Articles about: MLOps

Data from 89% of healthcare organisations have been stolen in the past two years due to cyberattacks. As many as 81% of the cybersecurity-related incidents in healthcare result from employees’ negligence. Patient health records can be sold on the black market for as much as USD 363, which is higher than a cost of any […]

In the previous paper, I presented how MLOps supports the implementation of ML (machine learning) systems. The following text builds on one of the practices previously outlined, i.e. monitoring. Below I will attempt to answer three questions:  Why is the monitoring of production models so important, and what are the possible consequences of lack thereof?  What are […]

In the survey conducted in 2020, 36% of survey participants said that data scientists spend a quarter of their time deploying ML models. Another 36% of respondents said that the ratio was as high as 1:1. So how to expand the process of deployment of machine learning systems? What should be done so that data engineers can focus […]