Machine learning (ML) is advancing by the minute. It makes operations much easier for many fields such as healthcare, finance, transportation, hospitality, government, and many others.
But first, what is ML? What does it do?
Machine Learning in a Nutshell
In a nutshell, ML is a type of artificial intelligence that gives a machine the ability to predict outcomes based on a set of data.
This machine learning data is interpreted through algorithms, making them, over time, self-sufficient.
One of the common mistakes people make in using ML for this business is they don’t take the time to see the line between research and application.
The Relevance of Research and Application
Of course, these two are essential in ML.
Without research, the correct application of a great invention becomes highly impossible. The absence of application renders the amount of time and effort poured into a study futile.
You simply can’t have one without the other. So, this begs the question, what mistake do business owners commit in this aspect?
They Always Hire Researchers
The skills and expertise of machine learning researchers should never be undermined. Without their dedication, this branch of science wouldn’t flourish into what it is today.
However, hiring a research team for your ML needs might not be too suitable for your business.
For example, you’re looking for machine learning services in your area. So, you assemble a research team only to find out later on that because they conduct intensive studies into something different for your company, they won’t be able to deliver the results you need just yet.
Although there’s no doubt that your research team will be able to reap the harvest eventually, there is a much better way.
Hire Engineers Instead
If your goal isn’t to discover the next big thing about science and AI, hiring researchers might only push your goals back. For immediate and more attainable results, you would want to focus on the application of existing tools.
Keep in mind that when it comes to this part of the business, you don’t always have to create. You only need to utilize what has existed before, modify it into something you can work on for your business, and apply it to your operations.
For these steps, you’d need the help of engineering experts, those who put application above all else.
This is not to say that you should totally stay clear of researchers. Who knows? They might just be the ones to provide the machine learning services you need in your area. But it’s important that, between application and research, you know which one to prioritize.
To put your data to work, you need a better ETHoS. Contact Standpoint Software today.