Machine learning models—especially large-scale ones like GPT, BERT, or DALL·E—are trained using enormous volumes of data.

A crucial part of the machine learning lifecycle is managing data drift to ensure the model remains effective and continues to provide business value. Data is an ever-changing landscape, after all.

All thanks to machine learning and data science, we can now calculate data at a capacity of 5 billion calculations per second. ... Structure of predictive models for problem-solving.

Differential privacy is a method for protecting people’s privacy when their data is included in large datasets. Because differential privacy limits how much the machine learning model can depend ...

The Big Data Analytics, Artificial Intelligence and Machine Learning research cluster tackles important problems and develops real-life applications, harnessing technologies to extract insights and ...

Generative AI, which is technically a first cousin of AI, combines data science and machine learning at its core. To fully leverage this potential, leaders must build teams that understand the ...