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Unwrapping Machine Learning

Ashvin Naik

Cloud Infrastructure Marketing at Dell EMC

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

 

 

How IT service providers can turn customers’ data into gold with ECS

Ashvin Naik

Cloud Infrastructure Marketing at Dell EMC

With first-hand experience of building a services business, I know how tough it can be. You talk to your customers, and build or provide services to meet their needs. Often, however, you (the service provider) are seen as a “temporary short-term solution”– before either the customer gets their own solution up and running, or the need goes away. What you need is a way to turn these short-termist customer interactions into long-term, mutually profitable relationships.

Look at how giants like Amazon Web Services and Microsoft Azure have built sustainable and growing public cloud service businesses. Obviously, having household-name brands was a good start. But I believe the key to their success was offering convenient, low-priced data storage.

This convenience and low cost attracts many customers to store data in the cloud – often initially planned as a short-term measure. But once a business has their data in this cloud ‘walled garden’, the provider can sell them all sorts of longer-term value-adding services based around that data – elastic cloud computing, big data analytics, deep archive, disaster recovery, content delivery networks, and so on.

As an IT service provider, you are in a great position to translate this proven business model to your own on-premise private cloud or value-added public cloud. Simpler, inexpensive storage equals more customer data – and more possibilities for providing all sorts of innovative new services built around that data. From specialized services for industry verticals – like financial services, healthcare, government and so on – to meeting specific customer needs as they arise in the future. EMC’s Elastic Cloud Storage (ECS) is the perfect object storage infrastructure for service providers to provide these new services.

ECS Object Storage

Here’s one example of a service you can offer with ECS, to target a specific need for thousands of enterprise customers today. These organizations are using EMC Isilon as primary production storage infrastructure in their datacenters, typically running at around 80% storage utilization – which is fine. But on a busy day, the 20% free capacity could potentially be filled up very quickly – perhaps by someone uploading massive files of raw 4K video footage. Bad things can happen within a datacenter that is at max capacity – so how to avoid this situation? The new EMC Isilon CloudPools feature can be easily configured to overflow data to a cloud storage system – preventing the risks of a datacenter meltdown.

This is where a big new service provider opportunity arises. You can easily offer overflow cloud storage as a service to Isilon customers, and win valuable business. ATOS is now delivering exactly this kind of service in Germany using ECS – maintaining customers’ data sovereignty, and opening up new accounts for value-added services in the future.

‘Hadoop as a service’ is another new and up-coming opportunity. With built-in HDFS capabilities in ECS, you can provide Hadoop data protection and value-added in-place analytics without having to transfer data into separate Hadoop clusters. You can extend this service to Governance, IoT and machine learning based on the customer’s data in your datacenter.

Many more services can be provided with this single storage infrastructure built on top of ECS. As a highly scalable, multi-tenant, multi-protocol object storage system, ECS is the perfect platform for you to take your business to the next level.

ECS turns you into a ‘cloud alchemist’ with the potential to transform even the most everyday data into pure gold – new insights, new opportunities for customers, and innovations to change lives. You can offer a wide range of services at costs that are comparable with a public cloud, but with the peace of mind and quality of service that your customers expect from you, as their trusted advisor in the IT services sector.

Learn more about how ECS solutions.

Join the ECS discussion on Twitter: @EMCECS

Ripple effects of global data: global Hadoop.

Ashvin Naik

Cloud Infrastructure Marketing at Dell EMC

Strata Hadoop world Singapore left me pumping my fists with proof that recipes like the Gartner Value escalator or our very own transformation roadmap provide a simple actionable plan to build hindsight, provide insights and move towards foresights that enable data driven transformations.

The example of transformation came from quite an unexpected keynote speaker Rishi Malhotra : CEO and co-founder of Saavn, in what he termed as data ripple effects.

