Role of Machine learning (ML) techniques in IoT - Phdassistance.com
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ROLE OF MACHINE LEARNING (ML) TECHNIQUES IN IOT An Academic presentation by Dr. Nancy Agens, Head, Technical Operations, Phdassistance Group www.phdassistance.com Email: [email protected]
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The volume of data generated by IoT devices is tempting more and more companies to jump into the bandwagon. But the idea is still in infancy as companies need to figure out how to put the data to good use. Companies are exploring ways of moving IoT strategies from proof-of-concept to deployment. Since the social consequences of IoT have not been fully understood it is crucial for corporates to implement IoT strategies systematically. Machine Learning is indispensable while trying to incorporate IoT strategies.
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Transcript of Role of Machine learning (ML) techniques in IoT - Phdassistance.com
ROLE OF MACHINE LEARNING (ML) TECHNIQUES IN IOTAn Academic presentation byDr. Nancy Agens, Head, Technical Operations, Phdassistance Group www.phdassistance.comEmail: [email protected]
Introduction
Reinforcement Learning
Internet of Things (IOT)
Metadata
Conclusion
Outline
TODAY'S DISCUSSION
According to market research firm, there will be 7.6 billion internet connected devices in businesses by 2020.
Companies are exploring ways of moving IoT strategies from proof-of- concept to deployment.
Since the social consequences of IoT have not been fully understood it is crucial for corporates to implement IoT strategies systematically.
Machine Learning is indispensable while trying to incorporate IoT strategies.
Introduction
Machine Learning (ML) algorithms is applied to the different variables scenario which get trained and able to predict outcomes with increasing accuracy.
Machine Learning models interact with their environment and learn from the outcomes in what is known as reinforcement learning.
The importance of IoT and ML can be understood from the fact that Uber would rather be seen as a technology company than a transportation company.
It’s app connects the passenger looking for a ride and the driver nearby who is willing to take it.
Reinforcement Learning
Industrial IoT, though tipped for a big leap has not translated into good return on investment for companies.
Also, most companies are still in the exploratory phase and do not possess the in- house capabilities needed to implement such strategies.
IoT has enabled optimal utilization of energy in malls by collecting data on weather, energy consumption and energy prices and then calculating the most cost-effective solution to use energy generated in the mall.
Smart cities use data collected on air quality and weather to offer solutions in real- time such as offering cheap public transport in places predicted for traffic congestion.
Internet of Things (IOT)
According to IDC, video surveillance will account for the largest share of IoT datawith industrial and automotive category seeing the fastest data growth rates.
Many firms are still sitting on huge volumes of data unable to turn them intoactionable Information.
IoT metadata is also a growing segment.
Metadata is data that is created based on other IoT data files.
They can be used for Developing Intelligent systems, drive personalization or bring context to random scenarios.
Metadata
https://www.phdassistance.com/blog/recent-challenges-in-artificial-intelligence-adversarial-attacks/
https://www.phdassistance.com/blog/recent-challenges-in-artificial-intelligence-adversarial-attacks/
https://www.phdassistance.com/blog/recent-challenges-in-artificial-intelligence-adversarial-attacks/
https://www.phdassistance.com/blog/recent-challenges-in-artificial-intelligence-adversarial-attacks/
In the long run, organizations must be prepared to manage change in order to incorporate and use IoT and ML for value generation.
To effect a digital transformation, organizations must eveolve new methods of hiring and training manpower that can leverage this potential.
Few companies possess the scale, skill sets and tech expertise for implementation of successful IoT strategies and they will scout for partners with complimentary capabilities.
Conclusion
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