Azure ML workbench- Installation-Part 1

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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

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after installing I saw below page

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

so I open the “https://portal.azure.com”.

then I start to create a “Machine Learning Experimentation”

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then I need to set up the parameters as below:

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

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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”

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

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

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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

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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″

 

 

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

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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.

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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.

deploy

Also, for this practice, I am going to run “iris_sklearn.py”. 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.

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

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Then just write “az login” it will ask you to go to the “http://aka.ms/devicelogin” and provides a code that you should put there.

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

 

 

 

 

 

 

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

 

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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.

 

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

https://docs.microsoft.com/en-us/azure/machine-learning/preview/quickstart-installation

 

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Leila Etaati
Dr. Leila Etaati is Principal Data Scientist, BI Consultant, and Speaker. She has over 10 years’ experience working with databases and software systems. She was involved in many large-scale projects for big sized companies. Leila has PhD of Information System department, University of Auckland, MS and BS in computer science. Leila is Microsoft Data Platform MVP.

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