How to Build an AI App: A Step-by-Step Guide

Over 50% of companies planned on using AI services in 2023. For context, only 20% of companies were using AI in 2017. These numbers suggest that this is just the beginning and AI is only expected to grow. Moreover, many individuals are starting to use AI daily. For instance, Google’s voice-to-text feature, Siri, and YouTube’s recommendation system — all of these applications are integrating AI.

That is why if you are thinking of learning more about building AI apps, it is the right time for this. Learning how to build an AI app would give you an edge over others. You could use this skill to make your own AI app or provide services to a business.

We have created this guide to ease your learning journey on how to develop AI applications. In this article, you will go from understanding the basics of artificial intelligence to learning the process of making an AI app. So without further waiting, let’s dive into it.

What is Artificial Intelligence?

In simple words, artificial intelligence is the ability of a computer to mimic human actions, including creative thinking, speaking, etc. AI is hyped a lot nowadays, but it has been around for decades. The term, ‘Artificial Intelligence’ was coined by John McCarthy in 1955. He defines it as: “The science and engineering of making intelligent machines”.

Chat GPT, a chatbot made by OpenAI, has commercialized the use of AI. Now, almost all major tech companies are shifting their focus to AI. This is mainly due to two main reasons:

• We have more data nowadays, which is required to train AI models, and
• Technology has improved a lot.

Before trying to learn how to create an app using AI, it is important to learn more about the AI itself. This will help you be more creative and make the process easier.

Main Fields of AI

Artificial Intelligence has many subsets. A paper published in 2022 suggests that there are six main fields of AI. Strengthening your basic knowledge about these fields will help you in learning how to build an AI app. So let’s take a look at them one by one.

1. Machine Learning

Machine learning is often considered the equivalent of AI but the truth is — it’s a subset of artificial intelligence. ML helps a system to learn from examples (or data) without being programmed all of the time. This way, it gives the computer the ability to understand and deduce, similar to humans.

For example, an AI system can be trained to recognize an image of a cat by showing it different pictures of a cat. These pictures can either be labelled or not. This means that ML can be achieved through supervised learning or unsupervised learning. It can also be done through reinforcement learning, which is often used to train pets.

2. Deep Learning

Deep learning is another subset of artificial intelligence. However, it is different from machine learning as it uses artificial neural networks that are inspired by the structure of the human brain. It is used to learn and understand complex patterns and relationships within large amounts of data.

Image recognition would be a great example of deep learning. Deep learning is used to train AI models on large datasets of labelled images. Neural networks learn to recognize patterns and features within the images. This enables the system to accurately classify objects in new, unseen images.

3. Neural Networks

Neural networks are a part of deep learning. However, they are an important subset of AI. It consists of layers of nodes or neurons, each processing input data. The network learns by adjusting the weights and biases based on the errors of its predictions during training. This makes it easy to recognize patterns and make predictions on new, unseen data. Therefore, it is beneficial for tasks like image recognition.

4. Natural Language Processing

Chat GPT, Bard, Gemini AI, and other AI chatbots exist due to one technology — NLP. NLP stands for Natural Language Processing, NLP focuses on the interaction between humans and computers through natural language (not binary codes). Because of this, AI chatbots can understand the questions and respond in natural language.

5. Robotics

Artificial intelligence and robotics are often mixed. But robotics is another subfield of AI because many robotic applications require AI techniques. AI in robotics is used to handle tasks such as object manipulation, motion planning, localization, and mapping. It’s the AI that gives the robots the ability to perceive their environment, make decisions, and perform actions to achieve specific goals.

6. Computer Vision

Computer vision trains computers to understand and interpret the visual world. This includes acquiring, processing, analyzing, and understanding digital images to extract visual data from the real world. This technology is used in daily activities such as facial recognition and for sophisticated uses like medical image analysis and autonomous vehicles.

What is an AI app, and why should you make one?

An AI app is a software program that uses AI to perform specific tasks. These tasks can range from simple/repetitive tasks to complex tasks that require human-like intelligence. Nowadays, AI apps are used in various fields, such as chatbots, image editing, language learning, etc.

For example, Chat GPT has an app that can converse with users, answer given questions, and generate new text. Initially, it could only process text-based input, but now it can process, understand, and reply in the form of voice and even images.

Examples of Successful AI Applications

Here are some other successful AI applications that will help you when you build AI apps:

• Bing: Microsoft Bing was originally a search engine. But now, Microsoft has introduced a chatbot — similar to Chat GPT — in this app. This chatbot can respond to questions using text or images, translate words, and proofread writing.
• FaceApp: FaceApp became popular for its ability to edit selfies in creative ways. This app uses the power of artificial intelligence. It allows users to adjust features on selfies like makeup, hairstyle, and facial hair, or even change physical features like face shape, gender-based features, age, etc.
• Voice Assistants: Siri, Alexa, Google Assistant, and many other virtual voice assistants are powered by AI. They can perform tasks ranging from setting reminders to sending messages and providing recommendations.
• Recommendation Systems: Applications on many platforms, like YouTube, Netflix, and Amazon, use AI to recommend content based on user behaviour.

Tip: Try to study the workings and case studies of existing AI apps to understand and learn how to build an AI app effectively and efficiently.

Why You Should Learn to Make an AI App

Since AI is being integrated into most of our daily tasks, it can be incredibly beneficial to know how to create artificial intelligence apps. It can open up a world of possibilities for creating solutions that can automate tasks, analyze data, and make predictions.

There is a growing demand for AI in various industries. Hence, having this skill can enhance your career prospects. Knowing how to create an AI app may lead to opportunities in high-paying jobs.

