Seldon CEO Alex Housley dives into how organisations can sufficiently protect their machine learning deployment through powerful monitoring capabilities. Explainability for ML algorithms is an essential companion to moving to production, but achieving transparency and navigating changing regulation, such as the EU’s new AI legislation, is difficult to execute in real-life. Alex will explain several use cases that will demonstrate how to approach deployment responsibly and efficiently, whist future-proofing organisations by minimising the risk their models pose.
- What does recent legislation, such as the EU regulation on machine learning, mean for ML deployment?
- How can you bake in monitoring and other risk-minimising methods into your deployment pipeline?
- What do you need to consider when building out an efficient ML deployment pipeline?