About the Managing ML models with UiPath AI Center (v2024.10) training

Managing ML models with UiPath AI Center shows how you can automate more and improve decision making in your automation projects by powering up automation with Artificial Intelligence. You’ll learn how AI Center can simplify the management and deployment of Machine Learning in your automations. The course teaches you to create projects, upload datasets and deploy ML models as skills with AI Center.

The UiPath AI Center overview is an advanced course. It takes 1 hour 30 mins to complete. At the end, you’ll receive a diploma of completion.

Product alignment

This training was built using the 2023.10 version of the UiPath Platform components. Although we're now aligning it with the 2024.10 version, the new features introduced in 2024.10 do not impact the core functionality covered in this course. Therefore, the videos and text remain fully relevant.

You may notice some visual differences between the 2023.10 version shown in the course and the 2024.10 version you are using. However, these differences are minor and should not affect your understanding or execution of the tasks.

Learning prerequisites

This course requires an understanding of the main automation concepts and the general automation capabilities of UiPath Studio. Consider going through the following course before this one: UiPath AI Center Overview 

Audience

The Managing ML models with UiPath AI Center course is aimed at Data Scientists and Automation Developers, as well as anybody else interested in seeing how Machine Learning can bring value in different business areas.

Agenda

You’ll be introduced to the main concepts of AI Center. The full agenda covers:

AI Center main concepts: 

• Projects, Datasets, Data Labeling, ML Packages, Pipelines, ML Skills, Closing the Loop, ML Logs 

Practice: AI Center and UiPath Studio

Learning objectives

At the end of the Managing ML models with UiPath AI Center course, you should be able to: 

• Create projects and upload datasets in AI Center. 

• Train and evaluate out-of-the-box ML packages in AI Center with pipelines. 

• Deploy ML models from AI Center to UiPath Studio as ML skills and ensure that data is sent back to AI Center. 

• Use the ML logs to monitor the ML models in AI Center.