How to Integrate AI and ML in an Existing App?
AI supports better decision making, and ML allows the system to acquire new things from the data.
Every-one of us loves convenience, comfort, and ease of living. Today there are machines available that understand human behavior making our daily life easy than we have ever imagined. Machine Learning (ML) and Artificial Intelligence (AI) are revolutionizing the industry. These technologies are designed to change the way people use devices and conduct business.
With the advancement of these technologies, every industry is adapting them for transformation and business growth.
This piece of the content focuses on explaining the role of Artificial intelligence and Machine learning and how to integrate the same in your existing application.
It has become crucial for companies to develop customized solutions as per people’s requirements. Understanding customer’s behavior can help enterprises to serve in a better way. And this is made possible with Machine learning app development.
Some of the notable examples of AI and ML in Mobile App Development are: -
· Gmail offers Smart Replies
· LinkedIn helps to Match Candidate with Jobs
· Pin Interest to Identify Object in Image
· Facebook provides Proactive Detection
· Google Chrome helps in Predictive Search
· Amazon shows Recommendations based on searches
For any business to prosper it is vital to make profits. This can be achieved only if old users are retained and new users are gained. Businesses can leverage this advantage by integrating machine learning and artificial intelligence in-app.
Following are the three fundamental ways of integrating machine learning and artificial intelligence in mobile apps.
Mechanized Reasoning
Have you ever thought about how artificial intelligence defeats a human-being at chess or how Google Maps and Uber optimize routes to get their users to reach their destination quicker? The answer is mechanized reasoning. This part of AI allows the computers to apply motive for solving problems.
Customized Recommendation
Every business may it be B2C or retailers try their best to retain customers and build new business relations. This is possible by providing the users’ insights into what they need next. With Artificial Intelligence-powered functionality can be done. Some of the renowned brands like Flipkart, Amazon, and Netflix, etc. are reaping the benefits of the same.
Need of Integrating ML and AI into your mobile app
· Advanced Search
· Customization
· Upgraded Security
· Envisaging User Behavior
· More Pertinent Ads
Behavior
User behavior in the app is of prime importance and can help AI achieve a new level of security. Even if your data is stolen and someone tries to perform online transactions without your knowledge the artificial intelligence system can predict this and stop the same immediately. This way AI safeguards mobile apps.
Now the most important part of the discussion is how to get started.
How to Implement AI and ML in an App?
Execution of AI and ML into an app calls in for an epic shift in the way the application works. Let’s understand how the implementation goes ahead.
· Identify the matter you wish to solve through AI
· Recognize your data
· Understand that using APIs would not be enough
· Decide upon your metrics as per which your AI must work
· Deploy data scientists
Identify the matter you wish to solve through AI
There is an array of different areas where AI and ML can be employed in your mobile business. The very first thing to do is to know the main areas and the matter you wish to resolve. Some of the problems like recommendation systems, customer service, security systems, and data-based insights can be few examples.
So decide upon that which is that part of your application that would advantage of this technology. Would it give better ETA? And then gather data precisely from that field.
Recognize your data
Before you look forward to AI and ML app development, it is necessary to have the basic knowledge of where can you get the data. At the point of obtaining data and enhancement, it would help you to know the platforms from where the information can be gained at first. Next for the refinements of the data always make sure the data you wish to feed in your AI module is truly informative, original, and clean.
Understand that using APIs would not be enough
When implementing AI in a mobile app keep in mind that the more widely APIs — Application Programming Interfaces are used the more unsound they are. APIs are not enough to support a heavy, full-fledged AI solution but they are fair enough to convert your app into an AI app. This means the more intelligent model you seek the harder you need to work towards data modeling — roughly that APIs alone cannot solve.
Decide upon your metrics as per which your AI must work
For an AI implementation program, it is necessary to decide upon your specific aim. There is no point in having ML or Artificial Intelligence feature implemented in your mobile app till you have the method to measure its efficacy. This is possible only after having the know-how of what exactly do you want it to answer. Hence it is a must to understand what you would like to accomplish before implementing ML or AI in your mobile app.
Deploy data scientists
Lastly, the most crucial point to consider is deploying a data scientist on your payroll or outsource the same to a mobile app development agency that has a team of data scientists.
With data scientists, all your management to data refining needs would be taken care of. All the things you require to excel in your ML and AI game would be fulfilled.
Concluding lines…..
Implementing AI and ML into your business can profit you in many ways. From advanced search, customization, apt digital marketing, and knowing the user behavior.
The above points can surely guide you towards the easy implementation of AI and ML to your existing mobile application.
Well, the taste of the pudding is in eating.
Happy Reading!!
About Author
Shilpa M Toprani is a Content Writer at Prakash Software Solutions Pvt. Ltd. With over 12 plus years of experience in the content creation field, she treasures writing. From Content Writing/Editing/SEO/SERM Writing she has written for an array of sectors and a multitude of technology domains.