The tremendous growth of Robotic Process Automation over the last few years can be attributed to RPA’s ability to easily and quickly automate standard rules-based business tasks, with a low code implementation approach.
RPA is value for money. But RPA has been limited when it comes to automating more complex tasks.
RPA when
- Coupled with ML can automate more cognitive processes, such as calculating the likelihood of a consumer defaulting on a loan.
- Combined with cognitive vision can be trained to recognize images and UI elements on a screen.
- Used with Natural Language Processing Models can intelligently extract information from millions of invoices and input it into an ERP system and can trigger customer service requests with the help of bots that understand natural language.
RPA, Data Science, and AI are distinct practices in most organizations with their respective skilled resources, focus, and business priorities. The tools, pipelines, deployments, and project management are completely unaligned.
Achieving a symbiotic relationship across the three practice areas concerning people, technology and processes is not an easy thing to do. Automation is a strategic technology initiative and should not be limited to RPA. In fact, AI adoption can catalyze the automation strategy as organizations typically will explore the automation of more complex human-driven processes.
When Bots Carry AI and Machine Learning into your Processes, everyone wins…
UiPath’s ‘AI Fabric’ offering brings AI tooling within RPA in a way that potentially opens up automation opportunities for more complex business use cases. RPA, Data Science and AI teams can now work within a common framework to integrate and deliver their solutions for complex business use cases.
AI Fabric connects seamlessly with UiPath studio and Orchestrator to create a unified experience within all UiPath Platforms.
How does AI Fabric work?
AI Fabric facilitates AI functionality through pre-built UiPath ML/NLP models or the upload of custom ML/ NLP models. AI is leveraged by directly injecting an ML/NLP model into the business process. Custom models could be built by in-house enterprise data-science teams, partner ecosystems, or by the open-source community.
Auto-ML platforms can also be used to source ML models. This functionality comes embedded within UiPath Orchestrator. AI Fabric helps you orchestrate all functionalities of AI: deploy, consume, manage, and improve machine learning models.
Models can be easily deployed within the context of the Orchestrator tenant and can be consumed by any configured robot connected to the Orchestrator tenant. The End-to-End ML Lifecycle deployment model can be managed and scaled.
Traditional Implementation Models vs AI Fabric
Traditional implementations of ML models are difficult to integrate and consume and have a whole lot of management issues like optimization, security checks, and installing various dependencies. Whereas, AI Fabric delivers end-to-end visibility of model versioning and updates.
RPA teams not only will be notified about model updates but data science teams will have visibility as to how the models are being used in production. Additionally, it can analyze model performance and impact with advanced model management and monitoring.
From a tooling perspective AI Fabric makes the sourcing of ML/NLP models to be a seamless experience with task management capabilities that make it easy to manage the selected model and make it available for the robots to consume. An ML Package contains all the code of the Machine Learning model and is managed within the Orchestrator. Deploying an ML Package exposes a REST API endpoint that can be called within an RPA workflow using the ML Skill activity.
Bringing Excitement into Deployment
As an RPA developer, all you need to do is drag-and-drop an ML skill activity to the workflow pane and point to the models that have been uploaded in the Orchestrator include them as part of the RPA workflow without being bothered about manageability or versioning of the ML model. AI Fabric allows the creation of tasks that are contingent on output (range of score/ confidence measure) of the ML/ NLP model.
Due to the task management capabilities within AI Fabric, ML models constantly get better with more data, humans are more informed and robots better managed. All of that contributes to more evolved business processes that meet changing needs.
Computer Vision
Computer vision enables any UI interface to be analyzed by an ML model (generally very extensible across UI of various form factors). RPA can be used to automate across multiple operating systems using computer vision. Automation with AI can be developed using AI fabric. Repetitive and complex documents can be processed using AI-enabled Document Capture. Customer Service Processes can be automated with business-context cognition capability provided by NLP models. AI Fabric’s tooling makes it easier to pipe more data into the retraining of these ML/NLP models.
UiPath’s AI ecosystem has 100+ AI technology partners and this ecosystem continues to grow. AI Fabric enables RPA and data science teams to apply cognitive AI and extend the scope of automation offered by UiPath. AI Fabric is essentially the first step in deploying hyper-automation.
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