Azure Machine Learning Services : Introduction – Part 1

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In this post series, I am going to show how we can use Azure Machine learning services and the new features added that make life so easy to train, deploy, automate managing machine learning models [1]. In this post, first I will show how to use a no code environment for Auto ML, how to access it and some difference between Azure mL Studio and services.

 

 You are able to use Automated machine learning process to identify the best algorithms and best set of parameters. that make the process of machine learning faster

 

Reduce cost and enhance productivity by providing DevOps for machine learning.

 

Access to all open source framework such as Pytorch, Tensorflow, and Scikit-learn [2]

 

 

Azure ML Service VS Azure ML Studio

        VS    

Before going ahead, there is a question of what is the difference between Azure Machine learning Studio and Services

 

Azure ML Studio

is a drag and drops environment and No Coding required. There are some Pre Build Algorithms and data transformation tools.

When to use it: experiment ML models easily, built-in algorithms able to address your issues

 

Azure ML Services

it is a coding environment, you can access to most important open source machine learning frameworks such as Pytorch, Tensorflow and so forth. It is an environment for Python coding, and you have control over your ML algorithms or any free libraries.

in this and next post I will start with below topics

1- How to set up the environment

2- Image classification with MNIST Database

3- Regression model

4- Automated Machine learning

5-Model management

6- Machine learning Pipelines

7- Create and Consume Webservice in Power BI Services and Desktop

8- and many more discussions

 

Access to Azure Machine Learning Services

 

The first step is to create a workspace,

what is workspace: it is the main place that we put all the resources we created there.

I will create a workspace in Azure Portal to do that, you need to have a valid  account for Azure if you do not have, follow the below link

https://azure.microsoft.com/en-us/free/services/machine-learning/

 

you will have access to a 12-month free account

if you already have an Azure Portal, then you need to create Machine Learning Services Workspace

 

Next, after creating the machine learning services, you will see a component has been created. As you can see in the below picture

Number 2- we have the main workspace to explore the Azure ML Services

Number 3– Access to Azure ML Service forum

Number 4- some free samples posted in Github

Number 5- we access to some documentation

Number 6-Build a model using the Visual Interface (preview)

Number 7- Create Notebooks in Azure ML Notebook VM (Preview)

Number 8-  Create a new Automated Machine Learning Model (Preview)

 

in this scenario,  click on the Create a New Automated Machine Learning Model (Preview).

This is AutoML, what is mean it just get the data, the type of the problem you want to solve and configure where you want to compute then deploy it and you able to consume it in other applications.

Now Click on the Create a New Automated Machine Learning Model (Preview).

Once you created,  the main overview of the completes, running, failed, and other Aautoml practice will be shown.  As you can see in the above picture, some information about what day they run, the number of the completed model and so forth have been shown. Also you able to see five different pipelines in the Assets

 

for the next post,  I will show how to create an Experiment and see the run it for the Titanic example!

 

[1]. https://docs.microsoft.com/en-us/azure/machine-learning/service/overview-what-is-azure-ml 

[2] https://azure.microsoft.com/en-us/services/machine-learning-service/

[3]  https://towardsdatascience.com/achieving-a-top-5-position-in-an-ml-competition-with-automl-89a5a6fb8060 

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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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