New UiPath Business Automation Platform capability allows data science teams to reduce cost, time, and effort of deploying machine learning models into business processes
UiPath, a leading enterprise automation software company, 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:
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“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”
- 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.”
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