Integrating Machine Learning into Robotic Process Automation

February 2, 2023

The practical applications of combining robotic process automation (RPA) and machine learning models over the years have been key for unlocking the value of IoT systems. Since the volume of data generated by IoT sensors is far too great for any human to process and use, RPA has been essential for making predictions with the help of ML. Robotic Process Automation (RPA) enables the automation of repetitive tasks to improve efficiency, reduce errors, and lower costs by automating tasks that are time-consuming and prone to human error. RPA on its own is only ideal for handling predictable tasks and predetermined business rules with little to no modifications. In cases where data needs to be handled offline, RPA lacks the cognitive computational abilities of ML to comprehend such exceptions and offer viable solutions. 

ML’s ability to simulate concept learning and identify patterns in large volumes of real-time data provides support to the automation issues RPA can encounter and that we will explore. 

Addressing Drawbacks of Robotic Process Automation

While RPA and ML working together can effectively automate business processes and improve outcomes like reduced time to market, they face certain challenges that hinder their efficiency. One challenge is that RPA is not well-suited to handle unstructured data, which limits its ability to process unstructured documents such as invoices, passports, emails, and handwritten formats. By incorporating OCR with machine learning models, companies can structure their data more easily for RPA devices and automate end-to-end workflows for improved efficiency.

Another challenge in deploying machine learning models with RPA is the need for secure, air-gapped solutions in areas with poor or unreliable connectivity. To address this, companies should seek solutions designed specifically for the secure deployment of models in remote settings. The ability to operate in the field without internet connectivity, while conserving bandwidth and ensuring security, is essential in such scenarios.

A major limitation of RPA is its inability to make fast and accurate predictions from analytical models working with big or streaming data. ML models can efficiently run inferences on batch data with minimum latency, but deploying models to handle streaming data remains a challenge. To overcome this, companies can integrate an ML deployment platform with their RPA to benefit from the rule-based automation of RPA and the additional precision of real-time predictions. This allows for intelligent automation that can auto-generate solutions to any problems or errors that may arise.

Consider a manufacturing plant that uses robots on the shop floor for tasks such as moving, sorting, and packaging products. These robots are equipped with computer vision and OCR technology to accurately identify products. However, there is a requirement to run the computer vision and OCR models on a small PC or laptop, not on a large cloud cluster, to reduce network latency and improve model efficiency. In this scenario, a platform that allows for efficient running of the computer vision and OCR models becomes highly valuable, especially if it can be deployed on the shop floor using a small laptop, allowing the plant to run models near the edge.

Wallaroo’s Machine Learning Capabilities for RPA Applications

Wallaroo is built to deploy models for processing both structured and unstructured data, generating accurate predictions with actionable real-time insights that drive business success. Typically, implementing “Optical Character Recognition” requires GPU hardware upgrades, but with Wallaroo models can now run efficiently on standard CPUs. Not only that but with an ultra-fast distributed computing engine you can run batch jobs or utilize streaming data to capture the latest data insights as it happens.

Wallaroo can also help introduce more flexibility to RPA, allowing your business to deploy machine learning practically anywhere eliminating connectivity issues. It was designed from the ground up to address the last-mile of model deployment. Especially models that encounter large streaming data sets of unstructured data such as those found in Robotic Process Automation. It houses an intuitive UI for easier management of data pipelines to ensure high throughput and offers the utmost flexibility in deployment with air-gapped capabilities for all ML processes. 

Reach out to us at to see what we can do for your machine learning use cases in IoT, RPA, or other scenarios. You can also try Wallaroo for free with our Wallaroo Community Edition