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A Complete Guide To Integrate AI & ML In Your Mobile App

AI

22
Mar 2021
2627 Views 12 Minute Read
a complete guide to integrate ai and ml in your mobile app
podcast
While discussing the present, we don't notice that we are actually speaking of yesterday's future. And one such futuristic tech to discuss is how to integrate AI & ML in current apps. Long gone are those days of generic services and simpler technologies; today, we live in a highly machine-driven world. Machines can learn behaviors and make our everyday lives more accessible than we ever imagined, making it necessary for us to understand ML and AI's integration process into apps.
 
Nowadays, AI and ML are infused in almost every mobile app we opt for, making it essential to know how to implement artificial intelligence and machine learning into mobile apps. For example, our food delivery app will display the restaurants which deliver the sort of food we like to order, time management apps tell us the ideal time to finish a task & how to prioritize our work, and our on-demand taxi apps display the real-time location of our rides.
 
Machine learning & artificial intelligence, which were once considered the top complicated techs to work on or even process, is something that has become a daily part of our lives without even users realizing its presence. A proof of this is the following functionalities provided by the top brand apps.
 
AI-enabled apps are taking control of the digital market quickly. People are appreciating and using it with complete excitement. Previously we used to think of this era, where the work must be done right after we say it. Now that dream has come to reality with AI and ML integrated apps. There is no need to write or recall things as our smart devices can easily manage this. All users need to do is put a reminder or command their smartphones, and the task is finished. AI has several key features, making it so demanding among mobile app developers & owners.
 
The vast inclusion of the two related techs has made the need for stressing over simple and even complex things stop existing as our apps and our smart devices are doing that for us.
 
Let's look at the below stats:
 
- AI & ML-based mobile apps are leading categories among funded startups
- The number of companies investing in machine learning is projected to double over the next 3 years
- 76% of US companies have surpassed their sales targets because of machine learning
- 40% of US companies use machine learning to enhance sales & marketing
- European banks have raised product sales by 10 percent and reduced churn rates by 20%, thanks to machine learning
 
Some tech professionals opine that AI apps will have a future discovery of electricity. This tech facilitates a broad range of activities in the business world. It has a massive demand in disaster management, banking industry, news channel, healthcare, agriculture, etc. It is forecasted that the worldwide AI market will reach $190.61 billion by the year-end 2025.
 
Any business's goal is to make a profit, and that can only happen when they gain new users & retain their old users. The challenging task can be made simple through AI as it comes as one of the advantages or benefits of incorporating AI and ML in apps.
 
There are 3 primary ways through which the power of artificial intelligence and machine learning can be infused into mobile apps to build the app more efficiently, smart, and sound. The ways can even answer how to include AI & ML to your app.
 
Reasoning
 
This part of AI deals with getting the computers to implement reasoning for solving issues. This facility makes artificial intelligence beat a human at chess and how Uber utilized automated reasoning for optimizing routes to get the app users to reach their destination quicker.
 
Recommendation
 
This is one of the most ordinary use cases of machine learning and artificial intelligence in the mobile app industry. Providing the user's insight into what they would need next is the secret of success of some top brands globally, such as Flipkart, Amazon, Netflix, amongst others have been utilizing AI-enabled power over a long-haul now.
 
Behavioral
 
Learning the user behavioral patterns in the app can help AI set a new border in the world. Each time someone tries to steal your data and impersonate any online transaction without you being aware, the AI system can track this odd behavior and cease the transaction there itself.
 
The 3 prime bases that respond to the top ways to infuse machine learning and artificial intelligence in your app can be used in various capacities to allow your app to offer a much better user experience. And after looking at how to implement AI and ML into apps, let us answer the why.
 
Why Should You Implement Machine Learning And Artificial Intelligence Into Your App?
 
Personalization
 
Any AI algorithm attached to your mobile app can assess various sources of information from social networking platforms to credit ratings & render suggestions to every user device. ML app development can be used to learn:
 
- Who are your users?
- What are their likings and preferences?
- What can they afford?
- What terms are they using to talk about different products?
 
Based on the above information, you can understand your user behavior and use that classification for target marketing. In simple terms, machine learning will enable you to render your users and potential users with more relevant & appealing content and make an impression that your app technologies with AI are tailored especially for them.
 
Below are some instances of huge brands that are setting examples of how to utilize AI & ML in mobile apps?
 
Uber uses machine learning to offer an estimated arrival time (ETA) and cost to its users.
 
Taco Bell is a TacBot that takes orders, responds to questions, and suggests menu items based on your preferences.
 
Migraine Buddy is a fantastic healthcare app that embraces machine learning to predict the possibility of a headache and suggests ways to reduce it.
 
