Azure ML workbench- Installation-Part 1


In Microsoft ignite 2017, Azure ML team announce new on-premises tools for doing machine learning. this tools much more comprehensive as it provides

1- a workspace helps data wrangling

2- Data Visualization

3-Easy to deploy

4-Support Python codes

in this post and next posts, I will share my experiment with working this tools.

first I got excited right away download it from

Download Here



after installing I saw below page


So, I have to go through the “Azure Portal” to create an “Experimentation Account”.

so I open the “”.

then I start to create a “Machine Learning Experimentation”


then I need to set up the parameters as below:


however after creation ML experiment you able to add new ML model.



as you see in above picture, we able to download “Azure ML workbench” from there, or we using Mac OS we also able to download it. Moreover, you able to create an account for machine learning model. in below the picture, I created “Machine Learning Model Managment”


then, you should have both in your Azure portal as below:



Now I am able to open the “Azure ML workbench”


So as you see in above there are no Projects, I have to create one. As it is my first time to work with this tool, I am going to try one of the sample projects there.  Under example panel, search for iris classification


So, I am going to create something new a below:

just click on the plus sign (number 1), then click on the New Project “number 2”





So, just put a name for the project name, project directory, and then simply create it.


Now, you should see below picture,

this project is a dataset about different types of flowers with their petal and sepal length. this sample is going to run a prediction a group (classification) to predict if we have specific sepal or petal length these flowers belong to which type of flowers.


so for the first process, is to run this project. in this video we able to specify where to deploy the project to the local PC, docker-spark, and docker-python.


Also, for this practice, I am going to run “”. however, before running the project you need to set up some configuration in command prompt


setting in the files choose the “open command prompt” to be able to install python and run the code.



Then, in the file write “pip install matplotlib”, you should see below results after running the code.


Then just write “az login” it will ask you to go to the “” and provides a code that you should put there.





right after you log in you should see the below message in command prompt.









Now, it is time to just run the code, just now run the code as below




You should see a green sign on the right side of the window. Moreover, in middle page, you will see some more explanation of model run.




in the next posts, I will explain how to explore the results, how to clean data and do the data visualization.


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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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