Automated Machine Learning: Data Profiling in Azure ML Services – Part 4

In the last 3 posts ( introduction to Azure ML Service, AutoML Environment and how to deploy the models), some demonstration on how we able to access Automated Machine learning and how to deploy a model has been discussed.

In this short post, I am going to show a section that helps you to understand your data better, name Data Profiling.

How to Access Data Profiler

how we can access data profiler before applying Automated Machine learning?

Let’s review how we access the AutoML,

1- Login to Azure portal ( if you do not have a subscription you  can get a free one)

2- create Azure ML service workspace or use the one you have

3- The in the left panel, click on the Automated machine learning 


4- you need to create a new Create Experiment

5- You need to put a name for the experiment name for the AutoML

Now you be able to access the Data Profiling you need to create specific Compute

6- You need to click on the New Compute at the bottom of the page

to do that you need to create specific Compute, I name it aiauthomlcompute

Then, click on the additional settings, to have access to the data profile, the “Minimum Number Nodes” should be at least One

Next, you need to put a name for Compute and create it.

Right after that, you able to see a profit appear as (Profiling enables)

that means, the Compute you will run the model on it, now support data profiling.

Now, just upload a new dataset, or used the one you already have.

In this demo, I am using the Titanic dataset, I am using the Titanic dataset, that I have used before for AutoML.

after loading the dataset, you will see the Profile,

the option is active

Note: if you do not specify the number of the minimum number of nodes, you not able to see this option 

In this stage, click on the Profile table, it will take a couple of minutes to load the profile.

After a while, you able to see the data profiling available for all columns of data.

Data Profile

As you can see in the below picture, for the age column,

1- There is a histogram that shows the data distribution of age

2- There is another column, that shows the adat type of the dataset.

3- in other columns, you able to see some information such as Minimum, Maximum, Count of data, Number of Missing values



4- Also, you able to see some information such as mean, median, variance, standard deviation and so forth.


In the histogram chart for the age of people, you able to see the age range and other information.


Data profiling helps data scientist to have some understanding of the data.



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Leila Etaati
Trainer, Consultant, Mentor
Leila is the first Microsoft AI MVP in New Zealand and Australia, She has Ph.D. in Information System from the University Of Auckland. She is the Co-director and data scientist in RADACAD Company with more than 100 clients in around the world. She is the co-organizer of Microsoft Business Intelligence and Power BI Use group (meetup) in Auckland with more than 1200 members, She is the co-organizer of three main conferences in Auckland: SQL Saturday Auckland (2015 till now) with more than 400 registrations, Difinity (2017 till now) with more than 200 registrations and Global AI Bootcamp 2018. She is a Data Scientist, BI Consultant, Trainer, and Speaker. She is a well-known International Speakers to many conferences such as Microsoft ignite, SQL pass, Data Platform Summit, SQL Saturday, Power BI world Tour and so forth in Europe, USA, Asia, Australia, and New Zealand. She has over ten years’ experience working with databases and software systems. She was involved in many large-scale projects for big-sized companies. She also AI and Data Platform Microsoft MVP. Leila is an active Technical Microsoft AI blogger for RADACAD.

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