ImprompDo is a Time management app that uses machine learning to find an ideal time for you to finish your tasks & prioritize your to-do list.
 
Optimize Fitness is a sports app that includes an available sensor & genetic data to tailor a super individual workout program.
 
Advanced Search
 
Through AI and ML-powered app development processes, you will get an app that allows you to optimize your mobile app's search options. AI and ML searches result more contextually & intuitively for its app users. The algorithms learn from the unique queries placed by users and prioritize the outcomes based on those queries.
 
Search algorithms and modern mobile apps also let you gather all the user data, including search histories & typical actions. This information can be used alongside the behavioral data & search requests to rank your products and services & display the best applicable results. Upgrades like gestural search or voice search can be infused for an enhanced performing app.
 
Forecasting User Behavior
 
forecasting user behavior
 
A great benefit of AI-enabled ML app development for marketers is to make them understand users' preferences and behavioral patterns by inspection of different sorts of data regarding gender, age, location, app usage frequency, search histories, etc. This information is the key to enhancing the effectiveness of your app and marketing efforts.
 
Amazon and Netflix's suggestion mechanism works on the same principle that machine learning helps create tailored suggestions for every individual. And not just Netflix and Amazon, but mobile apps like JJ food service, Qloo entertainment, and Youbox adopt machine learning to forecast the user likings and create the user profile likewise.
 
More Relevant Ads
 
Several industry professionals have applied this point that the only way to go ahead in this ever-lasting consumer market can be attained by personalizing every user experience. As per the Relevancy group's report, 38% of executives are already using ML for mobile apps as a part of their DMP (Data Management Platform) for advertising. 
 
By integrating ML in mobile apps, you can get rid of debilitating your users by approaching them with items and services that they have no interest in. Instead, you can focus your energy on producing ads that serve each user's unique whims and fancies.
 
Machine learning app development firms today can easily merge data smartly that will, in return, save time and funds went into inappropriate advertising & enhance the brand reputation of any business. For instance, Coca-Cola is known for tailoring its ads according to demographics. It does this by having information about what situations evoke users to speak about the brand and hence, defines the best way to serve ads.
 
Better Security Level
 
Apart from making an effective marketing tool, machine learning & artificial intelligence for mobile apps can even streamline & secure app authentication. Attributes like audio and image recognition make users set up their biometric information as a security authentication step in their smart devices. ML also helps you in building access rights for your users as well.
 
Apps like BioID and ZoOm Login have invested in AI and ML app development to permit users to use their FaceIDs and fingerprints to set up security locks to multiple websites and apps. In fact, BioID also provides periocular eye recognition for partially visible faces.
 
Now that we have looked into multiple areas wherein AI and ML can be integrated into the mobile app, it's time to look at the platforms which will make it possible.
 
Which Are The Top Platforms For The Development Of A Mobile App With ML?
 
1. Azure
 
Azure is a Microsoft cloud solution with a massive support community, a high volume of multilingual documents, and a high no. of accessible tutorials. This platform's programming languages are Python & R. Due to an advanced analytical mechanism, the developers can create mobile apps with precise predicting capabilities.
 
2. IBM Watson
 
The primary attribute of using IBM Watson is that it permits the developers to process user requests completely, irrespective of the format. Any sort of data, including images, voice notes, or printed formats, is assessed quickly with multiple approaches. Any other platform than IBM Watson does not render this search method. Multitasking in IBM Watson has the upper hand in most cases since it ascertains minimum risk factors.
 
3. Tensorflow
 
Tensorflow, Google's open-source library, allows AI app development firms to create various solutions depending upon deep learning, which is considered essential to solving nonlinear issues. This app works by using the communication experience with app users in their surroundings and slowly finding the correct answers as per users' requests, even though this open-source library isn't the best choice for amateurs.
 
4. Api.ai
 
It's a platform created by the Google development team, also known to use contextual dependencies. This platform can be effectively used to develop AI-based virtual assistants for iOS and Android. The 2 basic concepts that Api.ai depends on are Roles & Entities. Entities are the main objects, and Roles are partnering objects that ascertain the main object's activities. Moreover, the creators of Api.ai have created a super-powerful database that reinforced their algorithms.
 
5. Wit.ai
 
Api.ai & Wit.ai have highly similar platforms. Another well-known attribute of Wit.ai is that it converts speech files into printed texts. Wit.ai even facilitates a "history" feature that can assess context-sensitive data & thus can produce exact answers to user requests. This is usually the case of chatbots for commercial websites. This is an excellent platform for developing iOS, Android, or Windows mobile apps with ML.
 
6. Amazon AI
 
amazon ai
 
The popular AI-enabled platform is used to recognize human speech, visual objects with deep machine learning processes. The solution is totally adapted for cloud employment, thus enabling you to build low complexity AI-based mobile apps.
 
