Machine learning is a concept that utilized big data to make many processes smarter and automated. The concept is used in operations such as cybersecurity, predictive analytics, automated operations, and more. Now, for a few years, app developers have found ways of utilizing machine learning algorithms to benefit machine learning techniques to improve the efficiency and quality of applications. But machine learning algorithms are divided into three crucial categories. These categories are picked on the basis of their compatibility with the objectives of the app. So, before diving into the discussion of the influence of machine learning on the app development processes, let’s understand three main categories of machine learning.
Supervised machine learning- One of the most prominent use cases of machine learning technology, supervised machine learning is fed with the labeled data. This predefined data helps machine learning algorithms in concluding a process.Unsupervised machine learning- In these processes, the data is unlabelled and no rules are assigned. Machine learning algorithms make conclusions based on patterns and data relations that they can detect.Reinforced machine learning- This method uses trials and errors to update processes repeatedly until it ends up with the right conclusion.Impact of machine learning on the mobile app development processes
Now, as we are clear about three basic types of machine learning algorithms, it’s time to look into the impacts of machine learning on the app development industry.
1. Personalized user experience
One of the major advantages app developers have due to the machine learning algorithms is the freedom of personalizing the user interface. Many genres of apps including streaming apps, eCommerce apps, etc are already using unsupervised machine learning algorithms to personalize the user experience by offering them related recommendations. These recommendations are shortlisted with the help of the big data generated by tracking user behavior. Some common examples can be product recommendations that you see on eCommerce websites and apps, streaming recommendations based on your history, course recommendations depending on your educational backgrounds and preferences, etc.
2. In-depth analytics
Analytics is used to assist users with in-depth reports. These reports are usually integrated with educational apps, healthcare apps, fitness apps, and finance apps among others. By pairing up machine learning algorithms with big data, analytics are prepared. For example, apps such as Coursera offer analytics displaying weak and strong points of your preparation patterns. These patterns are detected on the basis of mock exams that you choose to pick. Machine learning uses the data generated through your exam attempts to provide you with personalized preparation tips and recommended courses that can improve your performance.
3. Enhanced connectivity
Connectivity of multiple devices is a common feature most apps are offering these days. If you have smart appliances at home, then you might have the idea that there are apps existing almost for all smart devices to control them even without a physical remote. The technology that is used to connect these devices with each other is called the Internet of Things (IoT). IoT is used as a bridge between devices to send and receive data. This data is used to control or inspect devices remotely. Due to advanced machine learning algorithms, the future of IoT looks more advanced and features such as responsive voice commands, smoother controls, and more will become a common thing.
4. Improved cybersecurity
Cyber security is one of the major threats that any company can face. However, with time, cyber security protocols are getting equipped with better protection strategies. These protocols are based on historical data and with machine learning and AI, predictive analytics is used to continuously monitor and defend hardware and software in an organization. Cyber security supported by machine learning is commonly used in fintech operations, customer data security, defense data security, and more.
5. Enhanced customer assistance
Machine learning is helping the customer service segment in improving the quality of its services. Machine learning frameworks help developers in building apps that do not only offer a more personalized texting experience with the help of chatbots, but also a better voice recognition system. Chatbots are able to understand various languages, pronunciations, accents, and more to assist customers in the best possible way. This feature has helped organizations in improving their customer satisfaction rates which directly impacted their customer retention rates positively as well.
Wrapping up
Machine learning is an evolving technology but it plays the most important role in advancing technologies such as Artificial Intelligence (AI). With the increasing efficiency of machine learning algorithms, AI operations get smarter. And, as the mobile app development industry continues to grow, it depends on developers to find ways of using machine learning to survive and thrive in the competitive market. As mentioned earlier, machine learning has endless potential, so, with time keeping an eye on new emerging technologies based on apps and machine learning is not a bad idea. By looking at the example of how far we have come from the days of Java phones, it is true that many unpredictable features and technologies will make their way into the future market and most of these will be innovations of machine learning and apps.
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