What is UiPath AI Center? Pairing Artificial Intelligence with RPA

What is AI Center?

Robotic process automation (RPA) has been traditionally automating rule-based processes and workflows. RPA workflows rely on the scaled intelligence of AI to learn new skills, such as visual understanding, process discovery and natural language processing (NLP). On the other hand, AI needs RPA to act as the delivery mechanism to solve real world business problems. It’s the combination of both of these technologies and their capabilities that encompass AI Center.

AI Center (formerly known as AI Fabric) empowers organizations to apply the power of AI in their Automation initiatives.  AI Center tools connect seamlessly with UiPath studio and Orchestrator to create a unified experience within all UiPath Platforms.

AI Center provides a centralized platform to develop, deploy and manage the AI models within the UiPath ecosystem. This integration on AI and RPA helps organizations to tackle a broad range of tasks with accuracy and efficiency.

AI Center is essentially the first step of deploying hyperautomation throughout your entire automation model. It allows organizations to rapidly examine all possible business processes with an evolving set of AI technologies, and identify what end-to-end processes are hyperautomation candidates. Through AI Center, both the human workforce and intelligent RPA work together to scale the automation capabilities in the organization.

Breaking Down AI Center

Essentially within the AI Center platform, RPA bots are responsible for bringing Machine Learning (ML) into an organizations business processes. The Machine Learning model in AI Center is comprised of 3 parts – ML Packages, ML Skills and ML Logs. Each of these parts represent a section of tasks to be done before being able to implement your model within the AI Center network.

ML Packages

An ML Package contains all of the code needed to run your Machine Learning model. These packages are somewhat similar to those that exist within your RPA workflows in Orchestrator, and the UiPath Studio. Packages can be managed within Orchestrator via the ML Packages page, which allows you to view the packages available versions, statuses, and changing logs. You can also upload new package versions in this page, delete unused or undeployed packages, or learn more information about each one.

• PRO TIP: It is highly recommended that users acting as Data Scientists handle these ML Packages.

ML Skills

An ML Skill is a deployed package ready to be consumed by RPA workflows. When deploying a ML Package, the container exposes a REST API endpoint so that it can be called within a RPA workflow using the ML Skill activity.

Skills can be managed within the Orchestrator tab in the ML Skills page, where you can see all the models deployed on the tenant (both ML and OS). Also within this page, users can view the statuses of packages that were created through ML Skills, what version they are, predictions and descriptions, and whether or not a GPU is required.

• PRO TIP: It is highly recommended that users acting as Process Controllers handle model deployments.

Smartbridge provides AI & Machine Learning services such as enterprise generative AI solutions, Microsoft Copilot Studio configuration and much more.

ML Logs

AI Center deploys skills that are created within the ML Skills tool. This deployment entails optimization and security checks, installing various dependencies, setting up a network within the tenant namespace, and finally checking the overall performance of the ML Skill.

ML Logs are essentially a consolidated view of all these ML-related events, such as package validation, deployment and prediction errors. Each event illustrates the start and finish times along with an any errors that may occur. Logs can be managed via the ML Logs page within Orchestrator.

• PRO TIP: It is highly recommended that RPA Developers handle these specific workflows.

UiPath delivered dozens of prebuilt ML models to help with document processing, language analysis and comprehension & image analysis. Based on your use case, these pre-build models can be use in your workflows. Tweak and train them as needed.

An AI Center Use Case

Given all this information, how and where can AI Center be used? Customer Service Inquiries is just one example of how AI and RPA can work together to accomplish a manual task that would have otherwise taken countless minutes, or even hours.

Through a simple UiPath workflow, AI Center NLP models can augment an unassisted bot to analyze customer inquiries using a ML Skill. these inquiries can be sorted accordingly, classified by whether an inquiry needs human intervention, or just an automatic response. Likewise, a similar use case can also be used for processing invoices to extract data from variety of invoice formats to streamline the accounts payable processes, reduce errors and processing times, which could result in additional incentives line early payment discounts, etc..

Through the intelligent and scalable capabilities provided by AI Center, users can govern their own automations, save minutes or even hours on daily tasks, and focus on innovative drivers within their organization.

Keep Reading: What is Hyperautomation? How to Automate End-to-End Processes