Machine Learning App Development – Disrupting Mobile App Industry

Machine learning is a subset of artificial intelligence reshaping numerous industrial domains. With the help of algorithms capable of continually adapting to data, the technology empowers computer systems to uncover valuable insights such as identifying credit card fraud, optimizing manufacturing procedures, predicting customer purchasing patterns, and understanding users’ online preferences.

All these instances have forced us to contemplate how computers can autonomously acquire knowledge from prior experiences. It is interesting to witness a specialized data management system using near-real-time analytics to identify normal patterns and anomalies, compare current data with historical records, and summarize observed regularities. 

Thanks to its remarkable accuracy, these predictions can facilitate intelligent actions without human intervention. Simply put, machine learning app development has the potential to enhance the intelligence of mobile apps and streamline tasks without requiring specialized programming.

This blog will help you understand everything related to machine learning app development, its associated costs, and the factors that affect it. In addition to looking at the process of building a machine learning app, we will also help you dive into an exclusive feature list that guarantees maximum ROI for the app.

What is Machine Learning?

Machine Learning (ML) is a specialized field within the AI ecosystem that revolves around constructing systems capable of learning and evolving through data analysis. This branch of science is intimately intertwined with AI. ML enables AI-based apps to acquire knowledge from the data they gather and enhance their accuracy over time. All this is possible without the need for manual programming.

Machine learning algorithms are capable of recognizing patterns in large datasets, allowing them to make data-driven predictions and informed decisions. With each iteration, these algorithms become more skilled at processing data, resulting in increasingly accurate predictions and choices.

There are three primary categories of machine learning available across the field of science:

Supervised machine learning: In this approach, algorithms are trained using predefined examples. This training allows the ML systems to make accurate conclusions when presented with new data.

Unsupervised machine learning: Algorithms in this category are trained on data without predefined rules. Their task is to uncover patterns and relationships within the data for future predictions.

Reinforcement machine learning: This technique involves training technology to make a series of decisions. Agents learn how to achieve specific goals within uncertain and potentially complex environments.

According to Grand View Research, the machine learning market is expected to expand from $52 billion in 2023 to nearly $420 billion by 2030, witnessing a CAGR of 34.8% from 2023 to 2030. The increasing market size can be attributed to its ability to revolutionize several industrial domains, aid decision-making processes, and enhance customer satisfaction. 

Thus, if you, too, are looking to invest in machine learning app development, now is the right time to partner with a dedicated app development firm that can help you leverage the growing market share while putting this into perspective. 

How to Build a Machine Learning App?

Creating a machine learning application usually involves a structured step-by-step process. Here’s a comprehensive guide our machine learning app development process:

1. Define the Problem

This phase includes initiating your machine learning app development process by clearly defining the primary objectives and the specific problems your business needs to address. It’s essential to have a deep understanding of the problem and how machine learning can offer a solution for the same.

Here are the few questions you need to map out to define your business goals:

• What is the main objective of using machine learning?
• What are the features that you wish to target for analysis?
• What is the type of input data to be used?
• What improvements do you expect to achieve?
• How will you measure the success of the app?

2. Data Collection and Cleaning

Once the problem is identified at your end, it is time to proceed with data collection. This is one of the critical stages in machine learning app development. Data can be gathered from historical databases or open data sources. After collecting the data, cleaning it by removing irrelevant inputs is important. The data quality directly affects the accuracy of machine learning algorithms and future predictions.

3. Data Preparation

Machine learning algorithms operate primarily on numerical data. Thus, it is vital to convert all data into a format machine learning can understand, such as text or images. In addition, the data preparation phase requires you to create a data pipeline to consolidate information from various sources, making it suitable for analysis.

4. Feature Engineering

Feature engineering is the next crucial step in machine learning app development. It involves creating new features by combining existing ones through arithmetic operations. This process helps make the data more relevant and meaningful for effective machine learning algorithms. Data preparation and feature engineering can be time-consuming but essential for successful machine learning app development.

5. Model Training

In this step, the machine learning algorithm enhances its data analysis abilities by dividing the data into two parts: one for training and another for evaluation. The evaluation data is crucial for assessing the model’s generalization ability to unseen data. During training, the algorithms learn patterns and mappings between features and labels. You also have the flexibility to fine-tune model training parameters such as learning rate, regularization, number of passes, and more.

6. Evaluate and Optimize Model Accuracy

During this stage, the machine learning algorithm’s performance is evaluated by using various accuracy metrics, such as detecting overfitting or underfitting. Based on the results, optimizing both the algorithms and training data is important to enhance the overall accuracy.

7. Deploy the Model into Production

The final stage is integrating the trained algorithm into your software for practical use. This step requires technical expertise and is often the most challenging part of machine learning app development. Once successfully integrated, the model can make predictions based on accurate data for real-world applications.