Rishi’s talk strengthened my belief that data large and small will be created everywhere and consumed for purposes not yet imagined.  As a modern enterprise, businesses have to treat all data as raw materials for future business expansion if not industry disruption. You have to capture, protect and use it for its current purposes but also keep it available for future applications in its native pristine form.

The two popular options for capturing and protecting data in geo-distributed Hadoop architectures are:

  • The public cloud and
  • The Hadoop HDFS storage

The public cloud storage is global, built in the web and caters to the new generation of applications at an attractive cost to store. However the fine print costs for every touch, withdrawal, move – an egress fee, much like the banks or telephone companies of yesterday can quickly add up.

Traditional Hadoop DAS storage – a single protocol (HDFS), single site storage needs edge extensions for transformation and conversion to cater to modern applications. Businesses end up having multiple silos of data stores as they add applications and uses to their existing data.

There was quite an interest in protecting data with sessions on  “Multi tenant Hadoop across geographically distributed data centers” and “Hadoop in the cloud: An architectural How-to” in addition to our very own EMC session on “Hadoop Everywhere: Geo-distributed storage for big data” that indicate a growing interest in addressing the challenges posed by modern mobile applications and the Internet of things.

Hadoop Geo-Distribution

Attendees, prospects and customers at Strata have been instrumental in validating the view that data is the asset of the future and needs to be captured, stored, protected and made available for future uses in a single shared global system. IT managers are looking to de-couple storage from the application stack with a disaggregated stack that is geo-distributed for protection as well as local access with a strong data consistency.

EMC Elastic Cloud Storage provides storage technologies that are simple, easy to manage, protect and scale into the exabyte range with built-in multi-protocol access for modern applications including S3, OpenStack Swift and HDFS.

Click here to learn more about the ECS solution for Hadoop or join our vibrant community on twitter.

 

Destination transformation: Making the roadmap fly.

Ashvin Naik

Cloud Infrastructure Marketing at Dell EMC

In my previous post, we discussed the three step transformation roadmap—a recipe for big data nirvana. Wearing my big data hat, an entrepreneur’s heart, and a problem solving intent, here are a few ideas to see the map in action.

The airline industry hasMake Big Data Fly gone through many cycles, innovations, and disruptions both internal and external. Whatever the path or experiences, most airlines use complex ticket-pricing systems to maximize revenue. In addition to the topline, big data can improve the bottom-line through some innovative transformations.

But wait, what if we could maximize revenue and minimize costs while improving customer experience and maybe taking a step toward developing loyalty. Algebraically, this is just solving for three variables at a time—a true big data challenge. (more…)

Big Data’s Revolutionary Roadmap

Ashvin Naik

Cloud Infrastructure Marketing at Dell EMC

Big data has the potential to transform humankind – from helping us cure diseases to simplifying and streamlining our lives. Now, let us not get into the debatable aspects of our increasingly digital universe but focus on the art of the possible.

After having digitized our entire lives, which is well documented in the digital universe study, we have Digital Universemoved on to machines.

With the help of devices, wearables, sensors and systems, we’ve begun collecting, connecting and disseminating information, thoughts, and experiences in ways unimaginable just a few years back.

Most organizations are looking for ways to ride the waves of big data and transform their domain – be it customer experiences, processes, systems or plain and simple time management. All big data projects need a roadmap, a recipe that can iteratively move you toward your goal. After wading through a wide array of literature, I was able to simplify the process into three steps: an exploration phase followed by optimization that leads to true transformation.Explore Optimize Transform

These iterative steps can help guide your projects irrespective of domain in an easy to execute format.

Explore:
All big data projects start with a question; data scientists will not touch anything without a good question – the first is often to explore. The question can be as generic as:

  • “Is our customer experience with our company good enough to keep them coming?”
    or
  • “How can we transform the customer experience so that customers become staunch evangelists for our brand?”
    or a much more specific question like
  • “How much more would a buyer spend if we kept him or her on the site for two more minutes?”

(more…)

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