a confusion Matrix shows cases where

0:16:57.420,0:16:59.220
both the predicted and actual values

0:16:59.220,0:17:01.980
were one uh the so-called true positives

0:17:01.980,0:17:04.500
at the top left and also cases where

0:17:04.500,0:17:06.600
both the predicted and the actual values

0:17:06.600,0:17:08.459
were zero the so-called true negatives

0:17:08.459,0:17:10.919
at the bottom right while the other

0:17:10.919,0:17:13.679
cells show cases where the predicting

0:17:13.679,0:17:15.380
and actual values differ

0:17:15.380,0:17:17.939
called false positive and false

0:17:17.939,0:17:19.919
negatives and this is an example of a

0:17:19.919,0:17:23.579
confusion Matrix for a binary classifier

0:17:23.579,0:17:25.559
why for a multi-class classification

0:17:25.559,0:17:28.079
model the same approach is used to

0:17:28.079,0:17:30.120
tabulate each possible combination of

0:17:30.120,0:17:32.940
actual and predictive value counts so

0:17:32.940,0:17:34.740
for example a model with three possible

0:17:34.740,0:17:37.559
classes would result in three times

0:17:37.559,0:17:39.120
three Matrix

0:17:39.120,0:17:41.880
the confusion Matrix is also useful for

0:17:41.880,0:17:43.860
the metrics that can be derived from it

0:17:43.860,0:17:48.260
like accuracy recall or precision

0:17:49.320,0:17:52.080
um we we say that the last step is

0:17:52.080,0:17:55.620
deploying the train model to a real-time

0:17:55.620,0:17:59.280
endpoint as a predictive service and in

0:17:59.280,0:18:00.900
order to automate your model into

0:18:00.900,0:18:02.760
service that makes continuous

0:18:02.760,0:18:04.980
predictions you need first of all to

0:18:04.980,0:18:08.039
create any and and then deploy an

0:18:08.039,0:18:10.080
inference pipeline the process of

0:18:10.080,0:18:11.940
converting the training pipeline into a

0:18:11.940,0:18:13.980
real-time inference pipeline removes

0:18:13.980,0:18:16.260
training components and adds web service

0:18:16.260,0:18:18.960
inputs and outputs to handle requests

0:18:18.960,0:18:21.240
and the inference pipeline performs they

0:18:21.240,0:18:22.679
seem that the transformation as the

0:18:22.679,0:18:26.160
first pipeline but for new data then it

0:18:26.160,0:18:28.679
uses the train model to infer or predict

0:18:28.679,0:18:32.539
label values based on its feature

0:18:32.820,0:18:36.120
um so I think I've talked a lot for now

0:18:36.120,0:18:40.380
I would like to let John show us

0:18:40.380,0:18:44.340
something in practice uh with uh with

0:18:44.340,0:18:47.280
the Hands-On demo so please John go

0:18:47.280,0:18:49.860
ahead sharing your screen and guide us

0:18:49.860,0:18:52.380
through this demo of creating a

0:18:52.380,0:18:53.760
classification with the Azure machine

0:18:53.760,0:18:55.860
learning designer

0:18:55.860,0:18:58.919
uh thank you so much Carlotta for this

0:18:58.919,0:19:01.380
interesting explanation of the Azure ml

0:19:01.380,0:19:04.740
designer and now

0:19:04.740,0:19:07.500
um I'm going to start with you in the

0:19:07.500,0:19:10.200
Practical demo part so uh if you want to

0:19:10.200,0:19:13.320
follow along go to the link that Carlota

0:19:13.320,0:19:18.380
sent in the chat so like you can do

0:19:18.380,0:19:21.840
the demo or the Practical part with me

0:19:21.840,0:19:25.260
I'm just going to share my screen

0:19:25.260,0:19:27.140
and

0:19:27.140,0:19:31.559
go here so uh

0:19:31.559,0:19:34.320
where am I right now I'm inside the

0:19:34.320,0:19:36.960
Microsoft learn documentation this is

0:19:36.960,0:19:40.260
the exercise part of this module and we

0:19:40.260,0:19:43.080
will start by setting two things which

0:19:43.080,0:19:45.299
are a prequisite for us to work inside

0:19:45.299,0:19:49.919
this module which are the users group

0:19:49.919,0:19:52.400
and the Azure machine learning workspace

0:19:52.400,0:19:55.620
and something extra which is the compute

0:19:55.620,0:19:59.760
cluster that calendar Target about so I

0:19:59.760,0:20:02.100
just want to make sure that you all have

0:20:02.100,0:20:05.660
a resource Group created inside your

0:20:05.660,0:20:08.039
auditor inside your Microsoft Azure

0:20:08.039,0:20:11.100
platform so this is my research group

0:20:11.100,0:20:14.640
inside this is this Resource Group I

0:20:14.640,0:20:17.299
have created an Azure machine learning

0:20:17.299,0:20:21.539
workspace so I'm just going to access

0:20:21.539,0:20:24.000
the workspace that I have created

0:20:24.000,0:20:27.000
already from this link I am going to

0:20:27.000,0:20:30.240
open it which is the studio web URL and

0:20:30.240,0:20:33.000
I will follow the steps so what is this

0:20:33.000,0:20:35.760
this is your machine learning workspace

0:20:35.760,0:20:38.220
or machine Learning Studio you can do a

0:20:38.220,0:20:40.080
lot of things here but we are going to

0:20:40.080,0:20:42.419
focus mainly on the designer and the

0:20:42.419,0:20:46.080
data and the compute so another

0:20:46.080,0:20:49.140
prequisite here as Carlotta told you and

0:20:49.140,0:20:51.480
we need some resources to power up the

0:20:51.480,0:20:54.299
the classification the processes that

0:20:54.299,0:20:55.140
will happen

0:20:55.140,0:20:58.080
so we have created this Computing

0:20:58.080,0:20:59.100
cluster

0:20:59.100,0:21:02.880
and we have like Set uh some presets for

0:21:02.880,0:21:04.140
it so