7. Clarifai
 
The solution based on AI assesses information with complex and capacitive algorithms. The apps made using this platform can adapt to individual user experiences, making it the most preferred choice for app developers who desire to invest in AI app development to enter the world of smart robots.
 
You now know the ways your app can become an AI-powered app and the tools that will help with AI and ML app development. The next, the last, and the most important part we will discuss are how to begin.
 
How to begin with the integration of AI into mobile apps?
 
- Recognize the problem you would like to resolve through AI
- Know your data
- Understand that APIs aren't adequate
- Set metrics to help you measure AI's success
- Hire data scientists
 
Integration of AI or ML in an app calls for a monumental shift in the app's operation. This shift, asked by AI, demands looking at pointers that are different from what is required while investing in the usual mobile app development process.
 
Below are a few things that you must consider while managing an AI project:
 
1. Recognize The Problem You Would Like To Resolve Through AI
 
What works in implementing AI in an app is incorporating the tech in one process instead of multiple. When the tech is implemented in a single app feature, it's much easier to manage and leverage it to the best extent. Therefore, recognize which is that part of your app that would advantage from intelligence. Would the tech help in providing a better-estimated arrival time? And then gather data particularly from that field.
 
2. Know Your Data
 
Before looking forward to AI app development, it is essential to understand where the data would come from. At the data fetching and refinement stage, it would help to recognize the platform where the info would come from in the first place. Later, you will have to look at the data refinement, ensuring that the data you plan to feed in your AI models are not copied, clean, and actually informative.
 
3. Understand That APIs Aren't Adequate
 
understand that apis arent adequate
 
Another big thing for incorporating AI in a mobile app is understanding that the more widely you use it, the more unsound APIs would prove to be. Although the APIs mentioned above are sufficient to convert your app into an AI app, they are not adequate to support a voluminous, full-fledged AI solution. The point being that the more you want a model to be smart, the more you'll have to work towards data modeling, something that APIs alone cannot solve.
 
4. Set Metrics To Help You Measure AI's Success
 
There is hardly a point to have an AI or ML feature applied in your mobile app until you have the mechanism to gauge its success, which can only be drawn after understanding precisely what you want it to solve. Therefore, before you head out to integrate AI or ML in your mobile app, understand what you would like it to attain.
 
5. Hire Data Scientists
 
The last essential point to consider is hiring a data scientist on your payroll or investing in an experienced app development company with data scientists in their team. They will help you with all your data refining & management needs, which are required on an essential level to stand & excel in your AI game.
 
In this stage you are now prepared to apply the intelligence in your app. Since we talked too much about data in the last segment and it's an integral part of AI, let's look at the solution to the problems that can arise out of data as the parting note.
 
What Are The Solutions To The Most Ordinary Challenges In AI Tech?
 
Just like any other tech, there is always a series of challenges attached to AI too. The basic working principle behind ML is the availability of adequate resource data as a training sample. And as a learning benchmark, the size of training sample data should be big enough to ensure a basic perfection in the AI algorithm. To eliminate the risks of misinterpretation of visual hints or any other digital information by the mobile app, the below are some methods that can be used:
 
1. Hard Sample Mining
 
When a subject includes many objects similar to the primary object, the machine ought to baffle between those objects if the sample size rendered for analysis as the example isn't big enough. Differentiating between various objects with various examples is how the machine learns to assess which object is the main object.
 
2. Data Augmentation
 
data augmentation
 
When there's a picture in question wherein the machine or mobile app is needed to recognize a central image, there must be alterations made to the whole image keeping the subject unchanged, thus allowing the app to register the primary object in various environments.
 
3. Data Addition Imitation
 
Here, some of the data is nullified, keeping only the data about the main object. This is done for machine memory to contain the data about the primary subject image and not about the surrounding objects.
 
In case you plan to build an AI-enabled app, then it is an excellent idea. Even if you implement this concept into your business, this will give your app and business a unique platform.
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Harnil Oza is the CEO & Founder of Hyperlink InfoSystem. With a passion for technology and an immaculate drive for entrepreneurship, Harnil has propelled Hyperlink InfoSystem to become a global pioneer in the world of innovative IT solutions. His exceptional leadership has inspired a multiverse of tech enthusiasts and also enabled thriving business expansion. His vision has helped the company achieve widespread respect for its remarkable track record of delivering beautifully constructed mobile apps, websites, and other products using every emerging technology. Outside his duties at Hyperlink InfoSystem, Harnil has earned a reputation for his conceptual leadership and initiatives in the tech industry. He is driven to impart expertise and insights to the forthcoming cohort of tech innovators. Harnil continues to champion growth, quality, and client satisfaction by fostering innovation and collaboration.

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