UiPath Amplifies Value of New Machine Learning Models by Integrating Amazon SageMaker with Automation Workflows

Rhea-AI Summary

UiPath (NYSE: PATH) has launched a new capability in its Business Automation Platform that enables data science teams to seamlessly integrate machine learning models from Amazon SageMaker into business processes. This integration simplifies the deployment of ML models, significantly reduces costs, and accelerates innovation. Users can rapidly deploy models, optimize productivity, and enhance the speed of ML innovations without coding. The partnership aims to help customers achieve faster time-to-value and operational efficiency by automating deployment processes, thereby lowering the operational burden on data science teams.

Positive

• Seamless integration with Amazon SageMaker enhances deployment speed of ML models.
• Reduces costs and complexity for data science teams.
• Accelerates innovation through automation, allowing teams to focus on strategic tasks.

Negative

• None.

New UiPath Business Automation Platform capability allows data science teams to reduce cost, time, and effort of deploying machine learning models into business processes

NEW YORK--(BUSINESS WIRE)-- UiPath (NYSE: PATH), a leading enterprise automation software company, today announced data science teams using Amazon SageMaker, an end-to-end machine learning (ML) service, can now connect to UiPath to quickly and seamlessly connect new ML models into business processes without the need for complex coding and manual effort.

The UiPath Business Automation Platform makes it simple for data scientists, ML engineers, and business analysts to automate deployment pipelines, reducing the cost of experimentation, and increasing the pace of innovation.

Amazon SageMaker is a fully managed service from Amazon Web Services (AWS) to prepare data and build, train, and deploy ML models for any use case with fully managed infrastructure, tools, and workflows. By connecting Amazon SageMaker to UiPath, users can:

• Rapidly deploy new ML models into production: connect newly completed ML models into production workflows in minutes, minimizing the time to value for business users; integrate Amazon SageMaker ML models into automation workflows without code; and use UiPath robots to drive workflows and manage end-to-end business processes.
• Optimize the productivity of data science teams: facilitate consistent and accurate workflows that reduce the need for human involvement and free up critical resources for strategic work. With UiPath automation, organizations can greatly lessen the burden on data science teams to deploy the latest ML models to end users. Teams can also improve reliability by decreasing human error while maintaining human oversight to meet governance and compliance standards.
• Increase the speed of ML innovation: enable engineering teams to test their ideas, tackle new challenges, and experiment more frequently with their data. Automation removes the manual effort to code, troubleshoot, and maintain scripts across the breadth of the ML data pipeline and improves the speed and reliability of new model deployment into business processes.

“Tens of thousands of active customers use Amazon SageMaker to train models with billions of parameters and make trillions of predictions per month,” said Ankur Mehrotra, General Manager, Amazon SageMaker at AWS. “With the integration with UiPath, our goal is to help customers accelerate the deployment of their machine learning models cost efficiently and with optimized infrastructure.”

“UiPath’s Amazon SageMaker connector is designed to solve a key pain point by allowing our customers to realize business value from their ML models faster. Data science teams can quickly embed ML models into actual business processes and reduce effort and the time to market,” said Sai Shankar, a Managing Director at Slalom, a purpose-led, global business and technology consulting company. “Working in cooperation with AWS and UiPath helps us deliver AI and ML enabled business process automations for our customers. Our data science and intelligent automation teams are eager to leverage the connector to help our customers operationalize ML models faster and leverage them at scale.”

“Data scientists and data science team leaders are working at the cutting edge, creating powerful new machine learning models to accelerate business performance. At the same time, these professionals are saddled with time-consuming, manual management which slows progress and adds costs,” said Graham Sheldon, Chief Product Officer at UiPath. “By connecting Amazon SageMaker to the UiPath platform, we are helping reduce this complexity with automation. This opens avenues for faster deployment, lower costs, and more opportunities for innovation through machine learning.”

To learn more about taking on new use cases by incorporating AI and ML models into automations, visit the UiPath AI Center.

About UiPath

UiPath has a vision to deliver the Fully Automated Enterprise™, one where companies use automation to unlock their greatest potential. UiPath offers an end-to-end platform for automation, combining the leading Robotic Process Automation (RPA) solution with a full suite of capabilities that enable every organization to rapidly scale digital business operations.

About Slalom

Slalom is a purpose-led, global business and technology consulting company. From strategy to implementation, our approach is fiercely human. In seven countries and 44 markets, we deeply understand our customers—and their customers—to deliver practical, end-to-end solutions that drive meaningful impact. Backed by close partnerships with over 400 leading technology providers, our 13,000+ strong team helps people and organizations dream bigger, move faster, and build better tomorrows for all. We’re honored to be consistently recognized as a great place to work, including being one of Fortune’s 100 Best Companies to Work For seven years running. Learn more at slalom.com.

View source version on businesswire.com: https://www.businesswire.com/news/home/20230308005044/en/

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Pete Daly

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Kelsey Turcotte

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Source: UiPath