Machine Learning is getting more and more popular and accessible, but implementing it is still not a trivial task.
There are a few vital things in the process of introducing Machine Learning that might turn out to be thorns in your side, so it’s best to consider them beforehand. Otherwise, you might end up either abandoning your project halfway, having to spend unforeseen resources, or unsettling your employees.
Before you start implementing Machine Learning, you need data. If you don’t have enough of it, find out how to gather and then consider ML.
Let’s say you have a dataset that contains information about cars. “Enough data” in this case means two things - a lot of cars and a lot of details for each car. You can see many ML examples where you have the size and price of some houses and try to find the correlation between them, but they’re just there to explain the basic principles. In reality, you wouldn’t want to calculate the price of a house based on just its size. You also wouldn’t want to take only a few examples and believe that they represent the real world well.
If you have a 100 cars, you will be able to build a ML model. The problem is it would probably not be very precise. The more cars you have in your dataset, the better the ML algorithm will be able to determine the relationship between their characteristics.
Once you decide you have sufficient amount of data, you need to analyze it. At this point you should have a few ideas on what you want to do with Machine Learning. When you go through your data, you have to find out which of these ideas are achievable. For example, let’s say that you want to determine which type of customers makes the most purchases. You have a lot of clients and a history of what they bought, but you don’t know their age. Well, characteristics such as age, gender, and location would probably have a considerable effect on the outcome. If you don’t know one or more of these details, maybe you have to disregard this idea. Hopefully, in the end at least one of your ML options will remain.
These were the general data-wise obstacles - you need a lot of data, and you need relevant data. There are, however, a few more vital things to consider.
In order to build your Machine Learning model, you’ll have to either hire data scientists, contact another company or teach some of your current employees. You might think: “A lot of companies already use Machine Learning models, I can just find one and use it”. Even if that happens to work, it would be risky and probably lead to wrong predictions. Every dataset requires different processing. Different algorithms are better for different tasks. The way you tune the parameters in your model also depends on the specific problem at hand. And the answer is always not just one. There are vital choices to be made at each step. That is why you’ll need to have people who have some grasp on what’s going on.
In order to If you want to implement Machine Learning you’ll also need to have control of your infrastructure. If you rely too heavily on external solutions, this might not be straightforward. You need to have access to the data you have as well.. You also need to be able to customize your applications so that you can apply the results from a ML model to them. If you use a website builder platform, this might be a problem.
It is also important to consider ethical restrictions. In your desire to automate certain processes, you might accidentally implement a ML model that happens to discriminate some of your clients. Let’s say you have a face recognition model and you want your users to be identified before you let them perform a certain action. The problem is you trained it only with pictures of brown-haired people. What if it doesn’t recognize a person who is blonde or bald, for example?
Gathering data can also involve ethical problems. Is it alright to start tracking people without their consent to gather more data so that you could improve your model? You have to be conscious of these moral implications.
As Machine Learning and AI are evolving, so are regulations. There might be laws which forbid you from gathering certain types of data. There might also be regulations that prevent discrimination against specific groups of people - something you might accidentally do with Machine Learning if you’re not careful. In addition, you should be careful if you use code that is copyrighted.
Introducing Machine Learning to your employees could be harder than it might seem. Some of them might have a negative mindset about it, some might get nervous. They might not understand what you want to achieve with ML and might think that they will lose their jobs. You have to think of how to bring it up to them and educate them on ML and its benefits.
There are a lot of obstacles on the way to actually implementing Machine Learning in your business processes. We tried to outline the most crucial among them. If you think you won’t bump into them, you can probably start working on your ML model.