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Demystifying Machine Learning | @CloudExpo #AI #ML #DX #MachinLearning

There’s a lot of discussion about the difference between Artificial Intelligence and Machine Learning

Demystifying Machine Learning - How Do Machines Really Learn?
By Mark Troester

Are machines really intelligent? Learn the answer and how it can affect your business. It's machine-learning 101 for curious business leaders out there.

Autonomous cars taking us on our favorite and most efficient routes, virtual assistants serving up the exact data a doctor needs to diagnose an illness or that an engineer needs to identify a faulty part, customer support bots that are always available to answer your questions and book your appointments accurately and quickly. All of these use-cases are either here or will be soon, and they all rely on Machine Learning to be successful. But how do the machines learn? There’s a lot of market confusion out there, and it’s important to take a step back and understand what we’re talking about and why.

There’s a lot of discussion about the difference between Artificial Intelligence (AI) and Machine Learning (ML), and even the term analytics has become very broad. What’s important from a business perspective is not the technical definitions but the value that it brings you. All of these technologies are powered by data, and the goal for a business is simply to do what it takes to move from data to insight to outcomes as efficiently as possible.

­One area where there is a clear connection between data, insight and outcomes is in the field of maintenance. Routine preventative maintenance has long used data to guide implementation, such as changing the oil in your car and rotating your tires at regular intervals. Done correctly, it will be much more efficient than simply driving your car until it breaks down and then fixing what’s broken. While there is some basic data that goes into the maintenance cycles, with the proliferation of IoT sensors it’s become possible to get a far more sophisticated view of the condition of your machinery and equipment.

The large volume of data presents a tremendous opportunity, but it can be hard to take full advantage. After all, you don’t simply need more data to guide your decisions—you’re likely drowning in that already. In fact, one of the biggest questions that you may be facing as a business leader is how to utilize the data you’re already generating on a daily basis.

Perhaps you’re already employing data scientists to build the models you need to make sense of it all, but they can only process a fraction of the data that is flooding in. Hiring more data scientists is not only expensive, but there is such a shortage that hiring enough may be impossible. A better solution, then, is to utilize a Machine Learning platform to remove this burden from the team processing your data, allowing them to focus on making decisions while the platform focuses on analysis at scale.

For example, Cognitive Predictive Maintenance (CPdM) is a powerful way to apply Machine Learning techniques to preventative maintenance. With a CPdM platform, all of the data coming in is reviewed, compared to historic results, analyzed for patterns and presented for viewing—automatically and in real time. The platform doesn’t just base its results on past events at a moment in time, but is constantly learning and improving based on the data being generated from countless sensors, personalizing the maintenance that is needed for every device you’re monitoring. Instead of changing your oil routinely at 3,000 miles, now you only change it when your engine needs you to. Imagine what that means for large industrial use cases where downtime—whether unplanned or routine—can cost hundreds of thousands or millions of dollars.

That’s what Machine Learning can do, and the efficiencies and savings it can generate for businesses is clear. Now that we know how they can be used, it’s time to look at how machines interact with data and demystify how they really “learn.”

What Really Happens with Machine Learning?
As we covered above, more data is readily available than ever before. Beyond the IoT sensors that constantly generate machine data, there’s also behavior data from customers and users and business transaction data in an ERP or CRM calling out to be analyzed. Data is in such abundance that the level of contextual insight that can be derived is phenomenal.

Humans, however, have a hard time processing data at this level. The average human is capable of processing several dimensions at once – for example, the temperature and humidity – but when too many variables are introduced, even the most intelligent among us can’t process it all. We start eliminating variables, or we generalize, which reduces the accuracy of our analysis.

Machines, on the other hand, do not have this limitation. Computers boil everything down to binary decisions, yes or no states, 0s and 1s, and eliminate nothing. Mathematical models can then be designed to simulate real world behaviors, and the computer can run the entirety of the data input against the model to yield a result that is as accurate as possible.

The key, then, to producing accurate results lies in the model. Ordinarily the creation – and critically, the frequent tuning – of a model is a complex task, requiring the painstaking labor of a team of trained data scientists. To keep the model up to date, it must take in the very latest and most relevant data, and the new analysis must be compared with past analysis and results, leading the data scientist to a series of conclusions that allows them to increase the accuracy further. This can be a difficult and time consuming process.

