AI applications are continuously changing how people do things. Elon Musk, CEO of Tesla, recently claimed that there will be no jobs in the future because AI will be doing everything. Today, you can use AI to create stunning art (Midjourney), for financial analysis (Domo), to create YouTube videos (Pictory), and to do tons of things. It’s only standard that you learn how to develop AI applications.
While the market is emerging, it’s enormous, and that’s why companies now wonder how to develop AI applications of their own (learn more with the help of Toplight research). OpenAI (Microsoft-backed AI company has a market valuation of around 80 billion dollars. It’s time to learn AI mobile app development and its nuance, including tech stacks, testing, maintenance, and marketing.
AI Mobile App Development: Tech Stack
Programming Languages: Python is the go-to language for AI, known for its extensive libraries and support. Another language, R, is ideal for data analysis and visualization.
Machine Learning Frameworks: Frameworks like TensorFlow, developed by Google, are great for deep learning. PyTorch is known for its flexibility and ease of use. Keras simplifies deep learning, making it more accessible to developers.
Data Management: Databases store and manage data, such as PostgreSQL or MongoDB. Additionally, Amazon Redshift helps with data analysis.
Data Preprocessing: Tools like Pandas are helpful for data integration, while NumPy is handy for number crunching.
Model Development: Jupyter Notebooks function like interactive notebooks for testing ideas. Integrated Development Environments (IDEs) provide a workspace for coding. Model development is crucial in AI app development.
Machine Learning Ops (MLOps): Docker is essential for packaging AI models neatly. Kubernetes helps manage these packages, and Continuous Integration/Continuous Deployment (CI/CD) tools automate model deployment.
Cloud Services: Leading cloud providers like AWS, Azure, and Google Cloud offer AI services and scalable infrastructure. Heroku is a platform for app deployment.
Version Control: Git is like a track changes feature for your code, allowing multiple team members to collaborate and track changes seamlessly. Git is incredibly essential for collaborative developmental processes involving many parties.
Development Libraries: OpenCV is essential for working with images, while Natural Language Processing (NLP) libraries are used for text analysis. Scikit-learn is a valuable tool for traditional machine learning, TML.
Related: Artificial Intelligence Jobs
How to Create an AI App: Hiring Talents
There are certain things to look out for when you want to make an AI app for Android and iPhone. Let’s explore the nuances of hiring developers to undertake your project, from concept to launch.
Understanding the nuance of the project: AI application development is a niche, and you need developers with an immense understanding of programming languages like Python and R. Also, the industry within which your AI app operates will determine compliance and scalability.
It’s also essential to access the skillset of potential hires for your project. You need developers with machine learning, deep learning, and data science experience. You also need developers with proficiency in frameworks like PyTorch and TensorFlow.
Other factors you need to consider are ethical awareness (a developer’s awareness of the ethical implications of AI and a safe AI developmental process), problem-solving skills, communication (the ability of the developer to communicate with broader team members), and cultural fit.
Testing Your AI Application for Vulnerabilities
It’s critical to test your AI application for vulnerabilities before launch. Roll out a limited edition of your application for users to test. Set up portals for them to fill out reports on the application’s functionality, including the operation efficiency of features. As they report on the strengths and weaknesses of your mobile application, you can plug vulnerabilities and fortify your app before the final app launch.
Depending on the platform you created the app for, launching can be done on Android or iOS stores. Artificial Intelligence application development does not end in the launch phase. Post-development issues can be a hassle, so you should be prepared for whatever comes.
Apps often perform excellently until the user base increases drastically. Application scalability is a thorough process that ensures that an app retains its original operation efficiency even though the user base has drastically increased. The maintenance team monitors backend functionality as it relates to frontend demands by customers, storage, security, traffic, and other related issues.
AI application marketing depends on many factors, including the market, industry, trend, and UVP. Let’s explore the market and trend:
Market: The market comprises competition and placement of the application. For example, AI applications for Telehealth services might not face stiff competition since there aren’t, strictly speaking, immense innovative applications within this sphere. Besides telehealth and telemedicine apps, many of which still need to be stifled by compliance and regulatory hurdles, AI apps in healthcare are few.
Trend: What does the market say? Is there a trend towards adopting AI applications in the industry you are trying to break into? Is there a future trend in place? Is your AI mobile application revolutionary? Does your application serve a purpose? Artificial Intelligence provides robust machine learning and deep learning capabilities to simple applications, turning them into intelligent entities. While this is exciting, you need to define your core service and how AI will help you make that core service better. Today, companies like Adobe are adopting AI and machine learning capacities to make their software smarter. Is your smart software necessary?
In conclusion, the world of AI mobile app development offers immense opportunities. You can create and maintain a successful AI app with the right tech stack, skilled developers, thorough testing, and a keen eye on market trends.
Remember, while AI is exciting, ensuring that your application serves a meaningful purpose and adapts to evolving trends is essential. Stay innovative, ethical, and user-focused for long-term success in this dynamic field.