Designing an app that meets your unique business needs can feel like looking for a needle in a haystack. You can customize existing applications, but they typically don’t fit quite right. Building an app from scratch is no small feat, but it allows you to create a solution tailored to your specifications. If you’ve considered going this route, you may have quickly realized that this is no easy endeavor. There’s a lot to unpack. This article will explore how to create your own AI application. You’ll learn how to successfully design, build, and deploy a custom AI application that meets your business needs, drives innovation, and delivers measurable results.
Lamatic's generative AI tech stack will help you achieve your goals. Our solution's templates, components, and modules simplify the process of creating your own AI application so you can focus on building a tool that meets your unique objectives.
What Is AI Mobile App Development and Why Its Important
AI mobile app development integrates artificial intelligence technologies into the app development process to create apps that can learn and adapt to user behavior. Building an AI-driven mobile app differs from traditional development because it requires advanced expertise, particularly in machine learning.
While conventional apps have fixed features, AI applications improve these features over time as they gather more data on users and their preferences.
Cool Examples of AI Apps You Can Build
There are many types of AI mobile apps to consider building. Some popular examples include:
- Chatbots
- Writing tools
- Generative AI apps
- Agents / Virtual assistants
Understanding the Basics of AI
AI is a subfield of computer science that aims to develop systems that can perform tasks that typically require human intelligence. These include:
- Learning
- Reasoning
- Problem-solving
- Perception
- Language understanding
At the heart of AI are machine learning and deep learning. Machine learning uses algorithms to parse data, learn from it, and make informed decisions based on what it has learned. Deep learning, a subset of machine learning, uses structures called neural networks that mimic the human brain’s operations.
The latest AI advancements, like OpenAI’s GPT series and Google’s BERT, have significantly pushed the boundaries in natural language processing, enhancing how machines understand human language.
Real-World Applications of AI
Real-world applications of AI systems are wide-ranging. Below, you can find the most common examples of AI in daily life:
Speech Recognition
Also known as automatic speech recognition (ASR), computer speech recognition, or speech-to-text, this capability uses NLP to process human speech into a written format. For example, Siri utilizes speech recognition to conduct voice searches.
Customer Service
Increasingly, more companies are turning to online virtual agents for customer service, thus replacing human agents. According to Servion Global Solutions, 95% of all customer interactions will involve artificial intelligence by 2025.
Computer Vision
In this case, AI technology allows computers and systems to derive meaningful information from digital images, videos, and other visual inputs. Its application is photo tagging on social media.
Discovery of Data Trends
AI algorithms can use consumer behavior to discover data trends, allowing companies to build effective cross-selling strategies and offer relevant add-on recommendations during checkout. That’s where predictive analytics software steps in.
Such software allows real-time decision-making with your data. For instance, the software can generate risk assessment models for fraud and risk detection, targeted advertising, and product recommendations.
Fraud Prevention
One of the primary problems that artificial intelligence tackles is payment and sensitive information fraud. Companies utilize AI-based systems to detect and prevent this type of fraud effectively.
Automated Stock
Trading AI-based high-frequency trading platforms make thousands or, sometimes, millions of trades each day. As of 2020, half of stock market trades in America were automated.
According to Allied Market Research, the global algorithmic market is forecast to reach $31.2 million by 2028.
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Tech Stack for AI Application Development
A tech stack combines programming languages, frameworks, libraries, and tools used to develop applications. Selecting the right tools to create your AI application can boost performance, enhance scalability, and simplify development.
Of course, no universal tech stack fits every AI mobile app development case. Our team exclusively determines the technologies for any project, considering requirements, desired features, and best development practices.
Key Components of a Typical AI Tech Stack
Let’s look at the core components of an AI tech stack in more detail.
Programming Languages, AI Platforms, and Libraries
Most artificial intelligence applications you see worldwide were written in one of these programming languages. Let’s take a look at them one by one:
Python
Python is considered a great programming language for beginners. But being simple to understand isn’t the only strong side of it. It is also a powerful tool for data analysis, with a great number of libraries to analyze and classify large datasets, making it perfect for AI solutions.
Java
Java is one of the most popular programming languages, so it’s no wonder programmers use it in AI development. In addition to its simple debugging and maintenance, Java is multi-platform, which is great for mobile app development. Most mobile apps are written in Java, and they include AI-based applications.
