Artificial intelligence, or AI, has driven enterprises to deliver robust app and software solutions. This technology has recently gained momentum through Chatbots, smart home assistants, driverless cars, and more. AI lets you do a lot of things. For example, AI, with the help of machine learning and deep learning, can help you create an app that drives more personalized solutions.
Imagine developing a mobile app that can understand natural language, recognize images, or even make predictions based on user data. With the rise of AI, these possibilities are becoming a reality.
This blog post will explore the advancement of AI with React Native to create smart mobile applications. We will also understand AI, ML, and Deep Learning and the steps and best practices in using these technologies.
Table of Contents
Artificial Intelligence (AI)
Does artificial intelligence require any introduction? We all have seen the power and capabilities of AI in sci-fi movies like Terminator, Iron Man, and Robot. But today, we know and use AI in Siri, Alexa, and Google Home. AI is everywhere today. AI is a highly advanced technology that helps machines learn and perform tasks similar to humans.
Machine Learning (ML) in AI allows systems to learn from data and respond with improved performance. In contrast, Deep Learning (DL) is a superior ML tech that uses the layered network to handle complex tasks like image recognition. Together, these technologies help enterprises create more innovative, personalized apps.
Machine Learning (ML)
Machine learning, or ML, is a subset of AI that analyzes data and human behavior. Machines here learn from past datasets and improve their knowledge and capabilities. Similarly, humans learn from previous examples to gain knowledge and forge a new path.
Machines can simultaneously understand, interpret, and analyze different patterns and data sets, leading to improved solutions. Machine learning enables machines to learn and replicate human thinking and actions. It acquires intelligence from human input and uses this knowledge to enhance productivity.
Machine Learning models comprise two models:
Training Model: This model helps the machine to learn from provided data and information.
Test Model: It tests the algorithm created from the training data to ensure they work correctly.
Deep Learning
Deep learning is recognized as an advanced subset of machine learning (ML) that analyzes data more deeply and accurately, using structured and unstructured data to improve algorithms. In deep learning, machines study and learn from human behaviors and accommodate new patterns.
Technologies like virtual assistants, face recognition, and driverless cars are examples of deep learning.
Why Must You Utilize AI and ML for Your Application?
It is always beneficial to harness the potentiality of AI and ML to build an app. The combination helps with the following:
Human-like Behavior: When machines learn and imitate human-like behavior and actions, they automate many time-consuming and effort-consuming tasks. This enables productivity and leads to cost-saving.
Personalized Offerings: The advancement of AI and ML leads to personalized offerings, which enhance customer experiences and client relationships. As a result, clients and customers feel valued, leading to long-term partnerships and better ROI.
Boost App Interaction: AI enhances app interaction, boosting engagement and conversion, leading to high-end business growth.
How do You Develop an AI App With React Native?
Here’s a breakdown of the steps you need to leverage to develop a high-performing React native App.
Pre-requisites:
You must access some essential deep learning tools to ensure top-notch AI app development solutions.
TensorFlow:
Google developed TensorFlow an ideal deep learning tool that helps machines handle and perform tasks via neural network training. It lets you save a binary file with a trained model. Plus, instead of building a network from scratch, it lets you use pre-trained models like Inception and an image classifier to make your model.
Inception:
Another pre-requisite tool developed by Google that is used for image classification. It has been trained on nearly 2000 images, making it highly effective for numerous image recognition tasks.
Let’s look closer at the development using React Native and TensorFlow.
Use the following components to integrate AI capabilities in your React Native app:
TensorFlow for React Native: npm i @tensorflow/tfjs-react-native
React Native Caffe2: npm i react-native-caffe2
React Native CoreML: npm i react-native-coreml
React Native Image ML: npm i react-native-core-ml-image
These tools and components will help you build an intuitive AI-powered app using React Native, ensuring high-end user experience and application functionality.
Steps to Build an AI-based App Using React Native
Step 1: Configure the Project
Create a file named “rn-cli.config.js” in the root directory of your mobile app project and configure it to include the TensorFlow model using the following code:
module.exports = {
getAssetExts()
return [‘pb,’ ‘txt’];
}}
pb (model file): extension of the output model
txt (label file): extension of the label file
Step 2: Add TensorFlow to Assets
You must place the TensorFlow model file in your application’s assets folder to enable image recognition. This will allow the image recognition functionality to access and utilize the TensorFlow model for accurate and efficient image recognition.
tensorflow_inception_graph.pb
Step 3: Add Label File to Assets
Next, include the label file in the assets directory:
tensorflow_labels.txt
Step 4: Initialize the tfImageRecognition API Class
Now that you have a model and label, you can initialize the `tfImageRecognition` API class using the following card:
const tfImageRecognition = new tfImageRecognition({
model: require(‘./assets/tensorflow_inception_graph.pb’),
labels: require(‘./assets/tensorflow_labels.txt’),
});
Step 5: Call for Recognition Function
The last step is to train the model to recognize the image in front of you using the training set. Here’s the code for the same:
const results = await tfImageRecognition.recognize({
image: require(‘./assets/panda.jpg’),
});
Hire React Native developers to use TensorFlow’s deep learning capabilities and optimize the training models. It will ensure that your React Native app accurately identifies and matches images, leading to high precision and performance.
Best Practices for an Optimized AI App Development
Every enterprise and developer adheres to unique app development requirements and methods. However, they often require expert guidance to get going and find the best possible solution. Here’s a list of the best AI app development practices you can follow for a seamless AI-driven app solution:
Select an Efficient Code Editor: Access to an efficient code editor will ensure seamless coding and commenting. Sublime Text is a great choice, but you must adhere to the flexible and robust one.
Analyze Raw Data: Understanding raw coding data will improve your coding skills. It will also help you create practical training sets and enhance AI models. When you run detailed data analysis, it helps enhance algorithms and outcomes.
Focus on User-centric Design: Create features that give users control and clarity. Remove unnecessary elements and ensure your design meets user needs. This will result in a more intuitive and satisfying user experience.
Proper Utilization of Python Dictionaries: Python dictionaries are great for storing data and connecting unique keys with values. This makes managing data easier by simplifying code and reducing the need for complex conditional statements.
Use Machine Learning Tools: You must use advanced machine learning tools to ensure proper enhancement of your AI-powered App. Consider using the following tools:
- Apple Core ML (Integrates machine learning models)
- Natural Language Processing (NLP) for processing human language.
- GameplayKit for AI in games.
- Caffe2 (A deep learning framework)
- C++ for performance-critical tasks.
- Python API for flexibility and integration.
- TensorFlow (A powerful tool for deep learning).
Low-Code Platforms: Using low-code platforms will increase productivity and app quality. These platforms ease collaboration and streamline the development process, allowing for the efficient creation of AI-driven solutions.
Final Thought
If you embark on creating an AI-driven app using React Native, this blog is an ideal reference point. You must start training the sets using machine learning and deep learning tools. You can create an outstanding app that stands out in today’s competitive market by integrating advanced coding solutions, delving into raw data, and leveraging low-code platforms.
If you seek professional help to build a next-gen AI-powered React Native App Metizsoft is your one-stop destination. We specialize in the latest technologies and trends in React Native app development. Our team utilizes advanced AI and ML methodology to create intuitive apps that drive conversion. Conduct a one-on-one AI in React Native consultation suitable for your AI-based app project requirements and enable a comprehensive and customized solution.
