Issue link: https://read.uberflip.com/i/1516423
9 Reduce downtime and increase efficiency with ML-based predictive maintenance When data from equipment, assets, and products is easily accessed, industrial customers can address trouble spots before they cause real problems. ML adds intelligence to existing processes, automates time-intensive manual tasks, and accelerates innovation with the creation of new products and services. AWS offers the most comprehensive set of AI and ML services, including purpose-built services for industrial such as Amazon Monitron and Amazon Lookout for Equipment, which require no machine learning experience. Additionally, Amazon SageMaker makes it possible to build, train, and deploy ML models for any use case with fully managed infrastructure, tools, and workflows. Baxter International Inc. (Baxter), a global medical technology leader, is driven by its mission to save and sustain lives. As part of its digital transformation on AWS, Baxter developed TrueVue, which connects the company's continuous renal replacement therapy devices to AWS IoT Core. TrueVue securely and seamlessly transmits device data to a patient's electronic medical record, providing auto documentation and visibility into how the therapy is being delivered. Baxter can collect and analyze this data remotely, gather near-real-time metrics, and perform proactive monitoring, ultimately providing patients and clinicians with a richer understanding of patient treatment and health. Read the case study › Baxter also worked with AWS to improve operational efficiency across its global manufacturing network through more effective predictive maintenance. Using Amazon Monitron—an end-to-end condition monitoring system that uses machine learning (ML) to automatically detect abnormal conditions in industrial equipment—Baxter was able to avoid over 500 hours of unplanned machine downtime, positively affecting the lives of approximately 10,000 patients. Read the case study › Baxter provides data-driven insights and improves operational efficiency with AWS USE CASE