Introduction to Azure ML Studio | Essay

Introduction to Azure ML Studio
Azure Machine Learning Studio (Azure ML Studio)
facilitates you to form, check and to create predictive
analytical results for your data. This is a drag and drop
tool which could publish models such as web services by
consuming custom apps or business intelligence tools
such as Excel.
Azure ML Studio provides you an interactive, visual
workspace where your drag and drop data sets and
analysis are converted to an interactive canvas. This is a
platform for operating machine learning workloads in
the cloud. The key objective of this article is to give a
brief introduction on Azure ML Studio and to make the
reader educated on data uploading procedure through
importing data in Azure ML Studio workspace.
Background of Microsoft Azure ML Studio
Microsoft Azure offers the service as a cloud computing
service for the fields of building, testing, deploying and
managing its applications and facilities from the
Microsoft managed data centers. Software, platform and
infrastructure are provided as services by Microsoft
Azure, supporting numerous programming languages,
tools and frameworks, which encloses both Microsoft
and third party software and systems.
“Project Red Dog” was the code name when Microsoft
Azure announced its name in October 2008, and was
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released on February 1, 2010. Moreover, on the March
25, 2011, Windows Azure retitled to as Microsoft Azure.
More than 600 Azure services are provided by Microsoft
which comprises Computer services, Mobile services,
Storage services, Data Management services, Messaging
services, Media services, Content Delivery Network
services, Azure Block Chain Work Bench services, Azure
Function services, etc. The Microsoft Azure Machine
Learning service is a part of the Cortana Intelligence
Suite which assists you predictive analytics and
interactions through data using speech and language
through Cortana. Furthermore, on July, 2014, Microsoft
introduced Azure machine Learning public preview for
the users.
What is Machine Learning?
Let us initially get an idea on ‘What is Machine Learning
and its History’, this is the scientific study of algorithms
and statistical models that are used by computer systems
to perform a specific activity relying only patterns and
interface. Sample data known as ‘training data’ are
utilized to build mathematical models based on machine
learning algorithms to make decisions and predictions.
This study is closely associated to computational
statistics which focuses on creating predictions through
computers.
History of Machine Learning
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In 1959, Arthur Samuel an American innovator in the
fields of Artificial Intelligence and Computer Gaming
created the term ‘Machine Learning’. In the 1970’s,
interest of Machine Learning related to pattern
recognition was continued and in 1990’s Machine
Learning was recognized as a separate field from
Artificial Intelligence (AI) where its objectives were
changed, so as to tackle solvable problems in a practical
nature. This condition arise since the emphasis on the
logical, knowledge based approach caused a gap in
Machine learning and AI. So that eventually, the
symbolic approaches which it had inherited from AI were
shifted to methods and models which were carried
forward from probability theory and statistics.
Use of Machine Learning Studio (ML Studio)
Let us now understand the uses of ML Studio in the field.
When deliberating the usages, these are some of the
benefits for the consumers.
 To develop a predictive analysis model using data from
one or more sources.
 Transforms and analyze data by statistical functions
where data manipulation is conducted to produce set
of results.
Note: This type of model developing is an iterative process.
Modifications for the parameters will change the results until
the user is satisfied with an effective, trained model.
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 Provides a cooperative, visual workspace which is user
friendly on a predictive analysis model.
 Data sets can be drag and dropped where the analysis
modules on to an interactive canvas, by connecting
them together and forming an experiment and run in
ML studio.
 Iteration, editing, saving can be done to the users’
model design.
Note: Before iterating the model design, a copy should be
saved and run again.
 Publishing of a predictive experiment as a web service
so that the users’ model can be accessed by others.
 Programming is not required in the process.
 Predictive analysis are done by visually connecting
data sets and modules.
What are the Applications of Machine Learning?
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