Software company UiPath has announced that data science teams using Amazon SageMaker, the comprehensive machine learning (ML) service, can now connect to UiPath and use new ML models in business processes without the need for complex programming.

The UiPath business automation platform makes it easy 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.

“Tens of thousands of active customers use Amazon SageMaker to train models with billions of parameters and make trillions of predictions per month. 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.”

said Ankur Mehrotra, General Manager, Amazon SageMaker at AWS.

“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.”

said Graham Sheldon, Chief Product Officer at UiPath.

By connecting Amazon SageMaker to UiPath, users can to rapidly deploy new ML models into production, optimize the productivity of data science teams and to increase the speed of ML innovation.

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