In a recent IDC spending guide titled Worldwide cognitive systems and artificial intelligence spending guide,   some fantastic numbers were thrown out in terms of opportunity and growth 50+ % CAGR, Verticals pouring in billions of dollars on cognitive systems. One of the key components of cognitive systems is Machine Learning.

According to wikipedia Machine Learning is a subfield of computer science that gives the computers the ability to learn without being explicitly programmed. Just these two pieces of information were enough to get me interested in the field.

After hours of daily  searching, digging through inane babble and noise across the internet, the understanding of how machines can learn evaded me for weeks, until I hit a jackpot. A source, that should not be named pointed me to a “secure by obscurity” share that had the exact and valuable insights on machine learning. It was so simple, elegant and completely made sense to me.

Machine Learning was not all noise, it worked on a very simple principle. Imagine, there is a pattern in this world that can be used to forecast or predict a behavior of any entity. There is no mathematical notation available to describe the pattern, but if you have the data that can be used to plot the pattern, you can use Machine Learning to model it.  Now, this may sound like a whole lot of mumbo jumbo but allow me to break it down in simple terms.

Machine learning can be used to understand patterns so you can forecast or predict anything provided

  • You are certain there is a pattern
  • You do not have a mathematical model to describe the pattern
  • You have the data to try to figure out the pattern.

Viola, this makes so much sense already. If you have data, know there is a pattern but don’t know what that is, you can use machine learning to find it out. The applications for this are endless from natural language processing, speech to text to predictive analytics. The most important is forecasting- something we do not give enough credit these days. The Most critical component of Machine Learning is Data – you should have the data. If you do not have data, you cannot find the pattern.

As a cloud storage professional, this is a huge insight. You should have data. Pristine, raw data coming from the systems that generate it- sort of like a tip from the horses mouth. I know exactly where my products fit in. We are able to ingest, store, protect and expose the data for any purposes in the native format complete with the metadata all through one system.

We have customers in the automobile industry leveraging our multi-protocol cloud storage across 2300 locations in Europe capturing data from cars on the roads. They are using proprietary Machine Learning systems to look for patterns in how their customers- the car owners use their products in the real world to predict the parameters of designing better, reliable and efficient cars. We have customers in the life-sciences business saving lives by looking at the patterns of efficacy and effective therapies for terminal diseases. Our customers in retail are using Machine Learning to detect fraud and protect their customers. This goes on and on and on.

I personally do not know the details of how they make it happen, but this is the world of the third platform. There are so many possibilities and opportunities ahead if only we have the data. Talk to us and we can help you capture, store and secure your data so you can transform humanity for the better.

 

Learn more about how Dell EMC Elastic Cloud Storage can fit into your Machine Learning Infrastructure

 

 

Ashvin Naik

Cloud Infrastructure Marketing at Dell EMC
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