What’s the difference? Which should your business use? And how does machine learning apply to IoT?
Published in · 3 min read · Apr 30, 2018
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This chapter is adapted from the Ultimate Guide to Getting Started in IoT — A free eBook written by Leverege. To download the eBook, click here.
With all the hype around machine learning, many organizations are asking if there should be machine learning applications in their business somehow.
In the vast majority of cases, the answer is a resounding no.
One of the major benefits of the cloud is that it enables you to leverage virtually infinite storage and processing power to gain critical insights from the data your sensors/devices will be collecting. Both data analytics and machine learning can be powerful tools in doing so, but there’s often confusion on what they actually mean and when is best to use one or the other.
At a high level, machine learning takes large amounts of data and generates useful insights that help the organization. That could mean improving processes, cutting costs, creating a better experience for the customer, or opening up new business models.
However, most organizations can get many of these benefits from traditional data analytics, without the need for more complicated machine learning applications.
Traditional data analysis is great at explaining data. You can generate reports or models of what happened in the past or of what’s happening today, drawing useful insights to apply to the organization.
Data analytics can help quantify and track goals, enable smarter decision making, and then provide the means for measuring success over time.
The data models that are typical of traditional data analytics are often static and of limited use in addressing fast-changing and unstructured data. When it comes to IoT, it’s often necessary to identify correlations between dozens of sensor inputs and external factors that are rapidly producing millions of data points.
While traditional data analysis would need a model built on past data and expert opinion to establish a relationship between the variables, machine learning starts with the outcome variables (e.g. saving energy) and then automatically looks for predictor variables and their interactions.
In general, machine learning is valuable when you know what you want but you don’t know the important input variables to make that decision. So you give the machine learning algorithm the goal(s) and then it “learns” from the data which factors are important in achieving that goal.
A great example is Google’s application of machine learning to its data centers last year. Data centers need to remain cool, so they require vast amounts of energy for their cooling systems to function properly. This represents a significant cost to Google, so the goal was to increase efficiency with machine learning.
With 120 variables affecting the cooling system (i.e. fans, pumps, speeds, windows, etc.), building a model with classic approaches would be a huge undertaking. Instead, Google applied machine learning and cut its overall energy consumption by 15%. That represents hundreds of millions of dollars in savings for Google in the coming years.
In addition, machine learning is also valuable for accurately predicting future events. Whereas the data models built using traditional data analytics are static, machine learning algorithms constantly improve over time as more data is captured and assimilated. This means that the machine learning algorithm can make predictions, see what actually happens, compare against its predictions, then adjust to become more accurate.
The predictive analytics made possible by machine learning are hugely valuable for many IoT applications. Let’s take a look at a few concrete examples…