Advantages of Machine Learning App Development for Your Business?

Businesses can now leverage machine learning algorithms and techniques to create highly personalized, efficient, and effective mobile apps. Let us look at the multiple benefits offered by machine learning for your business: 

Personalization

Any machine learning algorithm attached to your mobile app can analyze various sources of information, from social media activities to credit ratings, and provide recommendations to every user device. Machine learning mobile app development can be used to learn:

• Who are your customers?
• What do they like?
• What can they afford?
• What words do they use to talk about different products?

Based on this information, you can classify your customer behaviors and use that classification for target marketing. Simply put, ML will allow you to provide your customers and potential customers with more relevant and enticing content and make an impression that your mobile apps are customized especially for them.

To look at a few examples of big brands using machine learning app development to their benefit,

• Taco Bell employs a TacBot that takes orders, answers questions, and recommends menu items based on your preferences.
• Uber uses ML to provide its users an estimated arrival time and cost.
• ImprompDo is a Time management app that employs ML to find a suitable time to complete your tasks and prioritize your to-do list.
• Migraine Buddy is a great healthcare app that adopts ML to forecast the possibility of a headache and recommends ways to prevent it.
• Optimize Fitness is a sports app incorporating an available sensor and genetic data to customize a highly individualized workout program.

Advanced Search

Machine learning apps let you optimize search options in your applications. ML search results are more intuitive and contextual for its users. ML algorithms learn from the different customer queries and prioritize the results based on those queries. In fact, not only search algorithms, modern mobile applications allow you to gather all the user data, including search histories and typical actions. This data can be used along with behavioral data and search requests to rank your products and services and show the best applicable outcomes.

Upgrades, such as voice search or gestural search, can be incorporated for a better-performing application.

Predicting User Behavior

The biggest advantage of machine learning app development for marketers is that they understand users’ preferences and behavior patterns by inspecting different kinds of data concerning age, gender, location, search histories, app usage frequency, etc. This data is the key to improving the effectiveness of your application and marketing efforts.

Amazon’s suggestion mechanism and Netflix’s recommendation work on the same principle that ML aids in creating customized recommendations for each individual.

Not only Amazon and Netflix but mobile apps such as Youbox, JJ Food Service, and Qloo Entertainment adopt ML to predict user preferences and build user profiles according to that.

More Relevant Ads

Many industry experts have argued that the only way to move forward in this never-ending consumer market can be by personalizing every experience for every customer.

With the help of machine learning, you can avoid debilitating your customers by approaching them with products and services that they have no interest in. Rather, you can concentrate all your energy on generating ads that cater to each user’s unique fancies and whims.

Mobile app development companies today can easily consolidate data from ML that will, in return, save the time and money spent on inappropriate advertising and improve the brand reputation of any company.

For instance, Coca-Cola is known for customizing its ads according to the demographic. It does so by having information about what situations prompt customers to talk about the brand and has, hence, defined the best way to serve advertisements.

Improved Security Level

Besides making a very effective marketing tool, machine learning for mobile apps can streamline and secure authentication. Features such as Image recognition or Audio recognition allow users to set up their biometric data as a security authentication step in their mobile devices. ML also aids you in establishing access rights for your customers as well.

Apps such as Zoom Login and BioID use machine learning for mobile apps to allow users to use their fingerprints and Face IDs to set up security locks on various websites and apps. In fact, BioID even offers periocular eye recognition for partially visible faces.

ML even prevents malicious traffic and data from reaching your mobile device. Machine Learning application algorithms detect and ban suspicious activities.

Industries that Can Benefit from Machine Learning App Development

Machine learning is finding its place in various industrial sectors, offering a multitude of advantages. Let us explore the industries where machine learning holds the most promise:

1. Transportation

The development and growing use of smart and autonomous vehicles is reshaping the transportation ecosystem. AI solutions backed by machine learning algorithms play a pivotal role in traffic monitoring and predictive analysis, allowing cars to navigate autonomously without any human intervention.

2. Healthcare

Machine learning has made its way deep into critical healthcare processes, including disease detection and treatment. ML solutions gather and analyze patient data to aid in diagnosing illnesses and recommend potential treatment methods. Predictive healthcare models are also gaining traction, offering a sure-shot way to revolutionize patient care.

[Also Read: Patient Care in The Age of AI: Understanding The Use of Machine Learning in Healthcare]

3. Finance

The financial industry deals with vast amounts of data that require analysis and automation for predicting future trends, managing investments, and executing transactions. Machine learning and artificial intelligence are invaluable tools for detecting and predicting fraudulent activities across multiple sectors.

