Are you ready for a Machine Learning solution?

Machine learning (ML) seems to be on everyone’s lips nowadays, even though it’s not a novelty already. Many entrepreneurs are eager to apply ML to their companies, as its algorithms demonstrate an impressive practical value: ML allows them to examine customer behavior trends, enhance the product in general, and study the operational patterns of businesses.

ML can add to applications’ accuracy, meaning a more significant business profit. No wonder more people are trying to apply this data science field to their projects. The question is whether their products are ready to use ML solutions as part of their business.

Machine Learning approach in software development

While many confuse ML with artificial intelligence (AI), it is only a type of the latter one. ML allows data scientists to write programs that would improve their functionality and ability to predict specific output values (even if these programs were not programmed to do this). It is possible thanks to ML algorithms applying certain data types to make predictions regarding possible outcomes.
Contemporary corporate giants resort to ML and put it in the first place when it comes to internal operations. There are several approaches to classical ML:

• Supervised machine learning (or task-driven). The algorithms deal with labeled data so that they can then cluster datasets and make precise outcomes.
• Unsupervised machine learning (or data-driven). The algorithms analyze, find all the connections, and classify unlabeled data.
• Semi-supervised learning. The algorithms demonstrate the characteristics of both data-driven and task-driven learning. Developers can include labeled data in the algorithm, but still, the model can study this data.
• Reinforcement learning. This type’s algorithm chooses the best possible action throughout the multi-step process to get a bigger reward in this or that situation.

Data scientists choose the most appropriate ML approach depending on the data type they want to predict.
The difference between the traditional approach and the ML approach lies in the way how these two operate. ML algorithm builds its logic using input data, analyses it, and seeks answers or predicts the outcomes. In comparison, the traditional algorithm relies on code to make its logic and give an output. This difference means you would need either a traditional or an ML approach for diverse tasks.

When should you use the ML?

Perhaps, the concept of ML sounds like it has a solution for anything, but it’s not correct. ML offers solutions for specific problems. Sometimes developers need determined steps, simple computations, or rules to define target values. And these would be as effective as ML in other cases. ML would be a perfect decision when:

• You can’t code the rules. You may have too many rules for some tasks — they might overlap or must be meticulously sorted. It would be hard for humans to carry out the task without errors and get the correct answer. Here is when ML can help you out.
• You can’t handle large-scale tasks. Imagine you have thousands of emails, and you need to make sure these are not spam — it’s an exhausting task if you decide to solve it manually. ML can cope with the task fast and accurately.

When should you not use the ML?

So, ML can be an inappropriate solution sometimes, and it is especially not feasible in three cases:

• Problems of low complexity. Machine learning is a robust solution when you require a detailed analysis of the patterns or need to find complex connections. Also, if you have to work with data that consists of various independent variables, ML is not applicable.
• You haven’t enough labeled data. In ML, data labeling refers to the identification of such raw data as video files, images, or text files and adding some informative labels (context) so that the ML model can learn something from it. When there is not enough quality data — you can get false and even risky predictions.
• Your in-house specialists have poor expertise. Before you decide to apply ML to your product, make sure your employees have strong knowledge of machine learning and data science, or, at least, you can hire a team of experts. Otherwise, it would be nearly impossible to handle deep ML algorithms.

Benefits of using ML

If you understand that you can get labeled datasets and you have an expert team to work with ML algorithms, you should know what advantages you get from machine learning, eventually:

• Automatic: in ML, a computer analyses everything and interprets data. Data scientists do not predict data. For example, think about antivirus software that seeks viruses on its own.
• Applicable in different fields: ML is suitable for a vast variety of areas — engineering, logistics, education, finances, etc.
• Quickly identifies patterns and trends: the good thing about ML is that the more information you put into the algorithm, the more trends and process patterns it can detect. The simplest example is Instagram and how it studies user behavior based on people’s likes and the pages they follow.
• Works with diverse data: ML is an excellent solution for multitasking, where you need to handle different datasets.
• Space for improvement: ML copes better with tasks each time it gets more data and processes it. Eventually, you get a more accurate outcome.
• A perfect fit for education: modern education develops and changes fast. Today, kids master new technologies to study quicker and more effectively. ML can act as kids’ tutors and update them with school subjects news.
• IoT product helper: ML can also be helpful for IoT products’ development. For example, a smart thermostat app can analyze data on temperature preferences and weekly schedules to manage the office or building temperature. We at Edgica are keen on using ML components in our IoT solutions.