We foresee Amazon SageMaker Canvas empowering our business users and process engineers to start working on data science problems that were previously owned by the data science team. Equally important, however, was to ensure that our data science team had visibility into the models built so that they can support and productionize as needed. “Our business analysts are data savvy, and we needed the ability to let them create predictive models.
The collaboration is important because it helps us productionalize more ML models and ensure all models adhere to our quality standards and policies.”ĭavood Naderi, Data Science Team Lead at Industrial Applications, Siemens EnergyĪ subsidiary of Koch Industries since 2004, INVISTA brings to market the proprietary ingredients for nylon 6,6 and recognized brands including CORDURA and ANTRON. We found Amazon SageMaker Canvas a great addition to the Siemens Energy machine learning toolkit, because it allows business users to perform experiments while also sharing and collaborating with data science teams. This enables us to increase the speed of innovation and digitalization of our energy solutions such as Dispatch Optimizer and Diagnostic services. “The core of our data science strategy at Siemens Energy is to bring the power of machine learning to all business users by enabling them to experiment with different data sources and machine learning frameworks without requiring a data science expert.
They are transforming in key focus areas of environmental, social and governance (ESG), and their innovation is making the future of tomorrow different today, for both their partners - and their people.
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