AI App Building Guide Without Code

SCIENCE AND TECHNOLOGY

Samir Kumar Mandal

7/9/20246 min read

Introduction:

Creating an AI app can seem difficult. But with the right steps and tools, anyone can do it without writing code. This guide will show you how to build an AI app from scratch using no-code platforms. We will use simple words and short sentences. Let’s get started!

Step 1: Define Your Goal

First, decide what you want your AI app to do. Is it for image recognition, language translation, or something else? Having a clear goal helps you plan better.

Step 2: Choose a No-Code Platform

Next, pick a no-code platform to build your app on. Some popular platforms are:

  • Bubble: Great for building web apps without coding.

  • AppSheet: Easy to use and integrates with Google Sheets.

  • Adalo: Good for creating mobile apps with a drag-and-drop interface.

  • Wix: Offers AI tools for building websites.

Example Interface: AppSheet

Let's take a look at AppSheet's interface as an example. This image shows how you can create an app using a simple, visual interface

  1. Start a New Project: Click on "Start a New App" to begin creating your AI app.

  2. Data Source: Choose where your data will come from, such as Google Sheets, Excel, or a database.

  3. App Design: Use the drag-and-drop interface to design your app's layout. You can add buttons, forms, images, and more.

  4. Integrate AI: Connect to AI services like Google Cloud AutoML. This usually involves setting up a connection to the model’s API.

  5. Preview: Test your app in real-time to see how it works on different devices.

Why Choose AppSheet?

  • Ease of Use: AppSheet is intuitive and user-friendly, making it easy for anyone to create an app.

  • Integration: It seamlessly integrates with various data sources and AI services.

  • Flexibility: You can build apps for web and mobile without needing to write code.

Choosing the right no-code platform is essential for your AI app. Platforms like AppSheet provide a simple, visual interface to build and design your app, making the process straightforward even for beginners.

Step 3: Gather Data

We collect images of various flowers. We make sure each image is labeled with the correct flower name. We can use Google Images or a dataset like the Oxford 102 Flower Dataset.

Collecting and storing images is a crucial part of building an AI app, especially for tasks like image recognition. Here’s a step-by-step guide on how to collect many images and store them properly.

Step 3A1: Define Your Image Requirements

First, define the type of images you need for your AI app. Consider the following:

  • Categories: What different types of images do you need? For example, if you are building a flower recognition app, you need images of various flowers.

  • Quantity: How many images do you need for each category? Generally, more images lead to better model performance.

  • Quality: Ensure the images are clear and properly labeled.

Step 3A2: Collect Images

There are several ways to collect images:

  • Public Datasets: Use existing datasets available online. Some popular sources are:

  • Kaggle: Offers a variety of image datasets.

  • Google Dataset Search: Helps find datasets across the web.

  • Public Domain Resources: Websites like Unsplash or Pexels provide free images.

  • Google Images: Manually search for and download images. Ensure you have permission to use them.

  • User Contributions: Allow users to upload images to your app. This method requires proper validation and labeling.

  • Own Photography: Take your own pictures if feasible.

Step 3A3: Store Images

Once you have collected the images, you need to store them. Here are some storage options:

1.Local Storage:

  • Store images on your computer or an external drive.

  • Organize images in folders by category.

2.Cloud Storage:

  • Use cloud services like Google Drive, Dropbox, or Amazon S3.

  • Create folders for each category and upload images

3.Database Storage:

  • Use databases like Firebase, MongoDB, or SQL databases to store image URLs and metadata.

  • Store images in a cloud bucket and save the URLs in your database.

Example: Using Google Drive and Google Sheets

Let’s use Google Drive for storing images and Google Sheets for organizing image metadata.

1.Create Folders in Google Drive:

  • Create a main folder for your project.

  • Inside the main folder, create subfolders for each image category (e.g., “Roses,” “Tulips,” “Daisies”).

2.Upload Images:

  • Upload your collected images into the respective category folders

Example Sheet:

a)Image URL b)Category c)Description d) Date

https://drive.google.com/file/d/1xyz/view Roses Red Rose 09/07/2024

https://drive.google.com/file/d/2abc/view Tulip Yellow Tulip 09/07/2024

https://drive.google.com/file/d/3def/view Daisies White Daisy 09/07/2024

3.Organize Metadata in Google Sheets:

  • Create a Google Sheet to log details about each image.