0:21:04.140,0:21:07.080
where can you find this preset you go

0:21:07.080,0:21:10.200
here under the create compute you'll

0:21:10.200,0:21:13.220
find everything that you need to do so

0:21:13.220,0:21:16.740
the size is the Standard ds11 Version 2

0:21:16.740,0:21:19.799
and it's a CPU not GPU because we don't

0:21:19.799,0:21:22.500
know the GPU and we don't need a GPU and

0:21:22.500,0:21:25.799
a like it is ready for us to use

0:21:25.799,0:21:30.900
the next thing which we will look into

0:21:30.900,0:21:33.600
is the designer how can you access the

0:21:33.600,0:21:35.100
designer

0:21:35.100,0:21:37.679
you can either click on this icon or

0:21:37.679,0:21:40.020
click on the navigation menu and click

0:21:40.020,0:21:42.299
on the designer for me

0:21:42.299,0:21:42.900
um

0:21:42.900,0:21:45.780
now I am inside my designer

0:21:45.780,0:21:47.640
what we are going to do now is the

0:21:47.640,0:21:50.280
pipeline that Carlotta told you about

0:21:50.280,0:21:54.360
and from where can I know these steps if

0:21:54.360,0:21:57.120
you follow along in the learn module you

0:21:57.120,0:21:58.740
will find everything that I'm doing

0:21:58.740,0:22:02.340
right now in details uh with screenshots

0:22:02.340,0:22:05.820
of course so I'm going to create a new

0:22:05.820,0:22:09.120
pipeline and I can do so by clicking on

0:22:09.120,0:22:10.980
this plus button

0:22:10.980,0:22:13.740
it's going to redirect me to the

0:22:13.740,0:22:17.100
designer authoring the pipeline uh where

0:22:17.100,0:22:19.500
I can drag and drop data and components

0:22:19.500,0:22:21.780
that the Carlota told you the difference

0:22:21.780,0:22:22.980
between

0:22:22.980,0:22:26.340
and here I am going to do some changes

0:22:26.340,0:22:29.100
to the settings I am going to connect

0:22:29.100,0:22:31.860
this with my compute cluster that I

0:22:31.860,0:22:35.120
created previously so I can utilize it

0:22:35.120,0:22:38.100
from here I'm going to choose this

0:22:38.100,0:22:40.380
compute cluster demo that I have showed

0:22:40.380,0:22:42.600
you before in the Clusters here

0:22:42.600,0:22:45.900
and I am going to change the name to

0:22:45.900,0:22:47.820
something more meaningful instead of

0:22:47.820,0:22:50.580
byline and the date of today I'm going

0:22:50.580,0:22:53.760
to name it diabetes

0:22:53.760,0:22:56.120
and

0:22:56.120,0:23:00.020
let's just check this training

0:23:00.020,0:23:05.100
let's say training 0.1 or okay

0:23:05.100,0:23:09.360
and I am going to close this tab and in

0:23:09.360,0:23:12.000
order to have a bigger place to work

0:23:12.000,0:23:14.700
inside because this is where we will

0:23:14.700,0:23:17.220
work where everything will happen so I

0:23:17.220,0:23:19.559
will click on close from here

0:23:19.559,0:23:23.460
and I will go to the data and I will

0:23:23.460,0:23:25.620
create a new data set

0:23:25.620,0:23:27.900
how can I create a new data set there is

0:23:27.900,0:23:29.880
multiple options here you can find from

0:23:29.880,0:23:31.799
local files from data store from web

0:23:31.799,0:23:34.020
files from open data set but I'm going

0:23:34.020,0:23:36.539
to choose from web files as this is the

0:23:36.539,0:23:40.280
way we're going to create our data

0:23:40.280,0:23:43.380
from here the information of my data set

0:23:43.380,0:23:47.340
I'm going to get them from the Microsoft

0:23:47.340,0:23:50.820
learn module so if we go to the step

0:23:50.820,0:23:52.860
that says create a data set

0:23:52.860,0:23:55.020
under it it illustrates that you can

0:23:55.020,0:23:57.720
access the data from inside the asset

0:23:57.720,0:23:59.760
library and inside your asset liability

0:23:59.760,0:24:01.679
you'll find the data and find the

0:24:01.679,0:24:05.539
component and I'm going to select

0:24:05.539,0:24:09.000
this link because this is where my data

0:24:09.000,0:24:12.000
is stored if you open this link you will

0:24:12.000,0:24:14.820
find this is this is a CSV file I think

0:24:14.820,0:24:17.400
yeah and you can like all the data are

0:24:17.400,0:24:18.360
here

0:24:18.360,0:24:21.419
all right now let's get back

0:24:21.419,0:24:21.540
um

0:24:21.540,0:24:24.770
[Music]

0:24:26.880,0:24:28.200
and you are going to do something

0:24:28.200,0:24:29.880
meaningful but because I have already

0:24:29.880,0:24:31.820
created it before twice so I'm gonna

0:24:31.820,0:24:34.980
like add a number to the name

0:24:34.980,0:24:37.559
uh the data set is tabular and there is

0:24:37.559,0:24:39.360
the file but this is a table so we're

0:24:39.360,0:24:40.760
going to choose the table

0:24:40.760,0:24:42.240
[Music]

0:24:42.240,0:24:43.740
for data set time

0:24:43.740,0:24:46.260
now we will click on next that's gonna

0:24:46.260,0:24:51.179
review or uh display for you the content

0:24:51.179,0:24:54.020
of this file that you have

0:24:54.020,0:24:57.419
like imported to this workspace

0:24:57.419,0:25:01.559
and for these settings these are like

0:25:01.559,0:25:03.720
related to our filed format

0:25:03.720,0:25:08.280
so this is a delimited file and it's not

0:25:08.280,0:25:11.400
plain text it's not a Json the delimiter

0:25:11.400,0:25:14.159
is comma as like we have seen that they

0:25:14.159,0:25:16.640
those

0:25:26.700,0:25:29.039
so I'm choosing

0:25:29.039,0:25:32.900
errors because the only the first five

0:25:33.510,0:25:34.880
[Music]

