Systems | Development | Analytics | API | Testing

In AI we trust? Why we Need to Talk About Ethics and Governance (part 2 of 2)

In part 1 of this blog post, we discussed the need to be mindful of data bias and the resulting consequences when certain parameters are skewed. Surely there are ways to comb through the data to minimise the risks from spiralling out of control. We need to get to the root of the problem. In 2019, the Gradient institute published a white paper outlining the practical challenges for Ethical AI.

Future of Data Meetup: Future of data and analytics in the Hybrid & Multi Cloud

The most valuable and transformative business use cases require multiple analytics workloads and data science tools and machine learning algorithms to run against the same diverse data sets. It’s how the most innovative enterprises unlock value from their data. Turning data into useful insights is not easy, to say the least. The workloads need to be optimised for hybrid and multi-cloud environments, delivering the same data management capabilities across bare metal, private and public clouds. In this session, we will discuss how businesses can leverage the combination of best-in-class software and public cloud to help businesses turn raw data into actionable insights, without the overheads and without compromising performance, security and governance.