Why Businesses Fail at Machine Learning?
This blogpost discusses why businesses fail at machine learning?
The work suits in the hands of the one skilled to do it.
This famous saying is an instant response to the question that Why Businesses fail at Machine Learning?
Machine learning comprises of areas that are often seen and merged as one. But before getting started with that, let’s have a basic understanding of what we are talking about.
Pattern recognition and the notion that computers can learn other than being programmed to specific performance of action was the basic purpose of machine learning at the time of its birth but later, in today’s world, it has advanced way more than before.
It is a subset and branch of Artificial intelligence and computer science that imitates or tries to copy the human way of learning to improve its accuracy with experience by focusing on algorithms and data specifically.
It is designed to reveal key insights within Data mining projects and is trained to make classifications and predictions by employing statistical methods and algorithms.
Machine learning works as a tool in labeling the unlabelled stuff.
Switching to the main point which I want to make here that How Businesses fail at Machine Learning?`
Cassie kozyrkov, in her course, about Machine Learning, came up with a general unawareness or confusion that most people have regarding Machine learning is that people think that it’s one discipline.
But the reality is there are two types of machine learning. And businesses face trouble when they fail to acknowledge the difference between both types.
- Machine Learning Research
- Applied Machine Learning
What is Machine learning research?
If you make appliances for bringing comfort and convenience to others’ life that is Machine learning research. If your focus is upon general-purpose tools and you generate machine learning algorithms then this business is called Machine Learning research.
All the courses available in academia and Google enable people to learn this type of machine learning. An ample amount of knowledge is required being in this work-line.
Many Algorithms are here around us for so long that it’s hard to remember each one of them just like methods of least square for regression as it was published in 1805.
The sophistication of the current appliances demands nuances of every new appliance coming to market to innovate better one. That’s why the job of a researcher needs plenty of hardship.
What is Applied Machine Learning?
If you bring taste to recipes then hopefully you won’t try recreating a blender, chopper, oven or any other appliance for you. It’s none of your business. your job is to cook and use the appliances that are already available for you in the market.
The current hype focuses highly on the first type of Machine learning that is research rather than its application. Don’t you ever mistake by building your kitchen appliances warehouse being a chef? Similar is the case with engineers cooking lasagne (unless he’s a good cook). But that also is a rare fortune.
Concluding with the point that I am trying hard to make here is that the mathematics of back-propagation of most of the applications in their neural networking is not required to be acknowledged by all teams similar to the need of a chef knowing wiring diagram of a juicer machine.
Unfortunately, many businesses fail to get value from Machine learning because they don’t understand the variations between the research and applied side of Machine learning. And the only suggestion for avoiding such failure is to ‘Hire the right person’.
As The work suits in the hands of those skilled to do it.
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My name is Akhunzada Younis Said. I am a software project manager in HAZTECH, a software engineering graduate and a content writer. I love working with Linux, Data science and open-source software.