Welcome to the tag category page for Monitoring!
Continuous Glucose Monitoring (CGM) is a device that automatically tracks blood glucose levels throughout the day and night, giving insights into where glucose levels were, where they are, and where they are headed. CGM systems use a tiny sensor inserted under the skin on the abdomen or arm that tracks glucose levels every few minutes. It is approved for use by adults and children with a prescription and is recommended for patients on intensive insulin therapy. The average starting cost for a CGM system is around $1,000, and Medicare covers it through the durable medical equipment benefit. The major limitation of CGM is the measurement of interstitial glucose levels rather than real-time blood glucose levels, which causes a delay in treatment for hyperglycemia and hypoglycemia in patients.
Glucose monitors are devices used to measure and display blood sugar levels in individuals with diabetes. There are various types of glucose monitors available, including continuous glucose monitoring (CGM) and traditional blood glucose monitors. The accuracy of glucose monitors is generally high when used correctly, but occasional errors may occur. Glucose monitors do not require a prescription and can be purchased over the counter or online. The Accu-Chek Aviva Plus Blood Glucose Monitoring System is known for its accuracy and ease-of-use. Diabetic monitors including blood glucose meters and strips are needed to monitor the blood sugar levels.
MLOps, or Machine Learning Operations, is a set of practices that focuses on deploying and maintaining machine learning models in a production environment, ensuring reliability and efficiency. MLOps combines the principles of Machine Learning with DevOps to streamline the end-to-end process of developing, deploying, and monitoring machine learning models. It involves collaboration and communication between data scientists and operations professionals, aiming to increase the quality, simplify management processes, and automate the deployment of machine learning and deep learning models in large-scale production environments. MLOps is not particularly easy to learn and may take a few months of dedication to learn all the necessary skills. However, if you are a DevOps engineer with knowledge of machine learning algorithms, you can easily transition to MLOps in just a few weeks.
