Make your UiPath RPA Bots Smarter with AI using UIPads

Table of Contents

1. Introduction
2. What is Artificial Intelligence?
3. Applying Artificial Intelligence with UIPads
4. Building and Deploying an AI Model with UIPads
5. Downloading the Data Set
6. Preparing the Data Set
7. Creating Training and Evaluation Data Sets
8. Creating an ML Package
9. Creating a Pipeline
10. Adding the AI Package to UIPath Studio
11. Adding the ML Services Activities to a Robot
12. testing the ML Skill
13. Putting the Skill into Production
14. Conclusion

Introduction

Are you ready to explore the exciting world of Artificial Intelligence (AI)? Look no further! In this article, we will dive into the application of AI using UIPads. We will guide you through the process of building and deploying an AI model with UIPads. So, put on your learning hat and let's get started!

What is Artificial Intelligence?

Artificial Intelligence, or AI, is a branch of computer science that focuses on the development of intelligent machines capable of performing tasks that typically require human intelligence. These tasks include Speech Recognition, decision-making, problem-solving, and more. AI is revolutionizing various industries by enabling machines to learn, analyze, and make predictions based on data.

Applying Artificial Intelligence with UIPads

UIPads is a powerful platform that allows users to leverage AI capabilities in their workflows. It combines the power of UIPath and UIPet orchestrator to create and deploy AI models. In this article, we will walk you through the process of applying AI with UIPads to predict sales volumes based on advertisement budgets.

Building and Deploying an AI Model with UIPads

To build and deploy an AI model with UIPads, we need to follow a series of steps. Let's break down the process into manageable chunks:

Step 1: Downloading the Data Set

First, we need to download the data set that will be used to train and evaluate our AI model. We can find useful data sets on websites like Kaggle. Once downloaded, we will extract the data set and save it for further use.

Step 2: Preparing the Data Set

Before we can use the data set, we need to make some modifications. This includes removing unnecessary columns, splitting the data set into training and evaluation sets, and ensuring that the data is in the correct format.

Step 3: Creating Training and Evaluation Data Sets

Now that our data set is prepared, we can create separate training and evaluation data sets. The training data set will be used to train the AI model, while the evaluation data set will be used to assess the model's performance.

Step 4: Creating an ML Package

Next, we need to create an ML (Machine Learning) package. UIPads supports various ML models, and we can choose an out-of-the-box package that suits our needs. In this case, we will use the teapot automl regression package for our sales prediction model.

Step 5: Creating a Pipeline

Once the ML package is created, we can proceed to create a pipeline. The pipeline combines all the necessary steps, including training the model and evaluating its performance. We will specify the input and output data sets, as well as configure the pipeline accordingly.

Step 6: Adding the AI Package to UIPath Studio

To incorporate the AI package into our UIPath Studio, we need to manage packages and install the ML services package. This allows us to use ML skills in our UIPath robots.

Step 7: Adding the ML Services Activities to a Robot

With the ML services package installed, we can now add ML skills to our UIPath robots. These skills enable us to leverage AI capabilities in our workflows, making our robots smarter and more efficient.

Step 8: Testing the ML Skill

Before putting our skill into production, it's crucial to test its functionality. We can do this by providing input data in the required format and verifying that the skill produces the expected output.

Step 9: Putting the Skill into Production

Once we are satisfied with the performance of our skill, we can put it into production. This involves deploying the ML skill to our UIPath orchestrator and connecting it to our UIPath Studio instance.

Conclusion

Congratulations! You have successfully learned how to apply artificial intelligence with UIPads. By following the steps outlined in this article, you can build and deploy your own AI models, making your workflows more intelligent and efficient. Don't hesitate to explore further and discover the limitless possibilities of AI with UIPads.

🚀 Start building smarter robots with UIPads and embrace the power of artificial intelligence!