4. Cybersecurity

Machine learning is a game-changer in the cybersecurity ecosystem. ML solutions can help with analyzing incident reports, alerts, and other data sources to identify potential threats, enhance security analysis, and even offer guidance on effective responses to cyber threats.

5. Manufacturing

Machine learning also offers immense potential in the manufacturing industry. Companies using assembly lines can employ ML algorithms in conjunction with sensors and AI to predict maintenance needs, safeguarding against downtime and production disruptions.

6. eCommerce

Machine learning is a driving force that is revolutionizing the eCommerce industry. ML solutions are capable of offering personalized product recommendations, thereby increasing average order values and revenue by adhering to individual customer preferences.

7. Marketing

The marketing sector also stands to benefit significantly from availing machine learning app development services. This technology can enhance the efficiency of advertising campaigns by delivering highly personalized ads to users based on their unique interests and needs, thus optimizing ad targeting and engagement.

Real-World Examples of Machine Learning in Mobile Applications

Here are some leading mobile apps harnessing the power of machine learning:

Snapchat

Snapchat employs supervised machine learning algorithms, particularly in computer vision. This mobile app’s machine-learning algorithm identifies faces in photos and adds playful elements such as glasses, hats, dog ears, and more. 

[Also Read: How Much Does It Cost To Develop A Messaging App Like Snapchat?]

Tinder

Tinder uses a reinforcement learning algorithm to improve its “Smart Photos” feature. This algorithm showcases the user’s photos to the other users in a random order and then uses machine learning to analyze the number of right and left swipes each photo receives. This helps Tinder to determine which photos are most appealing to users. The algorithm then reorganizes the user’s photos to prioritize the most popular ones.

Spotify

Spotify leverages three machine learning algorithms to offer users personalized music recommendations, featured in the “Discover Weekly” section. This helps the app to foster deeper user engagement.

• Collaborative Filtering analyzes user-generated playlists and tracks they have listened to and suggests songs from similar playlists.
• Natural Language Processing analyzes the song lyrics, blog posts, discussions, and news articles, categorizing them into “cultural vectors” and “top terms” to recommend music with similar meanings.
• The Audio model analyzes the raw audio track data to categorize songs and suggest tunes with similar characteristics.

Yelp

Yelp uses supervised machine learning to enhance the user experiences with its “Recommended for You” collections. Machine learning algorithms go through the restaurant reviews and determine the most frequently mentioned dishes. The app also utilizes machine learning to design, classify, and label user-submitted photos of dishes with attributes that have  almost 83% accuracy.

Facebook

Facebook integrates machine learning algorithms across various facets of its platform. For instance, it employs machine learning to recommend potential new friends in its “People You May Know” section by analyzing the user profile, interests, current friends, and their connections. 

eBay

eBay adopts a Reinforcement Machine Learning algorithm for its product recommender, ShopBot. This intelligent chatbot interprets user preferences, processing text messages and images to identify the most suitable product matches.

Understanding the Machine Learning App Development Cost

The cost to create a machine learning app usually varies between $50,000 to $300,000. Several factors impact the overall cost of development, such as the complexity of the project, the timeframe it needs to be delivered in, the features to be integrated, the location of the hired machine learning app development company, etc. Let us explain how these factors impact the budget to build a machine-learning app in detail below:

Factors Affecting the Cost to Build a Machine Learning App From Scratch

Several factors influence the machine learning app development cost. If you wish to implement your machine learning mobile app ideas, it is vital to consider these factors and plan accordingly in order to set up a dedicated budget for robust app development:

App Complexity 

The overall app complexity is the most important factor that impacts its overall cost of development. For instance, a highly complex machine learning app with an extensive feature list will usually cost you more as compared to a simple app with minimal features. 

Data Availability

Collecting and preparing data can usually add to the overall cost of building a machine learning app. For instance, the budget can significantly be impacted if the data requires manual labeling or extensive data cleaning.

Algorithm Selection

The machine learning algorithms and techniques chosen for development can also impact the overall cost to create a machine learning app as development time and expertise vary for each. More advanced algorithms often demand larger datasets for effective training, potentially increasing the data acquisition cost.

Model Training

Training machine learning models can be time-consuming and affect overall infrastructure costs. For instance, specialized cloud services might be necessary for efficient model training. This can lead to increased infrastructure and operational expenses.

Data Quality

Poor data quality can lead to increased development costs as more effort is needed to clean and preprocess the data. 

Integration With Existing Systems

Integrating machine learning into an existing app or system may require additional development work and thus increase the overall costs. Seamless integration requires necessary modifications to be carried out for the existing software architecture. This can simply affect the project timelines and budget.

