Data systems (databases, big data processing systems, machine learning platforms..) are at the heart of data science. They allow the data scientists to manage and retrieve data, process it efficiently, and train and deploy powerful AI models. The computational demands of these systems have been increasing to match the need of the modern data-intensive applications. This, in turn, leads to an unsustainable increase in the carbon footprint of these applications.

To achieve sustainable progress for emerging applications, we must make their computational footprint more transparent and develop mechanism to minimize this footprint.

In this session, we will talk about ways to achieve this for machine learning platforms and data management systems.