0:25:34.880,0:25:38.159
for example okay uh if you have any

0:25:38.159,0:25:39.960
doubts if you have any problems please

0:25:39.960,0:25:42.960
don't hesitate to all right through me

0:25:42.960,0:25:45.020
in the chat

0:25:45.020,0:25:48.480
and like what what is blocking you and

0:25:48.480,0:25:50.940
me and Carlota will try to help you and

0:25:50.940,0:25:53.220
like whenever possible

0:25:53.220,0:25:55.799
and now this is the new preview for my

0:25:55.799,0:25:57.840
data set I can see that I have an ID I

0:25:57.840,0:25:59.700
have patient ID I have pregnancies I

0:25:59.700,0:26:02.220
have the age of the people

0:26:02.220,0:26:05.720
have the body mass together I think

0:26:05.720,0:26:08.460
and they have diabetical or not as a

0:26:08.460,0:26:10.679
zero and one zero indicates a negative

0:26:10.679,0:26:14.159
the person doesn't have diabetes and one

0:26:14.159,0:26:16.080
indicates a positive that this person

0:26:16.080,0:26:18.299
has diabetes okay

0:26:18.299,0:26:20.520
now I'm going to click on next here I am

0:26:20.520,0:26:23.400
defining my schema all the data types

0:26:23.400,0:26:25.380
inside my columns the column names which

0:26:25.380,0:26:28.760
columns to include which to exclude and

0:26:28.760,0:26:31.500
here we will include everything except

0:26:31.500,0:26:35.580
the path of the bath color and we are

0:26:35.580,0:26:37.860
going to review the data types of each

0:26:37.860,0:26:40.440
column so let's review this first one

0:26:40.440,0:26:43.320
this is numbers numbers then it's the

0:26:43.320,0:26:45.779
integer and this is

0:26:45.779,0:26:48.679
um like decimal

0:26:48.679,0:26:50.900
dotted

0:26:50.900,0:26:53.580
decimal number so we are going to choose

0:26:53.580,0:26:55.020
this data type

0:26:55.020,0:26:57.200
and for this one

0:26:57.200,0:27:01.200
it says diabetic and it's a zero under

0:27:01.200,0:27:02.460
one and we are going to make it as

0:27:02.460,0:27:04.460
integerables

0:27:04.460,0:27:07.980
now we are going to click on next and

0:27:07.980,0:27:10.080
move to reviewing everything this is

0:27:10.080,0:27:11.279
everything that we have defined together

0:27:11.279,0:27:13.500
I will click on create

0:27:13.500,0:27:15.179
and

0:27:15.179,0:27:17.940
now the first step has ended we have

0:27:17.940,0:27:19.919
gotten our data ready

0:27:19.919,0:27:22.440
now what now we're going to utilize the

0:27:22.440,0:27:24.360
designer

0:27:24.360,0:27:26.820
um Power we're going to drag and drop

0:27:26.820,0:27:29.820
our data set to create the pipeline

0:27:29.820,0:27:33.179
so I have like click on it and drag it

0:27:33.179,0:27:35.640
to this space it's gonna appear to you

0:27:35.640,0:27:39.659
and we can inspect it by right click and

0:27:39.659,0:27:42.179
choose preview data

0:27:42.179,0:27:46.200
to see what we have created together

0:27:46.200,0:27:48.900
from here you can see everything that we

0:27:48.900,0:27:50.700
have like seen previously but in more

0:27:50.700,0:27:53.100
details and we are just going to close

0:27:53.100,0:27:56.580
this now what now we are gonna do the

0:27:56.580,0:28:00.799
processing that Carlota like mentioned

0:28:00.799,0:28:03.659
these are some instructions about the

0:28:03.659,0:28:05.460
data about how you can loot them how you

0:28:05.460,0:28:07.140
can open them but we are going to move

0:28:07.140,0:28:09.720
to the transformation or the processing

0:28:09.720,0:28:13.500
so as Carlotta told you like any data

0:28:13.500,0:28:15.480
for us to work on we have to do some

0:28:15.480,0:28:17.299
processing to it

0:28:17.299,0:28:20.159
to make it easy easier for the model to

0:28:20.159,0:28:23.279
be trained and easier to work with so uh

0:28:23.279,0:28:25.860
we're gonna do the normalization and

0:28:25.860,0:28:29.159
normalization meaning is uh

0:28:29.159,0:28:33.539
to scale our data either down or up but

0:28:33.539,0:28:35.400
we're going to scale them down

0:28:35.400,0:28:38.820
and like we are going to decrease and

0:28:38.820,0:28:40.799
relatively decrease

0:28:40.799,0:28:44.640
the the values all the values to work

0:28:44.640,0:28:48.120
with lower numbers and if we are working

0:28:48.120,0:28:49.559
with larger numbers it's going to take

0:28:49.559,0:28:52.500
more time if we're working with smaller

0:28:52.500,0:28:54.779
numbers it's going to take less time to

0:28:54.779,0:28:59.159
calculate them and that's it so

0:28:59.159,0:29:02.159
where can I find the normalized data I

0:29:02.159,0:29:04.260
can find it inside my component

0:29:04.260,0:29:06.720
so I will choose the component and

0:29:06.720,0:29:09.659
search for normalized data

0:29:09.659,0:29:12.360
I will drag and drop it as usual and I

0:29:12.360,0:29:14.820
will connect between these two things

0:29:14.820,0:29:18.360
by clicking on this spot this like

0:29:18.360,0:29:20.159
Circle and

0:29:20.159,0:29:23.159
drag and drop until the next circuit

0:29:23.159,0:29:24.899
now we are going to Define our

0:29:24.899,0:29:27.419
normalization method

0:29:27.419,0:29:31.080
so I'm going to double click on the

0:29:31.080,0:29:32.640
normalized data

0:29:32.640,0:29:34.860
it's going to open the settings for the

0:29:34.860,0:29:36.480
normalization

0:29:36.480,0:29:38.820
as better transformation method which is

0:29:38.820,0:29:40.500
a mathematical way

0:29:40.500,0:29:42.299
that is going to scale our data

0:29:42.299,0:29:44.520
according to

0:29:44.520,0:29:47.760
we're going to choose min max and for

0:29:47.760,0:29:51.539
this one we are going to choose use 0

0:29:51.539,0:29:53.100
for constant column we are going to

0:29:53.100,0:29:54.480
choose true

0:29:54.480,0:29:56.880
and we are going to Define which columns

0:29:56.880,0:29:58.860
to normalize so we are not going to

0:29:58.860,0:30:01.080
normalize the whole data set we are

0:30:01.080,0:30:02.760
going to choose a subset from the data

0:30:02.760,0:30:04.559
set to normalize so we're going to

0:30:04.559,0:30:07.020
choose everything except for the patient

0:30:07.020,0:30:09.000
ID and the diabetic because the patient

0:30:09.000,0:30:10.919
ID is a number but it's a categorical

0:30:10.919,0:30:13.740
data it describes a vision it's not a

0:30:13.740,0:30:17.460
number that I can sum I can say patient

0:30:17.460,0:30:20.159
ID number one plus patient ID number two

0:30:20.159,0:30:21.720
no this is a patient and another

0:30:21.720,0:30:23.399
location it's not a number that I can do

0:30:23.399,0:30:25.740
mathematical operations on so I'm not

0:30:25.740,0:30:28.200
going to choose it so we will choose

0:30:28.200,0:30:30.539
everything as I said except for the

0:30:30.539,0:30:33.480
diabetic and the patient might I will

0:30:33.480,0:30:34.860
click on Save

0:30:34.860,0:30:37.740
and it's not showing me a warning again

0:30:37.740,0:30:39.480
everything is good

0:30:39.480,0:30:41.880
now I can click on submit

0:30:41.880,0:30:46.799
and review my normalization output

0:30:46.799,0:30:48.240
um

0:30:48.240,0:30:51.659
so uh if you click on submit here

0:30:51.659,0:30:54.659
and you will like choose create new and

0:30:54.659,0:30:56.460
set the name that is mentioned here

0:30:56.460,0:30:59.899
inside the notebook so it it tells you

0:30:59.899,0:31:03.419
to like create a job and name it name

0:31:03.419,0:31:05.460
the experiment Ms learn diabetes

0:31:05.460,0:31:06.720
training because you will continue

0:31:06.720,0:31:10.440
working on and building component later

