Tag Archives: Machine Learning

Analytics, Cloud

Machine learning for IIoT: 4 tips to get you started

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The Industrial Internet of Things (IIoT) will bring large volumes of fast-moving data.  This brings both challenges and opportunities.  At the risk of stating the  obvious, one challenge is making sense of large complex data sets.  Machine learning approaches can help here, so I’ve got four tips for getting you started with machine learning:

1.  Forget some of what you know about analytics

If you plan to deal with IIoT data, you may need to refresh your thinking about analytics.  Historically, analytics have been a relatively simple and sedate affair.  For example, analysis was often performed on historical data at some point in time after it was generated.  In addition – for better or worse – analytics often mirrored the siloed nature of data.  That is, the integration of data was minimal.  Industrial IoT will bring more data, faster, from a greater variety of sources.  Managing this data complexity to be able to respond to events in a timely way will required a much more automated and frictionless approach to the analytics value chain.  Machine learning is one way to achieve that.  It can be especially powerful with complex data, where patterns are not obvious and it’s difficult – nay, impossible – for humans to formulate and code rules.  Unfortunately, the lack of transparent logic in machine learning can be an obstacle for some people that must be overcome.  Many engineers just aren’t comfortable with black-box solutions.  Tough, get over it.

2.  Explore machine learning as a technology

The cloud changes everything.  In this particular case, it demolishes the barriers to entry for machine learning.  A new generation of machine learning tools (from BigML, Microsoft, Amazon, and IBM for example) are cloud-based products.  Most offer a free trial, some for an indefinite time period.  They also offer a much more guided, tutorial-style development experience than the previous generations of software.  So what’s the cost to learn more and experiment with it…?  It’s your time.  At this point, extensive investigation of machine learning tools prior to selection isn’t strictly necessary.

Here’s how the evaluation process can work:

  • Pick a cloud-based machine learning tool; any one, it doesn’t really matter.
  • Spend a day or two playing with it.
  • If you like it, play some more.
  • If you don’t like it, pick another tool and start over using the experience you’ve already gained.

3.  Don’t be fooled – successful machine learning isn’t all data science

True enough, at a technical level, machine learning can appear enigmatic.  Seemingly without rules or logic, it can be daunting to try and understand the details.  But, that’s what IT professionals, analysts, and data scientists are for.  Like all successful IT projects, successful machine learning projects do not start and end with IT.  Business and domain expertise are crucial to success.  Consider the application of machine learning to maintenance.  Domain expertise is necessary to identify potential source data to feed the algorithms.  Further, domain expertise is required to interpret and provide context to the output of machine learning.  Like all successful IT projects, machine learning applications require a collaborative cross-functional team.

4.  Consider prescriptive maintenance applications

Many enterprises will be breaking new ground with IIoT applications.  It’s critical that the first wave of IIoT applications deliver a tangible and measurable return on investment.  Re-inventing the approach to asset maintenance provides a clear path to measurable benefits.  Research by ARC’s Ralph Rio shows that the most common approach to maintenance is still simple preventative maintenance.  And yet, as the same ARC research also shows, that is not the optimal approach for the majority of assets.  Maintenance applications that incorporate machine learning are a promising approach for capitalizing on Industrial IoT data.  The potential return on investment (ROI) in predictive maintenance is real, tangible, and relatively immediate – all good things you need in a beachhead project.

So those are my four tips – consider it my Christmas gift to you.  And no, you can’t take them back to the store for a refund if you don’t like them…

Shameless plug alert:  This and more in my exceedingly good value research report on machine learning for Industrial IoT.

(Originally published on industrial-iot.com, a blog by ARC Advisory Group analysts)

Analytics, Cloud, IoT

Two reasons machine learning is warming up for industrial companies

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Machine learning isn’t new.  Expert systems were a strong research topic in the 1970’s and 1980’s and often embodied machine learning approaches.  Machine learning is a subset of predictive analytics, a subset that is highly automated, embedded, and self-modifying.  Currently, enthusiasm for machine learning is seeing a strong resurgence, with two factors driving that renewed interest:

Plentiful data.  It’s a popular adage with machine learning experts:  In the long run, a weaker algorithm with lots of training data will outperform a stronger algorithm with less training data.  That’s because machine learning algorithms naturally adapt to produce better results based on the data they are fed, and the feedback they receive.  And clearly, industry is entering an era of plentiful data. Data generated by the Industrial Internet of Things (IIoT) will ensure that.  However, on the personal / consumer side of things, that era has already arrived.  For example, in 2012 Google trained a machine learning algorithm to recognize cats by feeding it ten million images of cats.Today’s it’s relatively easy to find vast numbers of images, but in the 1980’s who had access to such an image library…?  Beyond perhaps a few shady government organizations, nobody.  For example, eighteen months ago Facebook reported that users were uploading 350 million images every day.  (Yes, you read that correctly, over a third of a billion images every day).  Consequently, the ability to find enough relevant training data for many applications is no longer a concern.  In fact, the concern may rapidly switch to how do you find the right, or best, training data – but that’s another story…

Lower Barriers to Entry.  The landscape of commercial software and solutions has been changed permanently by two major factors in the last decade or so:  Open source and the cloud.  Red Hat – twenty-two years old and counting – is the first company that provided enterprise software using an open source business model.  Other companies have followed Red Hat’s lead, although none have been as commercially successfully.  Typically, the enterprise commercial open source business model revolves around a no-fee version of a core software product – the Linux operating system in the case of Red Hat.  This is fully functional software, not a time–limited trial, for example.  However, although the core product is free, revenue is generated from a number of optional services, and potential product enhancements.  The key point of the open source model is this:  It makes evaluation and experimentation so much easier.  Literally anyone with an internet connection can download the product and start to use it.  This makes it easy to evaluate, distribute and propagate the software throughout the organization as desired.

Use of the cloud also significantly lowers the barriers to entry for anyone looking to explore machine learning.  In a similar way to the open source model, cloud-based solutions are very easy for potential customers to explore. Typically, this would just involve registering to create a free account on the provider’s website, and then starting to develop and evaluate applications. Usually, online training and educational materials are provided too.  The exact amount of “free” resources available varies depending on the vendor. Some may limit free evaluation to a certain period, such as thirty days.  Others may limit the number of machine learning models built, or how many times they can be executed, for free. At the extreme though, some providers will provide some limited form of machine learning capacity, free of charge, forever.

Like open source solutions, cloud-based solutions also make it easier – and reduce the risk – for organizations to get started with machine learning applications.  Just show up at the vendors website, register, and get started. Compare both the cloud and open source to to the traditionally licensed, on-premise installed software product. In this case, the purchase needs to be made, a license obtained, software downloaded and installed. A process that could, in many corporations, take weeks to achieve.  A process that may need to be repeated every time the machine learning application is deployed in a production environment…

My upcoming strategy report on machine learning will review a number of the horizontal machine learning tools and platforms available.  If you can’t wait for that to get started, simply type “machine learning” into your search engine of choice and you’re just 5 minutes away from getting started.

(Originally published on industrial-iot.com, a blog by ARC Advisory Group analysts)