UiPath Integrates with Amazon SageMaker to Unlock the Value of New Machine Learning Models

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

“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.”

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, according to the vendor.

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
  • Optimize the productivity of data science teams
  • Increase the speed of ML innovation

“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.”

For more information about this news, visit www.uipath.com.


Subscribe to Big Data Quarterly E-Edition