0:31:10.440,0:31:13.020
I have it already like created I am the

0:31:13.020,0:31:16.919
like we can review it together so uh let

0:31:16.919,0:31:19.860
me just open this in another tab I think

0:31:19.860,0:31:21.000
I have it

0:31:21.000,0:31:23.659
here

0:31:25.679,0:31:28.220
okay

0:31:30.720,0:31:34.740
so these are all the jobs that I have

0:31:34.740,0:31:37.340
read them

0:31:37.860,0:31:39.899
all the jobs there let's do this over

0:31:39.899,0:31:42.059
these are all the jobs that I have

0:31:42.059,0:31:43.679
submitted previously

0:31:43.679,0:31:45.840
and I think this one is the

0:31:45.840,0:31:48.360
normalization job so let's see the

0:31:48.360,0:31:50.100
output of it

0:31:50.100,0:31:54.120
as you can see it says uh check mark yes

0:31:54.120,0:31:56.640
which means that it worked and we can

0:31:56.640,0:31:59.399
preview it how can I do that right click

0:31:59.399,0:32:02.539
on it choose preview data

0:32:02.539,0:32:06.659
and as you can see all the data are

0:32:06.659,0:32:08.399
scaled down

0:32:08.399,0:32:10.980
so everything is between zero

0:32:10.980,0:32:15.860
and uh one I think

0:32:15.860,0:32:18.899
so like everything is good for us now we

0:32:18.899,0:32:21.840
can move forward to the next step

0:32:21.840,0:32:27.799
which is to create the whole pipeline so

0:32:27.799,0:32:30.840
uh Carlota told you that

0:32:30.840,0:32:33.179
we're going to use a classification

0:32:33.179,0:32:37.260
model to create this data set so uh let

0:32:37.260,0:32:40.620
me just drag and drop everything

0:32:40.620,0:32:45.380
to get runtime and we're doing

0:32:45.799,0:32:50.419
about about everything by

0:32:51.419,0:32:52.919
so

0:32:52.919,0:32:57.380
as a result we are going to explain

0:32:59.760,0:33:03.600
yeah so I'm going to give this split

0:33:03.600,0:33:06.240
data I'm going to take the

0:33:06.240,0:33:08.880
transformation data to split data and

0:33:08.880,0:33:10.380
connect it like that

0:33:10.380,0:33:12.299
I'm going to get a three model

0:33:12.299,0:33:15.240
components because I want to train my

0:33:15.240,0:33:16.679
model

0:33:16.679,0:33:19.740
and I'm going to put it right here

0:33:19.740,0:33:21.740
okay

0:33:21.740,0:33:24.419
like let's just move it down there okay

0:33:24.419,0:33:27.059
and we are going to use a classification

0:33:27.059,0:33:28.620
model

0:33:28.620,0:33:31.880
a two class

0:33:32.240,0:33:35.399
logistic regression model

0:33:35.399,0:33:38.159
so I'm going to give this algorithm to

0:33:38.159,0:33:41.480
enable my model to work

0:33:41.820,0:33:45.960
this is the untrained model this is

0:33:45.960,0:33:48.059
here

0:33:48.059,0:33:51.120
the left the left

0:33:51.120,0:33:52.860
the left like Circle I'm going to

0:33:52.860,0:33:54.899
connect it to the data set and the right

0:33:54.899,0:33:56.940
one we are going to connect it to

0:33:56.940,0:33:59.700
evaluate model

0:33:59.700,0:34:02.640
evaluate model so let's search for

0:34:02.640,0:34:05.220
evaluate model here

0:34:05.220,0:34:07.440
so because we want to do what we want to

0:34:07.440,0:34:10.800
evaluate our model and see how it it has

0:34:10.800,0:34:14.580
been doing it is it good is it bad

0:34:14.580,0:34:18.200
um sorry like

0:34:19.980,0:34:22.820
this is

0:34:23.460,0:34:25.560
down there

0:34:25.560,0:34:28.139
after the school

0:34:28.139,0:34:31.320
so we have to get the score model first

0:34:31.320,0:34:33.960
so let's get it

0:34:33.960,0:34:36.119
and this will take the trained model and

0:34:36.119,0:34:37.260
the data set

0:34:37.260,0:34:39.419
to score our model and see if it's

0:34:39.419,0:34:42.179
performing good or bad

0:34:42.179,0:34:44.899
and

0:34:45.240,0:34:47.159
um

0:34:47.159,0:34:49.080
after that like we have finished

0:34:49.080,0:34:51.060
everything now we are going to do the

0:34:51.060,0:34:52.139
what

0:34:52.139,0:34:54.359
the presets for everything

0:34:54.359,0:34:56.820
as a starter we will be splitting our

0:34:56.820,0:34:58.920
data so

0:34:58.920,0:35:01.140
how are we going to do this according to

0:35:01.140,0:35:03.780
what to the split rules so I'm going to

0:35:03.780,0:35:05.940
double click on and choose split rows

0:35:05.940,0:35:09.420
and the percentage is

0:35:09.420,0:35:12.780
70 percent for the and 30 percent of the

0:35:12.780,0:35:14.820
data for

0:35:14.820,0:35:18.420
the valuation or for the scoring okay

0:35:18.420,0:35:20.880
I'm going to make it a randomization so

0:35:20.880,0:35:22.980
I'm going to split data randomly and the

0:35:22.980,0:35:26.060
seat is uh

0:35:26.060,0:35:29.339
132 23 I think yeah

0:35:29.339,0:35:32.520
and I think that's it

0:35:32.520,0:35:35.040
the split says why this holes and that's

0:35:35.040,0:35:36.240
good

0:35:36.240,0:35:39.540
now for the next one which is the train

0:35:39.540,0:35:42.000
model we are going to connect it as

0:35:42.000,0:35:43.500
mentioned here

0:35:43.500,0:35:48.660
and like we have done that and then why

0:35:48.660,0:35:50.700
am I having here like let's double click

0:35:50.700,0:35:54.660
on it yeah it has like it needs the

0:35:54.660,0:35:57.180
label column that I am trying to predict

0:35:57.180,0:35:58.680
so from here I'm going to choose

0:35:58.680,0:36:01.380
diabetic I'm going to save

0:36:01.380,0:36:05.180
I'm going to close this one

0:36:05.520,0:36:07.380
so it says here

0:36:07.380,0:36:10.619
the diabetic label the model it will

0:36:10.619,0:36:12.300
predict the zero and one because this is

0:36:12.300,0:36:14.700
a binary classification algorithm so

0:36:14.700,0:36:16.260
it's going to predict either this or

0:36:16.260,0:36:17.520
that

0:36:17.520,0:36:18.900
and

0:36:18.900,0:36:20.160
um

0:36:20.160,0:36:23.880
I think that's everything to run the the

0:36:23.880,0:36:25.859
pipeline

0:36:25.859,0:36:29.040
so everything is done everything is good

0:36:29.040,0:36:31.200
for this one we're just gonna leave it

0:36:31.200,0:36:34.140
like for now because this is the next

0:36:34.140,0:36:36.480
step

0:36:36.480,0:36:39.839
um this will like be put instead of the

0:36:39.839,0:36:43.520
score model but then it's

0:36:44.099,0:36:46.920
delete it for now

0:36:46.920,0:36:49.500
okay

0:36:49.500,0:36:52.920
now we have to submit the job in order

0:36:52.920,0:36:55.680
to see the output of it so I can click

0:36:55.680,0:36:59.280
on submit and choose the previous job

0:36:59.280,0:37:01.200
which is the one that I have showed you

0:37:01.200,0:37:02.460
before

0:37:02.460,0:37:05.460
and then let's review its output

0:37:05.460,0:37:06.960
together here

0:37:06.960,0:37:09.960
so if I go to the jobs

0:37:09.960,0:37:15.119
if I go to Ms learn maybe it is training

0:37:15.119,0:37:18.180
I think it's the one that lasted the

0:37:18.180,0:37:20.640
longest this one here

0:37:20.640,0:37:23.700
so here I can see

0:37:23.700,0:37:27.079
the job output what happened inside

0:37:27.079,0:37:30.420
the model as you can see

0:37:30.420,0:37:33.839
so the normalization we have like seen

0:37:33.839,0:37:36.540
before the split data I can preview it

0:37:36.540,0:37:39.359
the result one or the result two as it

0:37:39.359,0:37:41.760
splits the data to 70 here and three

0:37:41.760,0:37:44.339
thirty percent here

0:37:44.339,0:37:46.859
um I can see the score model which is

0:37:46.859,0:37:49.140
like something that we need

0:37:49.140,0:37:51.920
to review

0:37:52.380,0:37:56.820
um inside the scroll model uh like from

