Insights | Considering Machine Learning for Your Next Software Development Project? Read This.

Considering Machine Learning for Your Next Software Development Project? Read This.

By Andy Hilliard | April 2, 2016

machine_learning.jpgMachine learning is a powerful tool for the modern business. In fact, some believe that businesses that fail to derive value from machine learning will face huge competitive disadvantages as the field continues to grow and adoption increases around the world.

Machine learning is an incredibly powerful artificial intelligence tool, which can process petabytes of information to order a chaotic world of big data. As one of the most popular courses at Stanford, applications for machine learning algorithms continue to rise, from predicting emergency room wait times to enabling a world of driverless cars. However, while machine learning is a powerful tool for utilizing iterative processes of pattern recognition and adaptation to improve outputs, we’ve not quite reached HAL 9000 levels of artificial intelligence, but understanding how machine learning can positively impact your next software development project is critical.

Software Developers Are Not Data Scientists
It’s important to note that software developers and data scientists might both participate in your machine learning initiative, but it would be unfair to expect a software developer to be able to fully utilize all the capabilities of machine learning. A data scientist is trained to understand and interpret large data sources to derive the best model for analyzing and predicting desired future outcomes based on your data.

However, that doesn’t mean a great developer doesn’t have some level of data science acumen to improve your software applications. Developers can do many things from building and deploying models to utilizing machine learning best practices to maintain predictive accuracy. With that in mind, we highlighted three things a great developer should know about machine learning.

Know How to Deal with Unstructured Data
Anyone involved in a big data project will tell you that data rarely comes in a neatly wrapped package ready for analysis and manipulation. Big data is often synonymous with unstructured data, which includes files like email messages, videos, photos, audio files and many other types of business documents that lack any sort of formal structure. Experts estimate that 80 to 90 percent of the data in any organization is unstructured.

A good developer should understand how to manipulate raw data and mold it into the desired format so the machine learning algorithm can consume it. For example, developers should understand how to utilize a number of computer visioning techniques to extract features from images or apply natural language processing to turn text into features.

Know Common Machine Learning Methods
As the most commonly used machine learning methods, supervised and unsupervised learning methods are essential for software developers to understand. Supervised learning algorithms are modified when the expected output of the algorithm isn’t in line with the actual outcome. For example, if you’re working on a new Internet of Things (IoT) application that needs to control a device based off the performance of other factors, you know when the outcome should happen and when it should not. A great developer should be able to incorporate this type of learning into your initiative.

Popular, but perhaps slightly less well understood, unsupervised learning is a bit more complicated. In this instance, the algorithm doesn’t know what the “right answer” should be, it must figure it out for itself. This kind of learning is really great when breaking down transactional data. Unsupervised learning can help identify customer segments with similar attributes for targeted marketing campaigns or identify the primary factors by which customer segments differ from one another.

Understand the Underlying Business Problems
Many important business problems like product recommendations and ad targeting have standardized machine learning formulas that can successfully be deployed across a number of different applications. However, a good developer will be able to look beyond the standard algorithms to understand which option might be the best for your business. Perhaps your application requires a higher level of predictive accuracy; a good software developer should be able to understand that and provide the appropriate recommendations.

While it’s true that developers are not data scientists, that doesn’t mean that they haven’t had the opportunity to learn some data science applications throughout the course of their software engineering experience. If you’re looking to incorporate some machine learning algorithms into your next software development project, be sure to find a developer with previous experience.

If you are confused about where to start or just don’t have the time to search through hundreds of software development partners, Accelerance can help. Contact us for a free consultation on your software development needs. We’ll help you find and qualify partners anywhere in the world to ensure that your projects are meeting their goals and helping your business be successful.  

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