C++
The biggest advantage of C++ is its speed and efficiency. Since many AI applications are complex, using a programming language that can execute code fast will improve the speed of AI processing. However, speed and efficiency come with a complex code structure.These are the most popular programming languages. In some cases, engineers can also turn to C#, R, Lisp, or Prolog. To optimize development costs, our software engineers use third-party AI and Machine Learning (ML) platforms, including the most popular ones:
Google TensorFlow
It is an innovative data science environment for numerical computation that uses dataflow graphs and creates AI-based projects from idea to launch. The functional and portable TensorFlow has a flexible architecture that allows computing to be deployed across multiple processors applying a standard API.
Microsoft Azure
The platform’s advantages are a wide range of algorithms, advanced analytical mechanisms, and high-quality multilingual documentation. AI/ML capabilities include:
- Predictive modeling
- Recommendations mechanism
- Natural language processing
- Pattern recognition and other services
Amazon AWS
It helps create, train, and deploy models and intelligent apps of any complexity. The platform is based on simple, scalable, and flexible ML technology, which is used, among other prominent engineers, by scientists from the Amazon community.Other trustworthy AI platforms include:
- IBM Watson
- Oracle AI cloud services
- The Mendix low-code platform
- H2O
- Wit.ai
Frameworks and Application Programming Interfaces (APIs)
AI, ML, and deep learning frameworks greatly simplify the development of complex, high-tech products and allow you to use third-party functionality. The most popular options are:
Microsoft Cognitive Toolkit (CNTK)
It helps create various ML models, including recurrent and convolutional neural networks. Using the framework, we quickly process arrays of unstructured data, choose metrics and algorithms, and train machines to think almost like people.
AWS Machine Learning
It assists in the development of highly complex, high-performance applications. It can also connect applications to cloud services and create forecasts using APIs.
PyTorch
Combining the ML library, pre-trained models, and scientific computing structure allows us to quickly build complex systems such as deep neural networks. The Python-powered framework provides excellent flexibility and efficiency.
Core ML/Create ML
Are well-suited for developing AI products for Apple devices in particular.
Caffe2
It helps build modular deep-learning environments. ML’s open-source set of algorithms enables engineers to experiment with different models.
Exploring Popular AI Frameworks and APIs for Optimized Application Development
Other popular framework options are:
- Keras
- Accord.NET
- scikit-learn
- SparkMLlib
Apart from frameworks, we use third-party APIs and software development kits (SDKs) )to speed up and optimize software development. The options include:
- Azure Text Analytics API
- Microsoft Face API
- Google Vision API
- Apple SiriKit and others
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- Best Generative AI API
- AI Integration Strategies
- AI Integration Tools
- Best AI APIs
- Benefits of APIs
How to Create Your AI Application from Scratch
1. Identify the Problem You Want to Solve With AI – Define Your Objective
Start by defining your goals. Ask yourself, “What specific problem will AI solve?” For example, you could reduce patient wait times with an AI scheduling system in healthcare. While defining your goal, set measurable targets, such as cutting patient wait times by 20% or 30%. Clear metrics help guide development and measure the AI system’s success.To identify the problem effectively, ensure you investigate and understand the current challenges in the system by analyzing patient flow data and peak times to identify the problem effectively. Keep records of your findings to communicate the objective and strategy to all stakeholders.
2. Collect And Prepare the Data
Now, when you’ve framed the problem, you need to pick the right data sources. It’s more critical to get high-quality data than to spend time on improving the AI model itself. Data falls under two categories:
Structured Data
Structured data is clearly defined information that includes patterns and easily searchable parameters. For example:
- Names
- Addresses
- Birth dates
- Phone numbers
Unstructured Data
Unstructured data doesn’t have patterns, consistency, or uniformity. It includes:
- Audio
- Images
- Infographics
- Emails
The Importance of Data Cleaning and Preprocessing in AI Model Development
You need to clean the data, process it, and store the cleaned data before you can use it to train the AI model. Data cleaning or cleansing is about fixing errors and omissions to improve data quality.
3. Choose a Programming Language
There is more than one programming language, including the classic:
- C++
- Java
- Python
- R
The latter two coding languages are more popular because they offer a robust set of tools, such as extensive ML libraries. Consider your goals and needs to make the right choice.
