Significant and need for Machine Learning in Cloud Computing- TutorsIndia.com

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SIGNIFICANT AND NEED OF MACHINE LEARNING IN
CLOUD COMPUTING
An Academic presentation by Dr. Nancy Agens,
Head, Technical Operations, Tutors India
Group www.tutorsindia.com Email
info_at_tutorsindia.com
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Today's Discussion
OUTLINE
In Brief Background Five Stages of ML
Recommendation
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In Brief
There is a need for an effective model to secure
the data in both the trusted and untrusted cloud
environment. The encryption is the process for
enhancing the secure level of data while before
upload to the trusted or untrusted cloud system.
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Cloud computing plays a major role in most of the
organizations
to outsource their information as well as for
system computational needs.
Such administrations are relied upon to
consistently give security standards.
Background
For example, data availability, confidentiality
and integrity in this way, an exceptionally
secure stage is one of the most significant
parts of Cloud-environment. In order to tackle
the issues of malware detection and
classification, machine learning (ML) plays a
significant role.
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Five Stages of ML

• ML technique comprises five stages of workflow
  namely,
• data gathering,
• preprocessing (cleaning and preparing of
  information),
• unique model building process,
• deploying and validating model into production.
• The information arrangement procedure of
  conventional ML approaches includes
  preprocessing the executable to separate a lot of
  features that gives a conceptual perspective on
  the product.
• Contd..

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Fig 1. Machine Learning Workflow
Contd..
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In order to solve the task scheduling, the
extracted features are imported into train a
model. An ML technique has been widely applied
to various applications but due to the rapid
growth of data over the cloud environment there
is a possibility to occur risk during quality
measurement and distribution of data over the
untrusted cloud.
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Recommendations
Quality Risks and Disruption The traditional
method has been a failure to consider some
primary risk, inefficiencies and operational
impacts at the time of user adoption and training
denotes secondary risk. The combination of both
primary and secondary risk factors help to
support smart technology which will enhance the
system performance. Emerging Technologies and
Methods The deep learning will be an effective
model for both classification and detection which
also effectively extracts the features via
in-depth analysis of data.
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