UiPath amplifies value of new Machine Learning Models by integrating Amazon SageMaker with Automation workflows

    UiPath, the industry-leading provider of enterprise automation software, recently announced that data science team members utilising Amazon SageMaker, an end-to-end ML service, can now link to UiPath to rapidly and easily incorporate new ML models into business processes without the need for challenging coding or manual work.

    About UiPath

    UiPath wants to help businesses realise their full potential through automation by delivering the Fully Automated EnterpriseTM. The premier Robotic Process Automation (RPA) technology and a comprehensive set of capabilities offered by UiPath provide an end-to-end platform for automation that enables any firm to expand digital business operations quickly.

    “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.”

    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 experimenting 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 integrating Amazon SageMaker with 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 fully developed ML models into production workflows in minutes.
    • Boost the rate of machine learning innovation by allowing engineering teams to test their theories, take on new tasks, and experimenting 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.
    • Improve the efficiency of data science teams by facilitating precise and uniform 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 involvement to fulfil governance and compliance criteria, teams can also increase reliability.

    “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.”

    Interested users can visit UiPath AI Center to learn more about applying AI and ML models to automations to take on new use cases.

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