Licensing and Data Costs

The costs of using proprietary algorithms, third-party datasets, or APIs can also add to the overall development budget. The cost of producing these data sets and APIs may vary across multiple regions and sectors. 

Location of the App Development Firm

The location of the hired machine learning app development company also significantly impacts the cost to create an app. For instance, the hourly rate of development is usually low in regions like Asia compared to the US and the UK.

Features to be Integrated

The machine learning app development features that are to be integrated can also impact the overall cost to create a machine learning app. For instance, an extensive set of features, such as real-time data processing, natural language understanding, or advanced recommendation systems, may require more development effort and resources, contributing to higher project costs.

Which are the Best Platforms for the development of a mobile application with Machine Learning?

There are multiple top platforms that excel in harnessing the power of machine learning for mobile app development. Let us look at them in detail below:

Azure – Azure is a Microsoft cloud solution. Azure has a very large support community, high-quality multilingual documents, and a high number of accessible tutorials. The programming languages of this platform are R and Python. Because of an advanced analytical mechanism, the developers can create mobile applications with accurate forecasting capabilities.

IBM Watson – The main characteristic of using IBM Watson is that it allows the developers to process user requests comprehensively regardless of the format. Any kind of data. Voice notes, images, or printed formats are analyzed quickly with the help of multiple approaches. This search method is not provided by any other platform than IBM Watson. Other platforms involve complex logical chains of ANN for search properties. The multitasking in IBM Watson places an upper hand in the majority of the cases since it determines the factor of minimum risk.

Tensorflow – Google’s open-source library, Tensor, allows developers to create multiple solutions depending upon deep machine learning, which is deemed necessary to solve nonlinear problems. Tensorflow applications work by using the communication experience with users in their environment and gradually finding correct answers as per the requests by users. Although, this open library is not the best choice for beginners.

Api.ai – It is a platform that is created by the Google development team, which is known to use contextual dependencies. This platform can be very successfully used to create AI-based virtual assistants for Android and iOS. The two fundamental concepts that Api.ai depends on are – Entities and Roles. Entities are the central objects (discussed before), and Roles are accompanying objects that determine the central object’s activity. Furthermore, the creators of Api.ai have created a highly powerful database that strengthened their algorithms.

Wit.ai – Api.ai and Wit.ai have largely similar platforms. Another prominent characteristic of Wit.ai is that it converts speech files into printed texts. Wit.ai also enables a “history” feature that can analyze context-sensitive data and, therefore, can generate highly accurate answers to user requests, and this is especially the case of chatbots for commercial websites. This is a good platform for the creation of Windows, iOS, or Android mobile applications with machine learning.

How Can Appinventiv Help You Leverage Machine Learning for Your Business?

Utilizing machine learning for mobile apps is the way forward, as it empowers your mobile application with an array of personalization options, enhancing its usability, efficiency, and effectiveness. While having a solid concept and user interface is crucial, integrating machine learning takes it a step further, ensuring that your users enjoy the finest possible experiences.

Creating machine learning applications is a complex process that requires extensive expertise in related algorithms, data science, and statistics. It is vital to employ the right methods for guaranteeing their success in this competitive world. Thus, collaborating with an expert app development firm that can guide you in this process and develop a valuable application for your business is the need of the hour. Machine learning has the potential to reshape your business, providing a competitive edge in a fast-changing digital landscape.

At Appinventiv, our machine learning experts are capable of developing robust apps per your unique business requirements. By collaborating closely with you, we aim to understand your precise needs and goals, allowing us to develop applications that bring exceptional value to your business. 

As a dedicated AI development company, we are well-equipped to assist you in navigating the complex aspects of your project, ensuring a smooth journey that is clear of common pitfalls and lays a solid foundation for a successful app development process.

Get in touch with experts to leverage machine learning app development services for your business now!

FAQs

Q. How much does it cost to develop a machine learning app?

A. The cost to create a machine learning app may vary from $50,000 to $300,000. There are several factors that impact the development budget, for instance, the overall complexity of the app, the timeline for development, the location of the hired machine learning app development firm, features to be integrated into the app, etc. Get in touch with our experts to get clear cost estimates based on your project requirements. 

Q. How long does it take to build a machine learning app?

A. The timeframe to build a machine learning app from scratch is directly dependent on the overall app complexity. For instance, developing a highly complex app with an extensive feature list will take around 12 to 15 months. On the other hand, a simple app with a simple UI/UX and a feature will take around 3 to 6 months. 

Q. What are the benefits of machine learning in mobile app development?

A. Machine learning in mobile app development offers significant advantages, including personalized user experiences, predictive analytics for informed decision-making, increased efficiency through automation, enhanced security measures, etc.

THE AUTHOR

Sudeep Srivastava

Co-Founder and Director