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Un-risk Model Deployment with Differential Privacy

As a general rule, all data ought to be treated as confidential by default. Machine learning models, if not properly designed, can inadvertently expose elements of the training set, which can have significant privacy implications. Differential privacy, a mathematical framework, enables data scientists to measure the privacy leakage of an algorithm. However, it is important to note that differential privacy necessitates a tradeoff between a model's privacy and its utility. In the context of deep learning there are available algorithms which achieve differential privacy. Various libraries exist, making it possible to attain differential privacy with minimal modifications to a model.

A Beginner's Guide to MLOps

MLOps is the combination of Machine Learning and Operations. Like DevOps for the software world, the concatenation of "ML" with the agile execution methodology "Ops", augurs a coming of age of Machine Learning.