There are
about 8 million applications available on Android and iOS platforms Today,
mobile applications are changing people’s lives and becoming a crucial part of
our daily lives, forcing developers to find new ways to improve user experience
and gain a competitive advantage.
In this
context, big data is a great step to obtain insights for business
decision-making. However, without Artificial Intelligence, managing large
amounts of data can be a challenge. Therefore, AI applications are as necessary
as big data analytics tools to detect patterns and provide cognitive
capabilities for huge amounts of data.
By
working with AI and big data, companies can obtain valuable information that
makes them more competitive in this large market. To correctly understand what
we will explain below, let's first look at some definitions:
- AI App
Development: By using machine learning algorithms to provide recommendations
and using natural language processing, AI app development creates mobile or web
applications that use artificial intelligence to improve the user experience.
- Big
data: Big data technology processes all collected data to derive relevant,
real-time business insights associated with productivity, profits, users, and
more. Its fundamental help lies in allowing large amounts of information to be
scalable and enhanced.
Available tools and how to implement them
In every
business and in custom software development company, being
aware of new trends is important. For mobile apps, it's even more important to
be on the cutting edge of AI tools and how to implement them. Let's look at
some of them:
1. ChatGPT
At this
point we know that ChatGPT needs no introduction, but for those who have not
known it yet, it is an AI assistant that is a powerful tool that can help you
accelerate the development of mobile applications by interacting in dialogue
with users to solve various problems or locate UX design tools.
ChatGPT
provides a level of precision that helps streamline content generation
operations, increasing productivity by helping professionals redistribute their
time as efficiently as possible.
How can
it be implemented?
- To
create short code snippets and generate code base.
- To help
you debug your code, that is, find and fix errors in your application code.
- To
generate ideas for features for your application.
- To
prioritize features and decide which ones you will include in your minimum
viable product (MVP).
2. CoreML
CoreML is
all about integration. It's a framework from Apple that you can use to
integrate machine learning (ML) models into your iOS app more easily, using a
simple drag-and-drop interface for easy use.
How can
it be implemented?
- To
create your ML model for your application.
- Create
a machine learning function to create a custom model based on your data.
- To
import models from third-party training libraries.
- To add
functionality to your application, such as text processing and analysis,
converting audio to text, image recognition and analysis, and sound analysis.
- To incorporate
functions such as: sentiment analysis, audio transcription, facial recognition,
barcode recognition and others.
3. Github Copilot
Known as
a coding assistant, it has the ability to convert natural language prompts into
coding suggestions when working with a different programming language, such as
Python, Javascript, Swift, and C++. Although it does not test code, its
greatest strength is automating the writing of repetitive code, saving
engineers time and effort that they can use to focus on more complex tasks.
How can
it be implemented?
- To
integrate into the integrated development environment (IDE) that you are using
to develop your mobile application in a custom software development company
- To offer
AI-based suggestions based on the context and style conventions of your code.
4. Mintlify
We all
know that clear and understandable code documentation is important, but we also
know that creating documentation can sometimes be the most difficult task that
software engineers have to face and that’s why many of them choose Mintlify
How can
it be implemented?
- To
automate the creation of code documentation.
- To scan
your code and generate documentation to explain it, minimizing the risk of
human error.
- To produce
legible and understandable documentation.
5. ML Kit and Gemini integration
ML Kit is
a robust solution if you are looking to integrate machine learning into your
mobile application. As Google's easy-to-use app development kit (SDK), it
offers mobile-optimized, production-ready ML models, allowing you to improve
your app with just a few lines of code. Compatible with Android and iOS
devices, ML Kit also supports offline functionality, ensuring that machine
learning features, such as data processing, remain on the device, improving
privacy and efficiency.
The
introduction of Gemini, Google's latest and most advanced AI model, is set to
further revolutionize application development, working in synergy with ML Kit.
Gemini brings multi-modal capabilities, meaning it can understand and process
various types of information, such as text, audio, images and video. This
versatility paves the way for creating more sophisticated applications that
offer multifaceted user interactions.