0:37:56.820,0:37:57.960
here

0:37:57.960,0:38:00.960
we can see that

0:38:00.960,0:38:04.460
let's get back here

0:38:05.940,0:38:08.220
like this is the data that the model has

0:38:08.220,0:38:11.579
been scored and this is a scoring output

0:38:11.579,0:38:15.300
so it says code label true and if he is

0:38:15.300,0:38:18.300
not diabetic so this is

0:38:18.300,0:38:19.200
um

0:38:19.200,0:38:21.839
around prediction let's say

0:38:21.839,0:38:23.880
for this one it's true and true and this

0:38:23.880,0:38:26.880
is like a good like what do you say

0:38:26.880,0:38:29.460
prediction and the probabilities of this

0:38:29.460,0:38:30.420
score

0:38:30.420,0:38:33.119
which means the certainty of our model

0:38:33.119,0:38:36.960
of that this is really true it's 80 for

0:38:36.960,0:38:38.780
this one is 75

0:38:38.780,0:38:42.599
so these are some cool metrics that we

0:38:42.599,0:38:45.359
can review to understand how our model

0:38:45.359,0:38:47.700
is performing it's performing good for

0:38:47.700,0:38:48.540
now

0:38:48.540,0:38:53.180
let's check our evaluation model

0:38:53.180,0:38:56.700
so this is the extra one that I told you

0:38:56.700,0:38:59.579
about instead of the like

0:38:59.579,0:39:01.800
score model only we are going to add

0:39:01.800,0:39:04.260
what evaluate model

0:39:04.260,0:39:06.900
after it so here

0:39:06.900,0:39:09.420
we're going to go to our asset library

0:39:09.420,0:39:12.180
and we are going to choose the evaluate

0:39:12.180,0:39:14.940
model

0:39:14.940,0:39:17.760
and we are going to put it here and we

0:39:17.760,0:39:20.220
are going to connect it and we are going

0:39:20.220,0:39:23.099
to submit the job using the same name of

0:39:23.099,0:39:25.140
the job that we used previously

0:39:25.140,0:39:29.520
let's review it uh also so after it

0:39:29.520,0:39:33.300
finishes you will find it here so I have

0:39:33.300,0:39:35.280
already done it before this is how I'm

0:39:35.280,0:39:37.380
able to see the output

0:39:37.380,0:39:40.320
so let's see

0:39:40.320,0:39:43.280
what what is the output of this

0:39:43.280,0:39:45.660
evaluation process

0:39:45.660,0:39:49.800
here it mentioned to you that there are

0:39:49.800,0:39:51.480
some metrics

0:39:51.480,0:39:54.839
like the confusion Matrix which Carlotta

0:39:54.839,0:39:57.060
told you about there is the accuracy the

0:39:57.060,0:39:59.760
Precision the recall and F1 School

0:39:59.760,0:40:02.339
every Matrix gives us some insight about

0:40:02.339,0:40:04.920
our model it helps us to understand it

0:40:04.920,0:40:08.579
more more and

0:40:08.579,0:40:10.560
like understand if it's overfitting if

0:40:10.560,0:40:12.240
it's good if it's bad and really really

0:40:12.240,0:40:16.339
like understand how it's working

0:40:17.060,0:40:20.400
now I'm just waiting for the job to load

0:40:20.400,0:40:22.920
until it loads

0:40:22.920,0:40:23.640
um

0:40:23.640,0:40:26.040
we can continue to

0:40:26.040,0:40:28.740
to work on our

0:40:28.740,0:40:31.800
model so I will go to my designer I'm

0:40:31.800,0:40:34.740
just going to confirm this

0:40:34.740,0:40:38.280
and I'm going to continue working on it

0:40:38.280,0:40:39.780
from

0:40:39.780,0:40:42.119
where we have stopped where have we

0:40:42.119,0:40:43.560
stopped

0:40:43.560,0:40:46.440
we have stopped on the evaluate model so

0:40:46.440,0:40:48.960
I'm going to choose this one

0:40:48.960,0:40:53.420
and it says here

0:40:54.180,0:40:56.940
select experiment create inference

0:40:56.940,0:40:58.200
pipeline so

0:40:58.200,0:41:01.079
I am going to go to the jobs

0:41:01.079,0:41:04.680
I'm going to select my experiment

0:41:04.680,0:41:06.660
I hope this works

0:41:06.660,0:41:09.720
okay salute finally now we have our

0:41:09.720,0:41:12.180
evaluate model output

0:41:12.180,0:41:15.480
let's previews evaluation results

0:41:15.480,0:41:18.660
and uh

0:41:18.660,0:41:22.220
cool come on

0:41:25.500,0:41:28.020
finally now we can create our inference

0:41:28.020,0:41:31.020
pipeline so

0:41:31.020,0:41:34.200
I think it says that

0:41:34.200,0:41:35.280
um

0:41:35.280,0:41:38.160
select the experiment then select Ms

0:41:38.160,0:41:39.359
learn so

0:41:39.359,0:41:43.320
I am just going to select it

0:41:43.320,0:41:48.300
and finally now we can the ROC curve we

0:41:48.300,0:41:51.000
can see it that the true positive rate

0:41:51.000,0:41:53.760
and the force was integrate the false

0:41:53.760,0:41:56.660
positive rate is increasing with time

0:41:56.660,0:42:01.020
and also the true positive rate true

0:42:01.020,0:42:03.540
positive is something that it predicted

0:42:03.540,0:42:06.960
that it is uh positive it has diabetes

0:42:06.960,0:42:09.480
and it's really a it's really true it

0:42:09.480,0:42:12.599
the person really has diabetes okay and

0:42:12.599,0:42:14.760
for the false positive it predicted that

0:42:14.760,0:42:17.579
someone has diabetes and someone doesn't

0:42:17.579,0:42:20.960
has it this is what true position and

0:42:20.960,0:42:24.960
false positive means this is The Recoil

0:42:24.960,0:42:28.020
curve so we can like review the metrics

0:42:28.020,0:42:32.160
of our model this is the lift curve I

0:42:32.160,0:42:36.000
can change the threshold of my confusion

0:42:36.000,0:42:37.740
Matrix here

0:42:37.740,0:42:39.119
and this could look don't want to add

0:42:39.119,0:42:43.920
anything about the the the graphs and

0:42:43.920,0:42:47.000
you can do so

0:42:50.460,0:42:51.000
um

0:42:51.000,0:42:54.720
yeah so just wanted to if you go yeah I

0:42:54.720,0:42:57.119
just wanted to comment comment for the

0:42:57.119,0:43:00.480
RSC curve uh that actually from this

0:43:00.480,0:43:03.900
graph the metric which uh usually we're

0:43:03.900,0:43:06.960
going to compute is the end area under

0:43:06.960,0:43:09.900
under the curve and this coefficient or

0:43:09.900,0:43:12.240
metric

0:43:12.240,0:43:15.060
um it's a confusion

0:43:15.060,0:43:18.420
um is a value that could span from from

0:43:18.420,0:43:22.920
zero to one and the the highest is

0:43:22.920,0:43:23.480
um

0:43:23.480,0:43:26.700
this the highest is the the score so the

0:43:26.700,0:43:29.220
the closest one

0:43:29.220,0:43:32.760
um so the the highest is the amount of

0:43:32.760,0:43:35.280
area under this curve

0:43:35.280,0:43:40.500
um the the the highest performance uh we

0:43:40.500,0:43:43.319
we've got from from our model and

0:43:43.319,0:43:46.440
another thing is what John is

0:43:46.440,0:43:49.680
um playing with so this threshold for

0:43:49.680,0:43:51.380
the logistic

0:43:51.380,0:43:55.920
regression is the threshold used by the

0:43:55.920,0:43:57.180
model

0:43:57.180,0:43:58.740
um to

0:43:58.740,0:43:59.520
um

0:43:59.520,0:44:02.940
to predict uh if the category is zero or

0:44:02.940,0:44:05.220
one so if the probability the

0:44:05.220,0:44:08.599
probability score is above the threshold

0:44:08.599,0:44:11.579
then the category will be predicted as

0:44:11.579,0:44:15.359
one while if the the probability is

0:44:15.359,0:44:17.460
below the threshold in this case for

0:44:17.460,0:44:21.300
example 0.5 the category is predicted as

0:44:21.300,0:44:23.579
as zero so that's why it's very

0:44:23.579,0:44:26.099
important to um to choose the the

0:44:26.099,0:44:27.839
threshold because the performance really

0:44:27.839,0:44:29.520
can vary

0:44:29.520,0:44:30.560
um

0:44:30.560,0:44:34.380
with this threshold value

0:44:34.380,0:44:41.099
