The ability to learn is no longer the prerogative of living beings. Machines have become truly intelligent, capable of making decisions based on their own “experience” and moving businesses they are working for to a significantly higher level of efficiency. How does it work? The easiest way to understand this is to analyze the main components of machine learning solutions.
Mind reading ads, spam detection in your email box, self-driving cars, faultless diagnosis made automatically, online translators with a keen sense of context, insights about the oncoming changes in the market, customer behavior prediction, super-accurate pricing – all this became possible due to the leveraging of machine learning. ML is everywhere, and if you are not looking in its direction, you are looking backward. When Bill Gates proclaimed machine learning was worth ten Microsofts, he was not exaggerating.
What is machine learning?
In simple terms, machine learning is the ability of computers to self-study based on the data entering it. While traditionally, a computer performs the actions strictly prescribed by the programs installed in it, in machine learning systems, it finds a solution by independently analyzing this data and identifying probable connections, regularities, and patterns in it. This happens due to various ML algorithms. For example, the classification algorithm allows machines to distinguish between normal message and spam.
With any learning, training is essential. ML is no exception. It is training continuously. The algorithm processes the received information into the probable output. In case of an incorrect result, a small correction is made – and it happens as many times as needed until the output is satisfactory.
For example, if you want the computer to set optimal prices for properties based on a set of characteristics, it will “train” until its results match the market. Moreover, as it develops, the machine learning system gains the ability to make more accurate predictions than human professionals.
You can hardly name any business areas in which machine learning solutions would not bring tangible benefits. Large retailers use it widely right now. By carefully studying the smallest changes in customer behavior, it can set up targeted ads more effectively and automatically create personalized emails which is beyond the power of any human marketer.
ML can take on a huge amount of routine work, eliminating errors and inaccuracies due to human factors. Be it document classification or analyzing changes in the market, a machine will do it much faster and more efficiently than a human. Thus, ML can significantly optimize many operations, saving time, funds, and allowing companies to achieve business goals much faster and smarter.
What are the components of a machine learning system
The structure of a machine learning system can provide a clearer understanding of how it works. Its main elements are: Receiving and storing raw data; Data transformation; Model training and Model testing; Output (Prediction). Let’s have a closer look at them.
Data absorption and storage
Actually, data is what the ML model uses to train on. That is why its collection and absorption are essential here. Traditional programming requires just a limited set of typical samples to process. At the same time, to teach ML algorithms to respond correctly to any input information, you will need much more data. The process gets even more complicated because the features required for predicting can be obtained from different sources. Moreover, these sources are constantly changing.
What are the probable sources? In the field of e-commerce, they may include user activity on the Internet, mobile app event logs, as well as external factors such as geolocation or weather. ML system is capable of simultaneously analyzing all these factors to make precise predictions.
Since the retrieved data may be useful in further queries, it makes sense to create intermediate storage that the system will access whenever needed. Such storage is often referred to as “data lake”. It can contain both raw and processed data.
The information collected by the system enters it in the form of raw data. To make it workable, it must be appropriately transformed. This transformation can include filtering events according to certain criteria. For example, we need to know only those user activities that have occurred in the last few months or exclusively in certain geolocation.
It is often necessary to deal with missing or distorted information. The system can detect various errors in the data. All such mistakes must be weeded out, the missing data must be filled in, for example, with the average value for the certain category.
Another important task is to combine data from disparate sources. For example, in e-commerce, we may need to juxtapose the age of a user, his recent activity with his geolocation. In self-driving cars, its speed, objects detected by computer vision, and weather conditions should be taken into account. In medical diagnosis, the system should consider the medical history, all the symptoms, the treatment that was taken before, the age of the patient, the recent test results, etc.
In any case, all this information should be standardized and transformed into a format suitable for machine processing.
Model training and testing
The training and testing stages of the ML model form a kind of loop. Training results are tested, test results are redirected for retraining. This cyclical movement is repeated until the result of the training turns into an accurate prediction, which becomes the system’s output.
At this stage, it is important to choose the correct ML model as well as the best settings for the selected model. You may need to test several various models to find the best fit for your business needs. Perhaps you will choose a combination of different ones, this approach is called the ensemble method.
It is also important to set up an adequate results display of the system’s work so that they are most convenient to use for the end-users of the system. You might get tabulated results. Someone will need a report that is automatically sent to the email. You may also want detailed infographics. All of these options can also be configured in your machine learning solution.
Instead of a conclusion
The best way to try out the effectiveness of the ML system is to test it in the “wild”. Are the results of its work reliable enough in real conditions, how does it work with new, unseen data? Therefore, it is extremely important to monitor the operation of the system and, if needed, make the necessary alterations. In any case, the introduction of machine learning into your business is a big step forward and an opportunity to discover new opportunities.