Highlights of Gemini's impact include:
- Diverse
applications: Gemini can be applied in different sectors, such as personalized
education, health, consumer experiences and creative industries, offering wide
utility.
- On-Device
Applications: The Gemini Nano model is tailored for on-device applications,
enabling efficient, real-time AI processing on mobile devices and edge
computing.
- Next-generation
performance: With Gemini Ultra achieving outstanding results across multiple
benchmarks, improvements in accuracy and efficiency are seen over existing AI
models.
- Developer
Accessibility: Gemini Pro, built into the Google AI SDK and accessible through
the Gemini API, simplifies the development of AI-powered application features.
- Quantum
Computing and IT Impact: As a significant advancement in quantum computing,
Gemini has the potential to transform IT and cybersecurity applications.
How to use AI for mobile development
AI has
become an essential tool in the development of mobile applications and its
future is closely related to rapid advances in artificial intelligence such as,
for example, personalization and recommendation.
This
promises immense potential to shape the app development landscape and transform
user experiences, but how is it used? Here are some examples:
1. Personalization:
Personalized experience has become everyone's major demand. AI algorithms helps
us to achieve personalization by analyzing user data such as heir behavior,
preferences and context. Because of this, applications become more intuitive
and user-centered, helping to deliver a unique, personalized experience for
each user.
2. Natural
language processing: This advantage allows users to interact with applications conversationally
to improve convenience and accessibility.
3. Improving
user engagement through recommendations – we've all been there. We don't know
what we want, but suddenly the app recommends something that seems interesting
to us. This is where AI-powered recommendation engines come into play. These
analyze user behavior, purchase history, and preferences to generate
personalized recommendations, thereby improving user engagement with the app
and increasing conversion rates.
How to scale your app with AI
Over
time, it became evident how artificial intelligence has revolutionized the
mobile application development industry. These powerful software development services emerged as a significant change, improving user experience
and machine learning.
Tips for scaling your app with AI
- Reduce
manual tasks: Now, AI speeds up the development process and saves developers
time by generating code snippets.
- Improve
testing and quality assurance: With the help of AI-powered algorithms, you can
automatically simulate real business conditions, detect and correct errors to
improve the performance of your application and ensure proper testing across
different machine operating system settings
- Improved
collaboration: With AI-based collaborative tools, you can provide features like
real-time code review, automatic documentation generation, and intelligent task
management. Communication and collaboration between development teams is
productive and efficient.
Let's see
how you can scale your application through machine learning as a subset of AI:
- Predictive
Analytics: With the help of Machine Learning algorithms, analyzing big data is
easier. This helps identify consumption patterns and trends and through
predictive analytics, it is simple for developers to predict user preferences,
forecast demand, and optimize app features for better user engagement.
- Intelligent
personalization: AI-powered recommendation systems and personalization algorithms
help deliver highly personalized experiences.
As AI app
development becomes a way to take your app to the next level, it’s important to
do it right and have a scalability plan.
Here are
some steps you can take to ensure you can scale your AI solutions:
1. Use
cloud-based AI platforms to manage your models, such as AWS (Amazon Web
Services) AI, Azure AI, and Google Cloud AI, to handle a large surge in demand.
2. Design
your AI systems to be modular, to upgrade or replace individual components
without having to overhaul the entire system.
3. Invest
in Edge AI to reduce latency and load on your central servers.
When it
comes to the future of mobile apps, big data plays a vital role, due to its
simple performance and advanced features. This has already become an essential
part of the mobile application industry.
Author Bio:
Glad you
are reading this. I’m Yokesh Shankar, the COO at Sparkout Tech, one of the primary founders of a highly creative space. I’m more
associated with digital transformation solutions for global issues. Nurturing
in Fintech, Supply chain, AR VR solutions, Real estate, and other sectors
vitalizing new-age technology, I see this space as a forum to share and seek
information. Writing and reading give me more clarity about what I need.