uh thank you uh so much uh kellota and

0:44:41.400,0:44:44.400
as I mentioned now we are going to like

0:44:44.400,0:44:46.560
create our inference pipeline so we are

0:44:46.560,0:44:48.540
going to select the latest one which I

0:44:48.540,0:44:50.819
already have it opened here this is the

0:44:50.819,0:44:52.859
one that we were reviewing together this

0:44:52.859,0:44:55.500
is where we have stopped and we're going

0:44:55.500,0:44:57.599
to create an inference pipeline we are

0:44:57.599,0:44:59.520
going to choose a real-time inference

0:44:59.520,0:45:02.520
pipeline okay

0:45:02.520,0:45:05.160
um from where I can find this here as it

0:45:05.160,0:45:08.099
says real-time inference pipeline

0:45:08.099,0:45:10.680
so it's gonna add some things to my

0:45:10.680,0:45:12.420
workspace it's going to add the web

0:45:12.420,0:45:13.980
service inboard it's going to have the

0:45:13.980,0:45:15.780
web service output because we will be

0:45:15.780,0:45:18.180
creating it as a web service to access

0:45:18.180,0:45:19.740
it from the internet

0:45:19.740,0:45:21.900
uh what are we going to do we're going

0:45:21.900,0:45:24.720
to remove this diabetes data okay

0:45:24.720,0:45:27.540
and we are going to get a component

0:45:27.540,0:45:29.359
called Web

0:45:29.359,0:45:33.180
input and what's up let me check

0:45:33.180,0:45:35.940
it's enter data manually

0:45:35.940,0:45:38.400
we have we already have the with input

0:45:38.400,0:45:39.540
present

0:45:39.540,0:45:42.119
so we are going to get the entire data

0:45:42.119,0:45:43.200
manually

0:45:43.200,0:45:45.420
and we're going to collect it to connect

0:45:45.420,0:45:49.560
it as it was connected before like that

0:45:49.560,0:45:53.040
and also I am not going to directly take

0:45:53.040,0:45:55.260
the web service sorry escort model to

0:45:55.260,0:45:57.839
the web service output like that

0:45:57.839,0:46:00.240
I'm going to delete this

0:46:00.240,0:46:03.960
and I'm going to execute a python script

0:46:03.960,0:46:05.880
before

0:46:05.880,0:46:09.500
I display my result

0:46:10.680,0:46:12.060
so

0:46:12.060,0:46:17.480
this will be connected like okay but

0:46:19.260,0:46:20.400
so

0:46:20.400,0:46:23.599
the other way around

0:46:23.599,0:46:27.660
and from here I am going to connect this

0:46:27.660,0:46:30.960
with that and there is some data uh that

0:46:30.960,0:46:33.480
we will be getting from the node or from

0:46:33.480,0:46:37.680
the the examination here and this is the

0:46:37.680,0:46:40.740
data that will be entered like to our

0:46:40.740,0:46:44.400
website manually okay this is instead of

0:46:44.400,0:46:47.460
the data that we have been getting from

0:46:47.460,0:46:49.740
our data set that we created so I'm just

0:46:49.740,0:46:51.960
going to double click on it and choose

0:46:51.960,0:46:55.579
CSV and I will choose it has headers

0:46:55.579,0:47:00.839
and I will take or copy this content and

0:47:00.839,0:47:02.819
put it there okay

0:47:02.819,0:47:05.700
so let's do it

0:47:05.700,0:47:07.920
I think I have to click on edit code now

0:47:07.920,0:47:10.680
I can click on Save and I can close it

0:47:10.680,0:47:13.079
another thing which is the python script

0:47:13.079,0:47:16.700
that we will be executing

0:47:17.099,0:47:19.380
um yeah we are going to remove this also

0:47:19.380,0:47:21.140
we don't need the evaluate model anymore

0:47:21.140,0:47:24.319
so we are going to remove

0:47:24.319,0:47:28.579
script that I will be executing okay

0:47:28.579,0:47:32.599
I can find it here

0:47:33.540,0:47:34.619
um

0:47:34.619,0:47:35.760
yeah

0:47:35.760,0:47:38.640
this is the python script that we will

0:47:38.640,0:47:41.520
execute and it says to you that this

0:47:41.520,0:47:43.619
code selects only the patient's ID

0:47:43.619,0:47:45.000
that's correct label the school

0:47:45.000,0:47:47.700
probability and return returns them to

0:47:47.700,0:47:49.980
the web service output so we don't want

0:47:49.980,0:47:51.960
to return all the columns as we have

0:47:51.960,0:47:53.339
seen previously

0:47:53.339,0:47:55.560
uh the determines everything

0:47:55.560,0:47:56.940
so

0:47:56.940,0:47:59.040
we want to return certain stuff the

0:47:59.040,0:48:02.940
stuff that we will use inside our

0:48:02.940,0:48:05.640
endpoint so I'm just going to select

0:48:05.640,0:48:07.980
everything and delete it and

0:48:07.980,0:48:11.060
paste the code that I have gotten from

0:48:11.060,0:48:14.280
the uh

0:48:14.280,0:48:16.500
the Microsoft learn Docs

0:48:16.500,0:48:19.079
now I can click on Save and I can close

0:48:19.079,0:48:20.280
this

0:48:20.280,0:48:21.960
let me check something I don't think

0:48:21.960,0:48:25.020
it's saved it's saved but the display is

0:48:25.020,0:48:26.160
wrong okay

0:48:26.160,0:48:30.300
and now I think everything is good to go

0:48:30.300,0:48:32.640
I'm just gonna double check everything

0:48:32.640,0:48:36.359
so uh yeah we are gonna change the name

0:48:36.359,0:48:38.640
of this uh

0:48:38.640,0:48:40.800
Pipeline and we are gonna call it

0:48:40.800,0:48:42.780
predict

0:48:42.780,0:48:46.319
diabetes okay

0:48:46.319,0:48:50.339
now let's close it and

0:48:50.339,0:48:57.119
I think that we are good to go so

0:48:57.119,0:48:59.300
um

0:48:59.720,0:49:04.460
okay I think everything is good for us

0:49:06.420,0:49:08.339
I just want to make sure of something is

0:49:08.339,0:49:12.420
the data is correct the data is uh yeah

0:49:12.420,0:49:13.560
it's correct

0:49:13.560,0:49:16.319
okay now I can run the pipeline let's

0:49:16.319,0:49:17.640
submit

0:49:17.640,0:49:21.000
select an existing Pipeline and we're

0:49:21.000,0:49:22.740
going to choose the MS layer and

0:49:22.740,0:49:24.599
diabetes training which is the pipeline

0:49:24.599,0:49:27.060
that we have been working on

0:49:27.060,0:49:31.619
from the beginning of this module

0:49:31.680,0:49:33.839
I don't think that this is going to take

0:49:33.839,0:49:36.060
much time so we have submitted the job

0:49:36.060,0:49:37.319
and it's running

0:49:37.319,0:49:40.140
until the job ends we are going to set

0:49:40.140,0:49:41.720
everything

0:49:41.720,0:49:45.599
and for deploying a service

0:49:45.599,0:49:49.560
in order to deploy a service okay

0:49:49.560,0:49:50.520
um

0:49:50.520,0:49:54.000
I have to have the job ready so

0:49:54.000,0:49:56.040
until it's ready or you can deploy it so

0:49:56.040,0:49:58.319
let's go to the job the job details from

0:49:58.319,0:50:01.319
here okay

0:50:01.319,0:50:05.119
and until it finishes

0:50:05.119,0:50:07.260
Carlotta do you think that we can have

0:50:07.260,0:50:09.240
the questions and then we can get back

0:50:09.240,0:50:12.859
to the job I'm deploying it

0:50:13.700,0:50:17.579
yeah yeah yeah so yeah yeah guys if you

0:50:17.579,0:50:18.980
have any questions

0:50:18.980,0:50:24.119
uh on on what you just uh just saw here

0:50:24.119,0:50:26.940
or into introductions feel free this is

0:50:26.940,0:50:30.300
a good moment we can uh we can discuss

0:50:30.300,0:50:33.900
now while we wait for this job to to

0:50:33.900,0:50:36.260
finish

0:50:36.300,0:50:38.760
uh and the

0:50:38.760,0:50:40.220
can can

0:50:40.220,0:50:45.000
we have the energy check one or like

0:50:45.000,0:50:47.700
what do you think uh yeah we can also go

0:50:47.700,0:50:49.680
to the knowledge check

0:50:49.680,0:50:50.940
um

0:50:50.940,0:50:56.339
yeah okay so let me share my screen

0:50:56.339,0:50:58.980