For example:
- Python is a good choice for beginners as it has the most straightforward syntax that a non-programmer can quickly learn.
- C++ boasts high performance and efficiency, making it ideal for AI in games.
- Java is easy to debug, user-friendly, and can be used on most platforms. It also works well with search engine algorithms and large-scale projects. Java is generally used to build desktop applications.
- R is developed for predictive analysis and statistics. Thus, it’s primarily used in data science.
4. Opt for the Right Platform
Apart from the data required to train your AI model, you must pick the right platform for your needs. You can go for an in-house or cloud framework. What’s the main difference between these frameworks?
The cloud makes it easy for enterprises to experiment and grow as projects go into production and demand increases, allowing faster ML model training and deployment.
In-house Frameworks
For example, you can choose:
- Scikit
- Tensorflow
- Pytorch
These are the most popular ones for developing models internally.
Cloud Frameworks
You can train and deploy your models faster with an ML-as-a-service platform or ML in the cloud. You can use IDEs, Jupyter Notebooks, and other graphical user interfaces to build and deploy your models.
5. Create And Select an Algorithm
When telling the computer what to do, you must also choose how it will do it. That’s where computer algorithms step in. Algorithms are mathematical instructions. It’s necessary to create prediction or classification machine learning algorithms so the AI model can learn from the dataset.
Exploring Different AI Algorithms for Solving Specific Problems in Healthcare
There are several types of algorithms for different kinds of tasks. You could use a combination of each depending on the problem you want to solve:
- Predictive Algorithms: These are designed to predict future results based on historical data. For example, a predictive algorithm could analyze past appointment durations and forecast future demand to reduce patient wait times.
- Classification Algorithms: These algorithms classify data into predefined groups. For example, in a healthcare setting, they could classify patients based on their risk of missing appointments, allowing the system to adjust schedules accordingly.
- Clustering Algorithms: These are used to group a set of objects so that objects in the same group are more similar than those in other groups. This could be useful in segmenting patient types to optimize resource allocation.
- Decision Tree Algorithms: These algorithms use a set of rules to make decisions. For instance, they could decide the best scheduling strategy based on the type of appointment and the time of day.
- Neural Networks: Inspired by the human brain, these algorithms excel in handling complex patterns and large datasets, again, like those in healthcare systems, to improve decision-making and efficiency.
6. Train the Algorithms
To create an AI, you need to train the algorithm using the collected data. It would be best to optimize the algorithm to achieve a high-accuracy AI model during the training process. You may need additional data to improve the accuracy of your model.Model accuracy is the critical step. Therefore, you must establish model accuracy by setting a minimum acceptable threshold. For example, a social networking company working on deleting fake accounts can set a “fraud score” between zero and one for each account. After some research, the team can decide to send all the accounts with a score above 0.9 to the fraud team.
7. Deploy and Monitor
After you’ve developed a sustainable and self-sufficient solution, it’s time to deploy it. By monitoring your models after deployment, you can ensure they continue to perform well. Remember to monitor the operation constantly.
Related Reading
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Start Building GenAI Apps for Free Today with Our Managed Generative AI Tech Stack
Lamatic offers a managed Generative AI Tech Stack. Our solution provides:
- Managed GenAI Middleware
- Custom GenAI API (GraphQL)
- Low Code Agent Builder
- Automated GenAI Workflow (CI/CD)
- GenOps (DevOps for GenAI)
- Edge deployment via Cloudflare workers
- Integrated Vector Database (Weaviate)
Lamatic empowers teams to rapidly implement GenAI solutions without accruing tech debt. Our platform automates workflows and ensures production-grade deployment on edge, enabling fast, efficient GenAI integration for products needing swift AI capabilities. Start building GenAI apps for free today with our managed generative AI tech stack.
What are the Benefits of Using Lamatic?
Using Lamatic to build your own generative AI application offers many benefits.
- The platform is designed for speed and efficiency: Their automated workflows minimize tedious tasks that stall progress and can lead to mistakes.
- Lamatic also emphasizes production-grade deployments: This means you won't have to waste time fixing bugs and errors after you launch your project. Instead, you can focus on your application’s unique features and capabilities.
- Lamatic helps teams avoid tech debt: Their solution provides everything you need to build, deploy, and operate your GenAI application, ensuring you can get to business quickly without accruing unnecessary technical liabilities.
