The Importance of Machine Learning in Applications
Machine Learning is an application of AI (Artificial Intelligence ) that enables the software to automatically learn, explore, and imagine outcomes without human intervention. Machine learning has been used in various fields, and currently, it is inevitable to mobile application development.
According to some statistics, in the year of 2017, Netflix saved $1 billion by using machine learning to make personalized recommendations. Imagine how this result increases every year along with the high success rate of computer technology development.
Businesses are always in search of new technological waves to follow and lead the market, outperforming their competitors. One of the modern tech waves that gain significant attention is AI.
Some twenty years ago, we used heavy computers and an Internet connection that was based on modems. Yet, things have been changed! Currently, each of us has a personal supercomputer in pockets, a Wi-Fi Internet connection and even much more! This modern era requires businesses to provide a super-personalized experience for the prospects and target audience. This is the reason why AI is the future.
Machine Learning in Application Development
Artificial Intelligence makes it easy to learn about people’s behavior that helps in building a highly personalized experience for them. If you wonder about how you can apply this kind of new technology in the nearest future, and start achieving long-term benefits from it, then I will try to help you. For this time, I want to focus your attention especially on AI Machine Learning as well as machine learning applications. We will together discuss some tips on how to use ML (machine learning) in mobile applications and how important it is for technologies in different industries. Let’s start!
Data mining allows big data analysis and helps to discover useful patterns and connections within significant data sets. It consists of data storage, maintenance, and data analysis. Machine Learning provides not only a set of tools but also the necessary learning algorithms that help to find all the possible connections within data sets.
Imagine, you want to develop a mobile application for the travel industry or already have. If you have appropriate traffic, then probably a ton of people daily use it. To speak clearly, it is impossible for human power both to analyze all the possible variations and to identify complicated customer behavior patterns.
Therefore, you can gather all the data about your customers, including:
- their gender and location,
- Facebook connected accounts,
- how they fill out their profile,
- how often they visit your app,
- how often they go on vacation, etc.
Once all these data are collected in your database, it is time to apply Machine Learning. As a result, you can analyze the data and receive valuable insights related to your mobile app users. For example, you might learn characteristic features of people under a specific age and who live in a specific geographical location. This helps you organize a strategy, show the users very personalized offers, and reach better results. You can build a general user test, and figure out the targeted destination that will increase the conversion for users.
Above we learned that in the case of machine learning, we deal with terms like data, model, training, decision, and experience. ML programs are fed a huge amount of data to train. In the training stage, it learns the rules of the problem and gathers experience. Due to the experience, it becomes easier to make a decision when a new problem arises. On the other hand, while working on statements related to new data and problems, it adapts to the new situations. We may say that like humans, it “learns while working”.
A very important aspect of creating an ML system is the process of building and training the models. These models cover collections of pre-processed data and chosen algorithms that work well on that data to figure out the output. Sometimes, it is a complex procedure to create a model.
In some general cases, we start with the fundamental rules and chosen algorithms. The system is to be fed with a lot of useful data. The data is the previous entries we have collected in the system. With the help of this data, first, we create several “candidate models” that are to be used and tried on new datasets, while their performance is observed. According to the results of the success rate, the better model is chosen and deployed to deliver fresh use cases. Due to this new incoming data, the chosen model takes a decision and collects more experience as well as adapts itself to the ever-expanding use cases.
How to use AI ML in apps
AI and Machine learning are very trendy aspects of technology and there are many directions to choose from. To choose the best way to go may depend on how much power and flexibility the developers want, or how specific their use case is. We can either choose from ready-made AI offerings (from Google Cloud or AWS platforms) or deploy own custom models. Below you may find the stages to follow after you understand what the AI ML can do and identify the areas where it may improve the application:
1. Estimate the situation and prioritize the additions
This stage is the preparation of a plan for AIML integration. Firstly, there is a need to decide how much you tend to acquire from the integration. Doing things one by one will be even better.
However, if you have enough budget, you can implement the integration of all changes at once. Once you already identified the main inclusion and improvements in your app and estimated your financial abilities, then it’s time to prioritize what requirements are more essential to be done first.
2. Making changes and usefulness
This stage offers a feasibility test that helps to understand whether or not the future implementations are going to benefit the business, as well as improve the user experience or increase engagement. A successful update is the one that makes the existing users happy and attracts more people towards the products. If an update is increasing your business efficiency, then there is no point in putting in money for it.
3. Involve AI ML Experts
One of the most important things is to choose the resources, that will carry out the development and up-gradation process. Once you don't work with the right specialists, accomplishing your expectations becomes more difficult. Accordingly, you should wisely make the selections.
4. Data integration and security
While implementing Machine Learning, your application needs a better data organization model. The old data may affect your ML deployment efficiency. So, after you plan what capabilities and features you should add in the app, your next step is to focus on databases. Accurately organized data and attentive integration support in keeping the application performance-oriented and provide high-quality in the long-term.
Another critical issue that can’t be ignored is security. To keep your application strong and robust, you should come up with the right plan to integrate security, following the standards and needs of your product.
The most critical point on this stage is to carefully deploy and test the implementations before making all the changes live. While adding AIML capabilities in your app, an important suggestion is to consider putting a strong analytics system in place. Such an approach helps you analyze the impact of new integration and get insights for future decisions.
6. Strong technological aids
Technologies and digital solutions that back your application must be chosen right. Your security tools, data storage aids, optimization solutions, backup software, and many more techniques should be strong and robust, which will help to keep your application consistent. Without this, an extreme decline may take place in performance.
How to Understand ML
Generally, ML is categorized in supervised and unsupervised learning ways:
1. Supervised learning — In this case, we feed the machine learning algorithms with a great amount of data that is labeled (for instance, marked with the results). Such data includes the segments or labels attached to the entries. In supervised learning, we lead the system on how to recognize any new input according to the data we have already delivered.
2. Unsupervised learning — here the data is neither labeled nor classified. In this case, the system is not aware of the success or failure of the outcome as it doesn’t have any possible guidance. In the case of unsupervised learning, the system itself tries to sort available data and bring patterns based on the given information. Then the system stores these patterns. In this case, when a new input appears, it should match with the already stored patterns as well as assign the chosen pattern on it.
Reinforcement learning is sometimes considered to be a type of unsupervised learning. Neither in this case, the input data is not labeled. However, when it achieves success, the data is delivered back to the system in order to signify that the result is successful which improves future outcomes.
Nowadays, there are many machine learning application examples across many industries, including government, transportation, healthcare, and e-commerce. You can choose the best ML app, depending on the ML use case. Machine learning algorithms are to improve customer experience, increase engagement, and maintain customer loyalty, and so on. This technology better fits any mobile business app that requires predictions and an enough large data set.