yeah so if you have not any questions

0:50:58.980,0:51:01.619
for us we can maybe propose some

0:51:01.619,0:51:05.339
questions to to you that you can

0:51:05.339,0:51:06.240
um

0:51:06.240,0:51:09.660
uh to check our knowledge so far and you

0:51:09.660,0:51:12.900
can uh maybe answer to these questions

0:51:12.900,0:51:15.420
uh via chat

0:51:15.420,0:51:18.300
um so we have do you see my screen can

0:51:18.300,0:51:19.859
you see my screen

0:51:19.859,0:51:22.020
yes

0:51:22.020,0:51:25.440
um so John I think I will read this

0:51:25.440,0:51:29.040
question loud and ask it to you okay so

0:51:29.040,0:51:32.040
are you ready to transfer

0:51:32.040,0:51:33.660
yes I am

0:51:33.660,0:51:35.460
so

0:51:35.460,0:51:37.260
um you're using Azure machine learning

0:51:37.260,0:51:39.780
designer to create a training pipeline

0:51:39.780,0:51:42.540
for a binary classification model so

0:51:42.540,0:51:45.300
what what we were doing in our demo

0:51:45.300,0:51:48.059
right and you have added a data set

0:51:48.059,0:51:51.660
containing features and labels uh a true

0:51:51.660,0:51:54.359
class decision Forest module so we used

0:51:54.359,0:51:56.819
a logistic regression model our

0:51:56.819,0:51:59.099
um in our example here we're using A2

0:51:59.099,0:52:01.260
class decision force model

0:52:01.260,0:52:04.500
and of course a trained model model you

0:52:04.500,0:52:07.200
plan now to use score model and evaluate

0:52:07.200,0:52:09.480
model modules to test the train model

0:52:09.480,0:52:11.640
with the subset of the data set that

0:52:11.640,0:52:13.500
wasn't used for training

0:52:13.500,0:52:15.960
but what are we missing so what's

0:52:15.960,0:52:18.780
another model you should add and we have

0:52:18.780,0:52:21.660
three options we have join data we have

0:52:21.660,0:52:25.200
split data or we have select columns in

0:52:25.200,0:52:26.819
in that set

0:52:26.819,0:52:28.260
so

0:52:28.260,0:52:32.040
um while John thinks about the answer uh

0:52:32.040,0:52:33.839
go ahead and

0:52:33.839,0:52:34.800
um

0:52:34.800,0:52:37.800
answer yourself so give us your your

0:52:37.800,0:52:39.540
guess

0:52:39.540,0:52:41.940
put in the chat or just come off mute

0:52:41.940,0:52:44.900
and announcer

0:52:46.740,0:52:48.960
a b yes

0:52:48.960,0:52:50.579
yeah what do you think is the correct

0:52:50.579,0:52:53.579
answer for this one I need something to

0:52:53.579,0:52:56.579
uh like I have to score my model and I

0:52:56.579,0:53:00.359
have to evaluate it so I I like I need

0:53:00.359,0:53:03.119
something to enable me to do these two

0:53:03.119,0:53:05.359
things

0:53:06.660,0:53:09.119
I think it's something you showed us in

0:53:09.119,0:53:12.980
in your pipeline right John

0:53:13.260,0:53:16.819
of course I did

0:53:23.460,0:53:28.020
uh we have no guests yeah

0:53:28.020,0:53:32.280
can someone like someone want to guess

0:53:32.280,0:53:35.579
uh we have a b yeah

0:53:35.579,0:53:38.760
uh maybe

0:53:38.760,0:53:43.260
so uh in order to do this in order to do

0:53:43.260,0:53:46.200
this I mentioned the

0:53:46.200,0:53:49.380
the module that is going to help me to

0:53:49.380,0:53:53.819
to divide my data into two things 70 for

0:53:53.819,0:53:56.220
the training and thirty percent for the

0:53:56.220,0:53:59.339
evaluation so what did I use I used

0:53:59.339,0:54:01.859
split data because this is what is going

0:54:01.859,0:54:05.280
to split my data randomly into training

0:54:05.280,0:54:08.579
data and validation data so the correct

0:54:08.579,0:54:12.240
answer is B and good job eek thank you

0:54:12.240,0:54:13.980
for participating

0:54:13.980,0:54:17.400
next question please

0:54:17.400,0:54:19.339
yes

0:54:19.339,0:54:22.559
answer so thanks John

0:54:22.559,0:54:26.040
uh for uh explaining us the the correct

0:54:26.040,0:54:26.940
one

0:54:26.940,0:54:30.420
and we want to go with question two

0:54:30.420,0:54:33.180
yeah so uh I'm going to ask you now

0:54:33.180,0:54:35.880
karnata you use Azure machine learning

0:54:35.880,0:54:38.280
designer to create a training pipeline

0:54:38.280,0:54:40.500
for your classification model

0:54:40.500,0:54:44.099
what must you do before you deploy this

0:54:44.099,0:54:45.960
model as a service you have to do

0:54:45.960,0:54:47.579
something before you deploy it what do

0:54:47.579,0:54:49.740
you think is the correct answer

0:54:49.740,0:54:52.740
is it a b or c

0:54:52.740,0:54:55.020
share your thoughts without in touch

0:54:55.020,0:54:58.380
with us in the chat and

0:54:58.380,0:55:00.180
um and I'm also going to give you some

0:55:00.180,0:55:02.940
like minutes to think of it before I

0:55:02.940,0:55:06.020
like tell you about

0:55:06.599,0:55:09.000
yeah so let me go through the possible

0:55:09.000,0:55:12.359
answers right so we have a uh create an

0:55:12.359,0:55:14.940
inference pipeline from the training

0:55:14.940,0:55:16.020
pipeline

0:55:16.020,0:55:19.260
uh B we have ADD and evaluate model

0:55:19.260,0:55:22.380
module to the training Pipeline and then

0:55:22.380,0:55:25.079
three we have uh clone the training

0:55:25.079,0:55:29.480
Pipeline with a different name

0:55:29.520,0:55:31.559
so what do you think is the correct

0:55:31.559,0:55:33.960
answer a b or c

0:55:33.960,0:55:36.660
uh also this time I think it's something

0:55:36.660,0:55:39.300
we mentioned both in the decks and in

0:55:39.300,0:55:41.960
the demo right

0:55:42.599,0:55:44.819
yes it is

0:55:44.819,0:55:48.720
it's something that I have done like two

0:55:48.720,0:55:51.800
like five minutes ago

0:55:51.800,0:55:57.200
it's real time real time what's

0:55:58.020,0:55:58.760
um

0:55:58.760,0:56:02.040
yeah so think about you need to deploy

0:56:02.040,0:56:05.460
uh the model as a service so uh if I'm

0:56:05.460,0:56:07.980
going to deploy model

0:56:07.980,0:56:10.380
um I cannot like evaluate the model

0:56:10.380,0:56:12.839
after deploying it right because I

0:56:12.839,0:56:14.940
cannot go into production if I'm not

0:56:14.940,0:56:17.579
sure I'm not satisfied over my model and

0:56:17.579,0:56:19.500
I'm not sure that my model is performing

0:56:19.500,0:56:20.280
well

0:56:20.280,0:56:23.460
so that's why I would go with

0:56:23.460,0:56:24.319
um

0:56:24.319,0:56:30.480
I would like exclude B from from my from

0:56:30.480,0:56:31.520
my answer

0:56:31.520,0:56:33.599
uh while

0:56:33.599,0:56:36.960
um thinking about C uh I don't see you I

0:56:36.960,0:56:39.480
didn't see you John cloning uh the

0:56:39.480,0:56:41.420
training Pipeline with a different name

0:56:41.420,0:56:44.640
uh so I I don't think this is the the

0:56:44.640,0:56:46.920
right answer

0:56:46.920,0:56:49.619
um while I've seen you creating an

0:56:49.619,0:56:52.859
inference pipeline uh yeah from the

0:56:52.859,0:56:55.020
training Pipeline and you just converted

0:56:55.020,0:56:59.280
it using uh a one-click button right

0:56:59.280,0:57:03.300
yeah that's correct so uh this is the

0:57:03.300,0:57:04.280
right answer

0:57:04.280,0:57:07.460
uh good job so I created an inference

0:57:07.460,0:57:11.280
real-time Pipeline and it has done it

0:57:11.280,0:57:13.440
like it finished it finished the job is

0:57:13.440,0:57:18.000
finished so uh we can now deploy

0:57:18.000,0:57:19.400
ment

0:57:19.400,0:57:21.500
yeah

0:57:21.500,0:57:25.339
exactly like on time

0:57:25.380,0:57:27.839
I like it finished like two seconds

0:57:27.839,0:57:30.859
three three four seconds ago

0:57:30.859,0:57:33.119
