Create Your Own AI App in Just 5 Minutes

Table of Contents

1. Introduction
2. Application Architecture
   1. Cloning the Repository
   2. Connecting to a Kubernetes Cluster
   3. Deploying a Streamlit UI Using Docker Compose
3. Using the AI Application
   1. Predicting Iris Species
   2. Getting the Port for the Model Server
   3. Connecting to the Model Server
   4. Sending Data and Getting Predictions
4. Behind the Scenes
   1. Deployment Process
   2. Inference Process
5. Modifying and Building Your Own App

Building an AI App in Five Minutes

Have You ever wanted to build an AI application but thought it would take too much time and effort? In this tutorial, we will Show you how to build an AI app in just five minutes. This is the first installment of our series, where we will walk you through the entire process step by step. By the end of this tutorial, you will be able to build your own AI app within minutes. So, let's get started!

Introduction

In this tutorial, we will be focusing on application architecture and the process of deploying an AI app. We will be using a pre-trained model to predict Iris species. Throughout the tutorial, we will provide background information on each step and explain how everything is connected. Let's dive in!

Application Architecture

Before we begin, let's take a look at the application architecture. Everything you need for this tutorial has been documented in a GitHub repository. You can easily follow along by cloning the repository to your local machine. In this tutorial, we will be connecting to a Kubernetes cluster to deploy a Streamlit UI using Docker Compose.

Cloning the Repository

To get started, clone the GitHub repository onto your local machine. This will give you access to all the necessary files and code for building the AI app. Once you have cloned the repository, you can proceed to the next step.

Connecting to a Kubernetes Cluster

In this tutorial, we assume that you already have a Kubernetes cluster installed. If you don't, you can refer to the documentation for instructions on how to set it up. Once your Kubernetes cluster is up and running, you can move on to the next step.

Deploying a Streamlit UI Using Docker Compose

Now, it's time to deploy the Streamlit UI. Using Docker Compose, we can easily set up the UI and connect it to the model server. The command to deploy the UI is provided in the repository. Simply copy and paste the command into your terminal, and within seconds, the UI will be up and running.

Using the AI Application

With the application architecture in place, let's explore how to use the AI application. The UI provides a user-friendly interface for predicting Iris species Based on user-selected input.

Predicting Iris Species

The AI application utilizes a model server to deliver predictions. In a future video, we will cover the deployment process of the model server. For now, let's focus on getting the port for the model server.

Getting the Port for the Model Server

In order to connect to the model server, we need to obtain the port number. By clicking on a specific link in the UI, you can access the port information. This allows us to establish a connection between the UI and the model server.

Connecting to the Model Server

Once we have the port number, we can use it to establish a connection to the model server. This connection enables us to access the available models and select the one we want to use for predictions.

Sending Data and Getting Predictions

With the connection established, we can now send data to the model server. The UI provides sliders that allow us to input the Dimensions of the iris pedal. By adjusting the sliders and pressing the predict button, we can generate predictions for the selected iris species. The model server returns the prediction, and we can see the results in the UI.

Behind the Scenes

Now that we have covered how to use the AI application, let's take a look at what's happening behind the scenes. There are two parts to consider: the deployment process and the inference process.

Deployment Process

The deployment process involves a Python script with four lines of code. This script creates a project, imports the model server function, adds the model itself, and deploys it. The script also sets up an HTTP trigger to enable communication with the model server.

Inference Process

The inference process is responsible for making predictions based on the user input. It involves making a POST request to the model server with the input data. The server processes the request and returns the prediction. The code behind this process is straightforward and transparent, allowing you to understand and customize it as needed.

Modifying and Building Your Own App

The great thing about the AI app we have built is that it serves as a template. You can modify the code and customize it to suit your specific needs. The GitHub repository provides all the necessary resources, including the Streamlit UI code and the Python code for deployment and inference.

In conclusion, building an AI app is no longer a time-consuming and complex task. With the right tools and resources, you can Create your own AI application in just five minutes. So why wait? Start building your AI app today and unlock the power of artificial intelligence!

Highlights

• Build an AI app in just five minutes
• Easy-to-follow tutorial with step-by-step instructions
• Cloning the GitHub repository for quick access to necessary files
• Deploying the Streamlit UI using Docker Compose
• Predicting Iris species using a pre-trained model
• Understanding the deployment and inference processes
• Modifying and customizing the AI app to suit your needs

FAQ

Q: How long does it take to build the AI app? A: With our step-by-step tutorial, you can build the app in just five minutes.

Q: Can I customize the app to suit my specific needs? A: Yes, the GitHub repository provides all the necessary resources for modifying and customizing the app.

Q: Do I need prior experience with AI to build the app? A: No, our tutorial is beginner-friendly and does not require prior experience with AI.

Q: Is the AI app scalable? A: Yes, the app can be deployed on a Kubernetes cluster, which allows for scalability.

Q: Can I use a different dataset for prediction? A: Yes, you can modify the code to use a different dataset for prediction.

Q: Are there any limitations to the app? A: The app is designed for predicting Iris species and may not be suitable for other types of predictions without modifications.

Q: Can I deploy the app on a different platform? A: The tutorial focuses on deploying the app using Docker Compose and a Kubernetes cluster. However, you can adapt the instructions for other platforms if desired.

Q: Can I contribute to the GitHub repository? A: Yes, the repository is open for contributions. Feel free to submit your modifications and improvements.

Q: Who can I contact if I have questions or need assistance? A: You can find contact information in the repository. Reach out to us, and we'll be happy to help!