  • Include columns like “Image URL,” “Category,” “Description,” and “Date Added.”

Step 4: Label Images

Proper labeling is essential for training your AI model. Ensure each image has a clear, accurate label that matches the category. If using public datasets, double-check the labels for accuracy.

Step 5: Preprocess Data

Raw data often needs cleaning. This step is called preprocessing. Here’s what you can do:

  • Remove duplicates: Ensure your data is unique.

  • Handle missing values: Fill or remove any gaps in your data.

  • Normalize data: Scale data values to a similar range.

Step 6: Choose an AI Service

No-code platforms often integrate with AI services that do the heavy lifting. Some popular AI services are:

  • Google Cloud AutoML: Allows you to train models without code.

  • Microsoft Azure AI: Offers various pre-built AI models.

  • IBM Watson: Provides a range of AI tools for different needs.

Step 7: Train Your Model

Training is where your AI learns. Many no-code platforms have easy interfaces for this. Here’s how you can do it:

  1. Upload your data: Use the platform’s interface to upload your data.

  2. Select your model: Choose the type of model that fits your goal (e.g., image recognition, text analysis).

  3. Train the model: Follow the platform’s steps to train your model. Most platforms have guides to help you.

Step 8: Evaluate Your Model

After training, test your model with some of your data. This shows how well your model learned. If it performs poorly, you might need more data or to tweak your settings.

Step 9: Build Your App

With your trained model, you can now build your app. Use the no-code platform’s drag-and-drop interface to create your app’s design and features. Connect your AI model to your app using the platform’s tools.

Step 10: Deploy Your App

Now it’s time to deploy your app. This means making it available for users. No-code platforms often have simple options for this. You can publish your app to the web or app stores with just a few clicks.

Step 11: Monitor and Update

AI models can degrade over time. Monitor your app's performance regularly. Update it with new data and retrain it if needed. No-code platforms make this easy with their user-friendly interfaces.

Conclusion

Building an AI app without coding involves many steps. Start with a clear goal, gather and preprocess data, choose the right no-code platform and AI service, train and evaluate your model, then build, deploy, and monitor your app. With patience and practice, anyone can build a useful AI app. Happy building!

Free No-Code Platforms

  • AppSheet: Offers a free tier for small projects with limited features. As your app grows or needs more advanced functionalities, you might need to upgrade to a paid plan.

  • Bubble: Has a free tier that allows you to build basic apps. However, for more complex features and custom domains, you need to move to a paid plan.

  • Adalo: Provides a free tier for building and testing apps. Publishing apps to the Apple App Store and Google Play Store requires a paid plan.

  • Wix: Allows you to build and publish websites for free with a Wix-branded domain. To use a custom domain and remove Wix ads, you need a premium plan.

Paid No-Code Platforms

  • OutSystems: Focused on enterprise-level applications with a free tier for small apps but primarily operates on a subscription basis.

  • Mendix: Also targeted at enterprises, offering a free tier for small projects and educational purposes, with premium plans for larger applications and businesses.

Considerations for Choosing a No-Code Platform

  1. Project Size: If you’re working on a small project or prototype, the free tiers might be sufficient.

  2. Features Needed: Advanced features, integrations, and customizations usually require a paid plan.

  3. Scalability: Consider future needs. If you expect your app to grow, look at the cost of scaling up with the platform.

  4. Support and Training: Paid plans often include better support and training resources.

Examples

AppSheet Pricing:

  • Free: Limited to basic app features and fewer users.

  • Premium: Includes advanced features, more users, and better support.

    Bubble Pricing:

  • Free: Basic app building with Bubble branding.

  • Personal Plan: Starting at $25/month, includes custom domains and additional features.

  • Professional Plan: $115/month, offers even more advanced functionalities and support.

No-code platforms can be both free and paid. Free tiers are great for learning, small projects, and prototyping. For more advanced needs and larger projects, you might need to invest in a paid plan. Always consider your project's requirements and future scalability when choosing a platform.

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