so uh

0:57:33.119,0:57:36.480
until like um

0:57:36.480,0:57:39.839
this is my job review so

0:57:39.839,0:57:43.260
uh like this is the job details that I

0:57:43.260,0:57:45.540
have already submitted it's just opening

0:57:45.540,0:57:48.119
and once it opens

0:57:48.119,0:57:50.180
um

0:57:50.400,0:57:52.740
like I don't know why it's so heavy

0:57:52.740,0:57:56.780
today it's not like that usually

0:57:58.740,0:58:01.020
yeah it's probably because you are also

0:58:01.020,0:58:06.000
showing your your screen on teams

0:58:06.000,0:58:08.160
okay so that's the bandwidth of your

0:58:08.160,0:58:10.740
connection is exactly do something here

0:58:10.740,0:58:13.740
because yeah finally

0:58:13.740,0:58:16.440
I can switch to my mobile internet if it

0:58:16.440,0:58:18.599
did it again so I will click on deploy

0:58:18.599,0:58:20.700
it's that simple I'll just click on

0:58:20.700,0:58:23.040
deploy and

0:58:23.040,0:58:25.619
I am going to deploy a new real-time

0:58:25.619,0:58:27.960
endpoint

0:58:27.960,0:58:30.300
so what I'm going to name it I'm

0:58:30.300,0:58:31.740
description and the compute type

0:58:31.740,0:58:33.720
everything is already mentioned for me

0:58:33.720,0:58:36.240
here so I'm just gonna copy and paste it

0:58:36.240,0:58:38.940
because we like we have we are running

0:58:38.940,0:58:41.280
out of time

0:58:41.280,0:58:45.680
so it's all Azure container instance

0:58:45.680,0:58:48.720
which is a containerization service also

0:58:48.720,0:58:51.059
both are for containerization but this

0:58:51.059,0:58:52.440
gives you something and this gives you

0:58:52.440,0:58:54.960
something else for the advanced options

0:58:54.960,0:58:57.420
it doesn't say for us to do anything so

0:58:57.420,0:59:00.420
we are just gonna click on deploy

0:59:00.420,0:59:05.220
and now we can test our endpoint from

0:59:05.220,0:59:07.859
the endpoints that we can find here so

0:59:07.859,0:59:11.460
it's in progress if I go here

0:59:11.460,0:59:13.799
under the assets I can find something

0:59:13.799,0:59:16.680
called endpoints and I can find the

0:59:16.680,0:59:18.599
real-time ones and the batch endpoints

0:59:18.599,0:59:22.020
and we have created a real-time endpoint

0:59:22.020,0:59:25.260
so we are going to find it under this uh

0:59:25.260,0:59:29.760
title so if I like click on it I should

0:59:29.760,0:59:32.640
be able to test it once it's ready

0:59:32.640,0:59:37.200
it's still like loading but this is the

0:59:37.200,0:59:40.980
input and this is the output that we

0:59:40.980,0:59:45.200
will get back so if I click on test and

0:59:45.200,0:59:49.920
from here I will input some data to the

0:59:49.920,0:59:50.900
endpoint

0:59:50.900,0:59:54.599
which are the patient information The

0:59:54.599,0:59:57.119
Columns that we have already seen in our

0:59:57.119,1:00:00.380
data set the patient ID the pregnancies

1:00:00.380,1:00:03.960
and of course of course I'm not gonna

1:00:03.960,1:00:05.940
enter the label that I'm trying to

1:00:05.940,1:00:08.099
predict so I'm not going to give him if

1:00:08.099,1:00:10.680
the patient is diabetic or not this end

1:00:10.680,1:00:13.200
point is to tell me this is the end

1:00:13.200,1:00:15.599
point or the URL is going to give me

1:00:15.599,1:00:17.640
back this information whether someone

1:00:17.640,1:00:22.680
has diabetes or he doesn't so if I input

1:00:22.680,1:00:24.780
these this data I'm just going to copy

1:00:24.780,1:00:27.780
it and go to my endpoint and click on

1:00:27.780,1:00:30.180
test I'm gonna give the result pack

1:00:30.180,1:00:32.359
which are the three columns that we have

1:00:32.359,1:00:35.520
defined inside our python script the

1:00:35.520,1:00:37.859
patient ID the diabetic prediction and

1:00:37.859,1:00:41.040
the probability the certainty of whether

1:00:41.040,1:00:45.720
someone is diabetic or not based on the

1:00:45.720,1:00:50.660
uh based on the prediction so that's it

1:00:50.660,1:00:54.359
and like uh I think that this is really

1:00:54.359,1:00:56.819
simple step to do you can do it on your

1:00:56.819,1:00:58.380
own you can test it

1:00:58.380,1:01:01.140
and I think that I have finished so

1:01:01.140,1:01:03.020
thank you

1:01:03.020,1:01:06.599
uh yes we are running out of time I I

1:01:06.599,1:01:09.780
just wanted to uh thank you John for for

1:01:09.780,1:01:12.299
this demo for going through all these

1:01:12.299,1:01:14.099
steps to

1:01:14.099,1:01:16.740
um create train a classification model

1:01:16.740,1:01:19.680
and also deploy it as a predictive

1:01:19.680,1:01:23.040
service and I encourage you all to go

1:01:23.040,1:01:25.079
back to the learn module

1:01:25.079,1:01:28.260
um and uh like depend all these topics

1:01:28.260,1:01:31.760
at your at your own pace and also maybe

1:01:31.760,1:01:34.799
uh do this demo on your own on your

1:01:34.799,1:01:37.140
subscription on your Azure for student

1:01:37.140,1:01:39.359
subscription

1:01:39.359,1:01:43.200
um and I would also like to recall that

1:01:43.200,1:01:46.260
this is part of a series of study

1:01:46.260,1:01:49.500
sessions of cloud skill challenge study

1:01:49.500,1:01:51.059
sessions

1:01:51.059,1:01:54.059
um so you will have more in the in the

1:01:54.059,1:01:57.540
in the following days and this is for

1:01:57.540,1:02:00.480
you to prepare let's say to to help you

1:02:00.480,1:02:04.880
in taking the a cloud skills challenge

1:02:04.880,1:02:07.040
which collect

1:02:07.040,1:02:10.799
a very interesting learn module that you

1:02:10.799,1:02:14.540
can use to scale up on various topics

1:02:14.540,1:02:18.359
and some of them are focused on AI and

1:02:18.359,1:02:20.819
ml so if you are interested in these

1:02:20.819,1:02:23.099
topics you can select these these learn

1:02:23.099,1:02:24.780
modules

1:02:24.780,1:02:27.660
um so let me also copy

1:02:27.660,1:02:29.819
um the link the short link to the

1:02:29.819,1:02:32.700
challenge in the chat uh remember that

1:02:32.700,1:02:34.980
you have time until the 13th of

1:02:34.980,1:02:37.980
September to take the challenge and also

1:02:37.980,1:02:40.440
remember that in October on the 7th of

1:02:40.440,1:02:43.020
October you have the you can join the

1:02:43.020,1:02:46.619
student the the student developer Summit

1:02:46.619,1:02:50.640
which is uh which will be a virtual or

1:02:50.640,1:02:53.220
in for some for some cases and hybrid

1:02:53.220,1:02:56.040
event so stay tuned because you will

1:02:56.040,1:02:58.559
have some surprises in the following

1:02:58.559,1:03:01.260
days and if you want to learn more about

1:03:01.260,1:03:03.480
this event you can check the Microsoft

1:03:03.480,1:03:08.099
Imaging cap Twitter page and stay tuned

1:03:08.099,1:03:11.460
so thank you everyone for uh for joining

1:03:11.460,1:03:13.079
this session today and thank you very

1:03:13.079,1:03:16.500
much Sean for co-hosting with with this

1:03:16.500,1:03:20.359
session with me it was a pleasure

1:03:21.839,1:03:24.119
thank you so much Carlotta for having me

1:03:24.119,1:03:26.579
with you today and thank you like for

1:03:26.579,1:03:28.079
giving me this opportunity to be with

1:03:28.079,1:03:30.180
you here

1:03:30.180,1:03:33.480
great I hope that uh yeah I hope that we

1:03:33.480,1:03:36.480
work again in the future sure I I hope

1:03:36.480,1:03:38.160
so as well

1:03:38.160,1:03:40.760
um so

1:03:44.099,1:03:46.500
bye bye speak to you soon

1:03:46.500,1:03:48.920
bye