How to protect the privacy of data used to train machine learning models?
Protecting the privacy of data used to train machine learning models is a critical aspect of responsible AI development. It involves a combination of techniques and practices designed to ensure that sensitive information is not exposed or misused. This task has become increasingly important as the scale and complexity of machine learning models grow, and
If one is using a Google model and training it on his own instance does Google retain the improvements made from the training data?
When using a Google model and training it on your own instance, the question of whether Google retains the improvements made from your training data depends on several factors, including the specific Google service or tool you are using and the terms of service associated with that tool. In the context of Google Cloud's machine
- Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, Introduction, What is machine learning
What are the differences between Federated Learning, Edge Computing and On-Device Machine Learning?
Federated Learning, Edge Computing, and On-Device Machine Learning are three paradigms that have emerged to address various challenges and opportunities in the field of artificial intelligence, particularly in the context of data privacy, computational efficiency, and real-time processing. Each of these paradigms has its unique characteristics, applications, and implications, which are important to understand for