UiPath announced that data science teams utilizing Amazon SageMaker, an end-to-end machine learning (ML) service, are now able to connect to UiPath to swiftly and effortlessly integrate new ML models into business processes without the need for difficult code or manual work.
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. Users can: by integrating Amazon SageMaker with UiPath
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.
Ankur Mehrotra, General Manager, Amazon SageMaker at AWS, stated that tens of thousands of active customers utilize Amazon SageMaker to train models with trillions of parameters and create trillions of predictions each month. By the connection with UiPath, we hope to assist customers in deploying their machine learning models more quickly, more affordably, and with better infrastructure.
“UiPath’s Amazon SageMaker connector is created to address a significant issue by facilitating faster commercial value realization for our clients’ ML models. Sai Shankar, Managing Director at Slalom, a purpose-led, international business and technology consulting organization, stated that data science teams can swiftly incorporate ML models into actual business processes and decrease effort and the time to market. “Our collaboration with AWS and UiPath enables us to provide our customers with AI and ML-powered business process automation. Our data science and intelligent automation teams are enthusiastic to use the link to assist our customers in quickly and widely utilizing ML models.
“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 of 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.