How to Build an AI App? Step-by-Step Guide

Building an AI app is a long and sophisticated process. There are four major steps in building an AI app: research, development, testing, and evaluation. Each step can be broken down into two to three more steps. This will help you learn how to build an AI app from scratch.

Step 1: Research

The research phase is the foundational step in creating an AI application. This is the stage where you have to lay the groundwork for your AI project. You need to identify and understand the problem you want to solve, explore existing solutions, and identify what steps to take.

To make the research phase easier, you can break it down into different steps.

1. Define the Problem

The first step in any AI project is to clearly define the problem you are trying to solve. In simple words, why are you trying to make this app? What problem would it be able to solve? This could be a business problem (e.g. ‘we want to reduce customer churn’) or a more specific AI problem (e.g. ‘we want to build a chatbot that can answer customer queries 24/7).

The key here is to be as specific as possible about what you want your AI app to achieve. Having a clear picture, in the beginning, will guide your decisions about which AI techniques to use and how to elevate your app’s performance to the next level.

Try to answer these questions to get a clear view of what you want:

• What is the specific problem that this AI app is supposed to solve?
• Who will be the users of this app?
• What is the goal or desired outcome of this AI app?

2. Identify Existing Solutions

Once you have defined your problem, the next step is to identify existing solutions. This involves researching how others have tried to solve similar problems. By doing this, you’ll be able to learn from their successes and failures.

To identify existing solutions, you can read academic papers, case studies, blog posts, or talk to experts in the field. Studying existing solutions can not only help you avoid reinventing the wheel but can also give you ideas for innovative approaches and perspectives to your problem.

3. Choose the Right AI Model

After studying existing solutions, you’ll need to choose the right AI model for your problem. There are many different types of AI models, each with its strengths and weaknesses. Choosing the right model depends entirely on your problem statement.

For example, if your app involves recognizing images, you could choose a convolutional neural network (CNN). It is particularly good at processing visual data. But if your app involves understanding natural language, you may choose a recurrent neural network (RNN). RNN is designed to handle sequential data like sentences.

Step 2: Development

Once the research phase is complete and the problem has been clearly defined, you can move on to the next crucial step: development. This is where the theoretical knowledge gained from research is put into practice. Let’s break this step down into sub-steps for more clarity.

1. Gather and Preprocess Data

AI models learn from data. So the first step in the development phase is to gather relevant data. This data could be gathered from public datasets, proprietary data, or data generated by users. Once the data is gathered, it needs to be preprocessed.

This is done to make the data suitable for use by the AI model. This involves cleaning the data (removing irrelevant information), normalizing it (scaling numerical data), or encoding it (converting categorical data to numerical data).

2. Model Training and Optimization

Using the preprocessed data, you can now train your AI model. Training involves feeding the data to the model and allowing it to adjust its internal parameters to better predict the desired outcome. Then, the model is optimized. This involves fine-tuning the model to improve its performance and efficiency.

3. User Interface Design

The user interface or UI is the point of interaction between the user and the AI application. It needs to be intuitive, responsive, and user-friendly. The design should make it easy for users to input data, understand the AI’s response, and navigate through the app. In this step, you’ll have to create wireframes, mockups, and prototypes. You’ll also have to test your UI designs with users to gather feedback and make improvements.

4. Backend Development

Training the computer to mimic human behaviour is not the only thing that’s required to create an AI app. You also have to set up servers, databases, and APIs. You’ll have to interface the AI models with the back end to ensure that the application can handle multiple requests and operate smoothly. In this step, you should also consider implementing security measures to protect the application and the user’s data.

Step 3: Testing

Testing before deploying the AI app is as essential as making it. There you should never consider this step to be unimportant. To test the app, you can use a separate testing dataset. It’s kept different to ensure that the model’s performance is not due to memorizing the training data. To gauge the performance of the app, it’s good to use various metrics like accuracy, recall, precision, and F1 score.

If the testing indicates that something is wrong, take the adjustment measures. If adjusting the parameters doesn’t work, then go back to the development step. However, gather and preprocess the data differently.

Step 4: Evaluation

Lastly, you need to assess your app in a real-world context. This includes deploying the AI model, monitoring, gathering feedback, and making improvements.

1. Deploy the AI Model

Deploying the AI model into an app means integrating the trained AI model into a custom software application. Backend development, UI design, training of AI models, and other prerequisites were completed in step two. Therefore, deploying the model should be easy. After this, you can either launch the application on AI apps or deliver it to your client.

2. Monitor Performance and Gather Feedback

It is important to keep on checking the performance and taking necessary steps after launching the app. You can ask users for feedback to get valuable insights. This will help you to understand how well the app is performing. If users register any complaints, make sure to take immediate action.

How Can Debut Infotech Help in Making an AI App?

Debut Infotech is a renowned name in the field of AI applications. We provide businesses and individuals with the tools and expertise they need to build an AI app. Moreover, Debut Infotech offers AI consulting services, guiding companies on how to build an AI app and integrate it seamlessly into their existing processes.

From facial recognition to object detection and image classification, Debut Infotech’s offerings are diverse and cutting-edge. We help you make AI apps that mimic human decision-making.

Conclusion

Building an AI app is not just a technical endeavour but a journey of innovation. The most important step is to know what problem you are aiming to solve.

Then, you have to take several steps, from selecting suitable AI models, gathering and preparing data, training, and finally integrating AI into the app. As we look towards the future, the question is not just about how to make an app with AI but how to do so in a way that’s beneficial for all.

But before moving on to this journey, we highly recommend you strengthen your basic knowledge. To learn more about AI development services, you can read other blogs on our website. You can also contact Debut Infotech’s team for consultation. Our team will guide you through the process step by step — in more detail.