Fortunately, it’s all math, and we can use Machine Learning to automate much of this process. A computer can tweak a mathematical model in a multitude of ways and test its accuracy, constantly adjusting the input or the algorithm. These adjustments make it appear as if the machine is learning, but it is merely improving the accuracy of a model.

At a certain level of complexity, the mathematical algorithms created in this way are beyond the understanding of the average human being (myself included). But it’s all still just a model—there’s no actual “learning.” The magic is in the math.

What Else Has Changed, and What to Do Next
In our next post, we’ll discuss the advances in technology that have made all this possible, and dive into the practical steps you can take to use this in your own business.

Can’t wait? Learn more about building cognitive-first business applications.

21st International Cloud Expo, taking place October 31 - November 2, 2017, at the Santa Clara Convention Center in Santa Clara, CA, will feature technical sessions from a rock star conference faculty and the leading industry players in the world.

Download Show Prospectus ▸ Here

Cloud computing is now being embraced by a majority of enterprises of all sizes. Yesterday's debate about public vs. private has transformed into the reality of hybrid cloud: a recent survey shows that 74% of enterprises have a hybrid cloud strategy. Meanwhile, 94% of enterprises are using some form of XaaS - software, platform, and infrastructure as a service.

With major technology companies and startups seriously embracing Cloud strategies, now is the perfect time to attend 21st Cloud Expo, October 31 - November 2, 2017, at the Santa Clara Convention Center, CA, and June 12-14, 2018, at the Javits Center in New York City, NY, and learn what is going on, contribute to the discussions, and ensure that your enterprise is on the right path to Digital Transformation.

Track 1. Enterprise Cloud | Cloud-Native
Track 2.
Big Data | Analytics
Track 3. Internet of Things | IIoT | Smart Cities

Track 4. DevOps | Digital Transformation (DX)

Track 5. APIs | Cloud Security | Mobility

Track 6.
AI | ML | DL | Cognitive
Track 7.
Containers | Microservices | Serverless
Track 8. FinTech | InsurTech | Token Economy

Cloud Expo | @ThingsExpo 2017 Silicon Valley
(October 31 - November 2, 2017, Santa Clara Convention Center, CA)

Cloud Expo | @ThingsExpo 2018 New York 
(June 12-14, 2018, Javits Center, Manhattan)

Download Show Prospectus ▸ Here

Every Global 2000 enterprise in the world is now integrating cloud computing in some form into its IT development and operations. Midsize and small businesses are also migrating to the cloud in increasing numbers.  

Companies are each developing their unique mix of cloud technologies and services, forming multi-cloud and hybrid cloud architectures and deployments across all major industries. Cloud-driven thinking has become the norm in financial services, manufacturing, telco, healthcare, transportation, energy, media, entertainment, retail and other consumer industries, and the public sector.

Cloud Expo is the single show where technology buyers and vendors can meet to experience and discus cloud computing and all that it entails. Sponsors of Cloud Expo will benefit from unmatched branding, profile building and lead generation opportunities through:

  • Featured on-site presentation and ongoing on-demand webcast exposure to a captive audience of industry decision-makers.
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  • Breakout Session Priority scheduling for Sponsors that have been guaranteed a 35-minute technical session
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  • Capitalize on our Comprehensive Marketing efforts leading up to the show with print mailings, e-newsletters and extensive online media coverage.
  • Unprecedented PR Coverage: Editorial Coverage on Cloud Computing Journal.
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For more information on sponsorship, exhibit, and keynote opportunities, contact Carmen Gonzalez by email at events (at) sys-con.com, or by phone 201 802-3021.

