When it comes to buzzwords, ‘machine learning’ is practically a senior citizen, first coined 59 years ago by American computer scientist Arthur Samuel. So why the nearly six-decade deep freeze? The idea of machines that could achieve “deep learning” and build their algorithms based on previous ones was quite simply not possible because computers weren’t powerful enough to handle all the necessary computing power, infrastructure, and storage requirements.

Now that the hardware has caught up, primarily due to cloud technology, organizations are scrambling to implement machine learning, many without even realizing what they have or what they can implement for to improve their businesses.

Before diving into the best ways to get machine learning in sync with your business, let’s take a look at how machine learning works and the key components that must be in place for machine learning to properly function.

How Machine Learning Works

While the result can be spectacularly complex, as witnessed in processes like Facebook’s facial recognition system DeepFace, machine learning is a pretty simple sequence of events. It starts with training data, which you’ll use to train an algorithm to create a test model. You derive training data for the historical data you’ll eventually pour into your machine learning algorithm to generate the results your business is searching.

Once the algorithm has developed a model, your next step is to test it. This data is often called validation data and comes from the same master set of historical data, but should have no overlap with the training data.

The test shows if your model behaves as expected. If it doesn’t, and rarely will it on the first incarnation, you’ll have to use additional training data until your model’s performance meets your initial expectations. Assuming you’ve parsed the training data correctly, the more you can feed into your algorithm, the better it performs. Once your validation data gives accurate results in regards to the algorithm’s expected behavior, it’s time to take the giant step forward into real-time data.

The need businesses have for real-time data analytics is the overwhelming force behind the uptick in Machine Learning needs. From Amazon to self-driving cars, the need for the constant flow of data and analysis has become integral. The combination of real-time data and machine learning gives businesses the ability to see what’s happening now and predict what will happen next.

Five Key Requirements of Machine Learning

To successfully harness the power of machine learning, businesses need to have a plan.  However, they also require the necessary hardware and infrastructure. These include the following:

  • Data preparation capabilities: Data needs to be prepared before it can be manipulated. Tools of this nature can do tasks as simple as discovering data to curating it, structuring it or turning it into models.
  • Algorithms: Before a computer can begin to ‘learn’ and predict future outcomes, it has first to be taught.
  • Automation processes: The entire point of the machine learning is that it can happen without human intervention or supervision. These processes allow the machine to access more and more data without human support.
  • Classification or Clustering? Classification, also known as supervised learning, is the process by which machines are given a set of predefined classes and wants to place new objects in one of those classes. Clustering, also known as unsupervised learning, deals with grouping a set of objects and then trying to find if there is a relationship between the objects. Make sure you know which one is necessary for your business needs.
  • Ensemble modeling: The ability for a machine to run multiple models and meld the results into a single score is essential for its future success in predictive analytics.


When you’re ready to integrate machine learning into your business, here are some tips to remember.

Tip #1: Treat data like it’s money because it is.

For all the buzz about blockchains and ICOs these days, there’s no form of currency more valuable than data. Unlocking its secrets can define and shape the future success of your business. Take every precaution to safely store, hoard and manage your data including harnessing it through machine learning to unlock its real potential.

Tip #2: Find one problem that machine learning can solve and get started.

You don’t have to justify machine learning for every single segment of your business. What you do need to do is identify the one problem that entirely could be solved with predictive analysis or data mining and get to work on solving that problem through the use of big data and machine learning.

Starting small is suitable for both the supporters of machine learning and its critics to see what it can do. If the first problem fails, you chalk it up to a learning experience and figure out where the fly in the ointment is. If the first challenge succeeds, you take that model and base the second problem’s solution on it, while continuing to refine your first effort.

Tip #3 Make data science required knowledge for your staff.

Don’t ask them all to learn how to write code and conjure up automated deliverables, but the ability to look at data and comprehend what the numbers mean cannot be understated in today’s real-time business environment.

Sites like The Data Incubator are offering curriculum for employees. If you don’t have the budget or the time for that sort of endeavor, an overview of probability and statistics will do nicely as well.

Tip #4 Define what success looks like in language everyone understands.

Machine learning, AI, and analytics are going to have a lot of specific terms that the average employee or stockholder isn’t going to have in their vernacular. Give them clear-cut expectations for your investment based on goals and percentages that they can process and make part of their business sense.

If your first Machine Learning process is an attempt to reduce spam mail in your employees’ company email accounts by 50%, and you end up cutting it by 65%, give them those two numbers.  Then add along how much space the reduction frees up on your server and the increase in productivity per employee based on the reduction in time wasted sifting through spam.

If you try to include your language process for the algorithms, you risk boring your stakeholders or making them feel unintelligent. They don’t need to know how it works, just that it does. It’s a bit like the construction company that built your desk wanting to walk you through what sort of screws they used. You’re not overly concerned with how it came together, just excited that it works.

Tip #5 Don’t be afraid to farm out unstructured data processing.

It’s not surprising that companies get a little paranoid about the thought of using a third-party to do data analysis. Companies like Target, Yahoo, and Equifax have spent millions wiping the egg off their faces following data breaches of customer data. However, before you start churning out your insights, you need a considerable amount of data processing done, some of which is in the form of mindless, repetitive tasks that can take home-grown mining apps weeks or months to do.

Consider an off-the-shelf API when you need simple processing like tagging named entities or breaking down order forms into how items are sold at what location. The likes of Google, Microsoft, IBM, and other giants have cloud-based APIs that can churn through your data and return it in a processed form ready to be turned into insights for your company’s future success.


About the Author

James is an avid investor in real estate and the stock market. He has found an edge in his real estate investing with digital marketing.

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