Changing assumptions and ever-changing data mean the work doesn’t end after deploying machine learning models to production. These best practices keep complex models reliable. Agile development teams ...

For anyone managing IT ops, setting performance thresholds has been a big, and tedious, part of the job. I mean, if you don’t tell the system that 8,000 server calls in an hour is way too many, how’s ...

At its very core, machine learning is an advanced means of making sense of massive amounts of data, and for this reason, machine learning and monitoring should go hand-in-hand. With the ability to ...

Once machine learning models make it to production, they still need updates and monitoring for drift. A team to manage ML operations makes good business sense As hard as it is for data scientists to ...

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Apple patents brain-reading AirPods with EEG sensors to detect seizures and monitor neural health, raising concerns about ...

Aliso Viego, California-based Sentrian's remote patient monitoring program has been in a pilot with COPD patients at Anthem subsidiary CareMore for about six months, Sentrian founder and Chief Medical ...

Citation: Ha NT, Manley-Harris M, Pham TD, Hawes I. A Comparative Assessment of Ensemble-Based Machine Learning and Maximum Likelihood Methods for Mapping Seagrass Using Sentinel-2 Imagery in Tauranga ...

Digital Twin of the Ocean is a continuously updated virtual counterpart of the real ocean that exchanges data in real time ...

In food drying applications, machine learning has demonstrated strong capability in predicting drying rates, moisture ...

NITK develops SVALSA, a machine learning-based landslide warning system for the Western Ghats, enhancing disaster ...