The World's Largest "Cloud Digital Transformation" Event

@CloudExpo | @ThingsExpo 2017 Silicon Valley
(Oct. 31 - Nov. 2, 2017, Santa Clara Convention Center, CA)

@CloudExpo | @ThingsExpo 2018 New York 
(June 12-14, 2018, Javits Center, Manhattan)

Full Conference Registration Gold Pass and Exhibit Hall ▸ Here

Register For @CloudExpo ▸ Here via EventBrite

Register For @ThingsExpo ▸ Here via EventBrite

Register For @DevOpsSummit ▸ Here via EventBrite

Sponsorship Opportunities

Sponsors of Cloud Expo | @ThingsExpo will benefit from unmatched branding, profile building and lead generation opportunities through:

  • Featured on-site presentation and ongoing on-demand webcast exposure to a captive audience of industry decision-makers
  • Showcase exhibition during our new extended dedicated expo hours
  • Breakout Session Priority scheduling for Sponsors that have been guaranteed a 35 minute technical session
  • Online targeted advertising in SYS-CON's i-Technology Publications
  • Capitalize on our Comprehensive Marketing efforts leading up to the show with print mailings, e-newsletters and extensive online media coverage
  • Unprecedented Marketing Coverage: Editorial Coverage on ITweetup to over 100,000 plus followers, press releases sent on major wire services to over 500 industry analysts

For more information on sponsorship, exhibit, and keynote opportunities, contact Carmen Gonzalez (@GonzalezCarmen) today by email at events (at) sys-con.com, or by phone 201 802-3021.

Secrets of Sponsors and Exhibitors ▸ Here
Secrets of Cloud Expo Speakers ▸ Here

All major researchers estimate there will be tens of billions devices - computers, smartphones, tablets, and sensors - connected to the Internet by 2020. This number will continue to grow at a rapid pace for the next several decades.

With major technology companies and startups seriously embracing Cloud strategies, now is the perfect time to attend @CloudExpo@ThingsExpo, October 31 - November 2, 2017, at the Santa Clara Convention Center, CA, and June 12-4, 2018, at the Javits Center in New York City, NY, and learn what is going on, contribute to the discussions, and ensure that your enterprise is on the right path to Digital Transformation.

Delegates to Cloud Expo | @ThingsExpo will be able to attend 8 simultaneous, information-packed education tracks.

There are over 120 breakout sessions in all, with Keynotes, General Sessions, and Power Panels adding to three days of incredibly rich presentations and content.

Join Cloud Expo | @ThingsExpo conference chair Roger Strukhoff (@IoT2040), October 31 - November 2, 2017, Santa Clara Convention Center, CA, and June 12-14, 2018, at the Javits Center in New York City, NY, for three days of intense Enterprise Cloud and 'Digital Transformation' discussion and focus, including Big Data's indispensable role in IoT, Smart Grids and (IIoT) Industrial Internet of Things, Wearables and Consumer IoT, as well as (new) Digital Transformation in Vertical Markets.

Financial Technology - or FinTech - Is Now Part of the @CloudExpo Program!

Accordingly, attendees at the upcoming 21st Cloud Expo | @ThingsExpo October 31 - November 2, 2017, Santa Clara Convention Center, CA, and June 12-14, 2018, at the Javits Center in New York City, NY, will find fresh new content in a new track called FinTech, which will incorporate machine learning, artificial intelligence, deep learning, and blockchain into one track.

Financial enterprises in New York City, London, Singapore, and other world financial capitals are embracing a new generation of smart, automated FinTech that eliminates many cumbersome, slow, and expensive intermediate processes from their businesses.

FinTech brings efficiency as well as the ability to deliver new services and a much improved customer experience throughout the global financial services industry. FinTech is a natural fit with cloud computing, as new services are quickly developed, deployed, and scaled on public, private, and hybrid clouds.

More than US$20 billion in venture capital is being invested in FinTech this year. @CloudExpo is pleased to bring you the latest FinTech developments as an integral part of our program, starting at the 21st International Cloud Expo October 31 - November 2, 2017 in Silicon Valley, and June 12-14, 2018, in New York City.

@CloudExpo is accepting submissions for this new track, so please visit www.CloudComputingExpo.com for the latest information.

Speaking Opportunities

The upcoming 21st International @CloudExpo@ThingsExpo, October 31 - November 2, 2017, Santa Clara Convention Center, CA, and June 12-14, 2018, at the Javits Center in New York City, NY announces that its Call For Papers for speaking opportunities is open.

Submit your speaking proposal today! ▸ Here

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Progress offers the leading platform for developing and deploying mission-critical, cognitive-first business applications powered by machine learning and predictive analytics.

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