Applying Machine Learning To Business Issues

Introduction

Unless you spent the last couple of decades spelunking caves in complete solitude, chances are pretty darn good you’ve heard about machine learning. It became a popular trend in various fields. Yet, as what happens with most trends, many companies try to get it before they realize where to use machine learning in their business. This can cause some unnecessary spendings and a lot of stress and confusion. To make things easier, we are going to give you an idea of what and how to use machine learning for with real advantages.

What Is Machine Learning?

One of the main confusions is caused by the terminology. Artificial Intelligence (AI), Machine Learning, and Deep Learning, — what are they and how do you tell one from another? While deep learning is not being used all that much, the other two are frequently put forth as interchangeable terms. In fact, Machine Learning and AI are strongly related but have nuanced differentiations.

Artificial Intelligence is a broader idea and includes any machine or computer activity which tries to imitate human behavior. The development of the AI field started way back in the 1950-s but gained extreme recognition in the last ten-twenty years. Many of the things we now tend to consider as part of our day-to-day routine were brought to us once as AI ideas. For example, real-time apps that help you to commute within big cities, or apps like Uber use Artificial Intelligence.

Machine Learning is one of the parts of the AI field. It refers to those solutions that use specified algorithms to spot patterns in the provided data and learn from it. So, while all the Machine Learning is part of the AI, no vice versa statement is possible. Due to its nature, Machine Learning is important for companies which operate on colossal amounts of data on a regular basis. It helps them achieve better results in a shorter period of time.

What Can Machine Learning Do?

There are a few applications for Machine Learning in business. Depending on the needs of the company and their field of action, the possibilities vary. But here are a few of the most common applications:

	Email filters If you want your emails to be sorted according to certain labels or topics you might need to set up some rules for your mailbox. Some of the rules are pretty straightforward. For example, an email from a stated address goes into a stated folder. But, other filters are purely contextual and it’s up to the machine algorithm to decide what to do with the email. We can provide the required data, the machine can learn from.
	Plagiarism checks This option might be particularly useful for companies that need to post a lot of unique content. That goes to any newspapers, magazines, commercial blogs, as well as technology companies providing instructions to their customers. Another field of relevance is, of course, a publishing agency.
	Fraud prevention checks For companies operating with any kind of payments, fraud prevention checks are a must. The human eye is often not capable of detecting all possible patterns of a fraud transaction. What may seem normal to a person, can raise a red flag during a machine check.
	Banking apps algorithms We are way past the day when the only things your online banking app could do was show your balance and transfer funds. Today, these apps are capable of so much more. For example, giving you a credit by analyzing your spendings and income. Some of the apps can provide you with suggestions on whether you should buy something based on your financial history.
	Image Recognition For companies that strive for a high level of security, options like facial recognition might be essential. The complexity of a human face and conditions like angle or view and light make it impossible to create a set of direct rules for facial recognition. That’s when Machine Learning provides effective solutions.
	Complex calculations and predictions This is probably the most popular application for the Machine Learning technology. The biggest advantage of the computer over the human brain in such cases is the possibility to process an incredible amount of data at the same time and build new algorithms based on it.

What Should You Consider Before Using Machine Learning For Your Business?

Introduction of the ML technology is not particularly simple or low-cost. That’s why it makes sense to look into the matter carefully. Otherwise, you might end up with a lot of spendings for something you didn’t really need.

1. Make sure the task you want to be done really requires Machine Learning use. The first question to ask yourself here is if the issue can be solved by applying general simple algorithms. Those include yes-no questions or if/else concept. If those will help you achieve your goal, perhaps, ML is not a necessity after all. You might want to look into other options and choose the one that fits you best.
2. It will take time. ML solutions require time to process the given data and teach themselves what to do. If you need Machine Learning for your project, consider some additional time for the machine’s education. You don’t want to end up with a lot of errors in your solutions.
3. How vital is it for you to have no errors? After a certain time of working with the right data, the quality of solutions becomes better and better. But even then, an error-free day is not going to be every day. Once in a while, your machine will get something wrong. The question is, how tolerable mistakes are for your business. If you need a perfect or very close to the perfect result, you might want to consider other options or a backup check.
4. Is your data clean enough? If you decided to go with the ML, you need to make sure that the data you feed to the machine is clean enough. This ensures the fastest learning and the best results. When the provided data is too messy, it’s hard for the machine to find patterns and provide good solutions. Additionally, you need to update your data as often as required. New data is essential to bringing up-to-date solutions.
5. You need to categorize. In order to sort your data appropriately, the computer requires some reference, a label. When they are provided to the machine in advance, it can easily distinguish which data should go where.

Pros and Cons Of Machine Learning

As well as any other technology, ML has some significant advantages compared to other algorithms and some drawbacks.

Let’s start with the nice things:

The possibility to use complex algorithms which are too complicated for human performance. This is one of the main benefits of Machine Learning. For example, for the face recognition or voice recognition technologies ML is a must. A computer can consider much more aspects than a human will. And, unlike a human, it does not have a personal perception. That is why the results observed in this field so far are pretty incredible. The accuracy of the predictions can be really impressive. The medical field, for example, uses deep learning to draw diagnoses and prescribe medication. Also, in cases of marketing predictions, like ads to be shown to a particular user, the technology did a big step forward in the last few years. ML is really good at customers segmentation and lifetime value prediction. The technology can help your business predict, how many of the newly signed up users will stay with your company and what kind of products they might be interested in.

Yet, there are a few Machine Learning problems that can get in the way:

There always will be mistakes. Somehow, people often expect a perfect performance from a machine. But, until now, there was no algorithm created that would return zero errors. If the issue you are trying to solve can not afford any mistakes, you might want to look into other methods or have a human check the results as a backup. It’s not the cheapest technology. There are quite some costs involved in the Machine Learning implementation. First of all, the computational costs are high because of the GPU need. The better the quality of hardware, the faster the learning process. Another thing that affects the costs is the rate of ML specialists. If you want things done right, you will want to hire someone who knows what they are doing. Which brings us to our next point. Shortage of specialists. Unless your business is somehow related to Machine Learning, you are not very likely to have an ML specialist within your staff. That means you will have to hire from the outside. That’s when you will face a talent gap which in a way causes the high costs for hiring. You will get solutions but not answers. Machine Learning will provide you with the ready-to-use solution for a posed issue. However, if you are interested in the logic of the machine, its way of thinking and making decisions, you will be disappointed. Most of the time, there is no telling why the machine chose one or another model. This doesn’t mean the solution is bad, but most likely too complex for human understanding.