UiPath Integrates Automation Workflows with Amazon SageMaker to Amplify ML Models

Enterprise automation software company UiPath has announced data science teams using Amazon SageMaker, an end-to-end machine learning (ML) service, can now connect to UiPath to seamlessly connect new ML models into business processes without the need for complex coding and manual efforts.

Data scientists, ML developers, and business analysts can easily automate deployment pipelines with the help of the UiPath Business Automation Platform, which lowers the cost of experimentation and speeds up innovation.

With fully managed infrastructure, tools, and processes, Amazon SageMaker is a completely managed service from Amazon Web Services (AWS) that allows users to prepare data, construct, train, and deploy ML models for any use case. By connecting Amazon SageMaker to UiPath, users can:

· Deploy ML models quickly into production by using UiPath robots to automate workflows and control end-to-end business processes, integrating Amazon SageMaker ML models into automation workflows without writing any code, and connecting freshly finished ML models into production workflows in minutes.

· Improve the efficiency of data science teams by facilitating precise and consistent procedures that require less human interaction and free up essential resources for strategic work. Organizations can significantly reduce the workload on data science teams by using UiPath automation to roll out the most recent ML models to end users. By reducing human error while keeping human oversight to fulfill governance and compliance criteria, teams can also increase reliability.

· Boost the rate of machine learning innovation by allowing engineering teams to test their theories, take on new tasks, and experiment with their data more regularly. Automation increases the speed and dependability of new model deployment into business processes and eliminates the need for manual script coding, troubleshooting, and maintenance across the whole ML data pipeline.

“With the connection with UiPath, our goal is to assist clients to expedite the deployment of their machine learning models while utilising optimum infrastructure,” said Ankur Mehrotra, General Manager, Amazon SageMaker, AWS.

“Data scientists and data science team leaders are at the forefront of machine learning research, developing strong new models to boost company performance.” According to Graham Sheldon, Chief Product Officer, UiPath, these specialists are also burdened with time-consuming, manual administration, which delays development and raises costs. “We are using automation to assist decrease this complexity by integrating Amazon SageMaker with the UiPath platform. This creates chances for machine learning innovation through speedier deployment at cheaper prices.”

Originally published at https://itvarnews.techplusmedia.com on March 10, 2023.