Advantage and Usage 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: [email protected]
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The enormous amount of information generated in a day to day life due to the technological innovations and developments in wide areas like education, government, social media, business, healthcare, finance, etc. Thus the huge amount of generated data creates potential toward discerning useful knowledge from it. The cloud computing (CC) plays a major role to address both usages of data storage and computational of huge data, especially for mining and knowledge discovery applications (Talia, 2015). But, also it requires dealing with the process of data in an efficient and cost-effective manner (as low).
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Transcript of Advantage and Usage for Machine learning in Cloud Computing- TutorsIndia.com
SIGNIFICANT AND NEED OF
MACHINE LEARNING IN
CLOUD COMPUTING
An Academic presentation by
Dr. Nancy Agens, Head, Technical Operations, Tutors India Group: www.tutorsindia.comEmail: [email protected]
In Brief
Background
Five Stages of ML
Recommendation
OUTLINE
Today's Discussion
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.
Background
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.
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.
ML technique comprises five stages of workflow namely,
1.data gathering,
2.preprocessing (cleaning and preparing of information),
3.unique model building process,
4.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..
Five Stages of ML
Fig 1. Machine
Learning Workflow
Contd..
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.
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.
Recommendations
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