Embracing Responsible AI for ML Models in Production with Amber Roberts
September 14, 2022
In this episode of MLOps Live, Sabine and Stephen are joined by Amber Roberts, Machine Learning Engineer at Arize AI. They explore the best practices for implementing responsible AI for MLOps that prioritize accountability, fairness, and bias reduction. They further look at the role of observability and explainability in building responsible AI.
Machine learning (ML) applications are now widely used across businesses that want to integrate artificial intelligence (AI) capabilities, moving beyond the realms of academia and research. An increasing interest in the principles, techniques and best practices for using AI ethically and responsibly is growing along with the number of AI and ML solutions.
Amber addresses the diverse questions surrounding aspects of observability, interpretability, privacy, reliability, fairness, transparency, and accountability using her breadth and depth of domain knowledge. She further discusses best practices for monitoring AI applications using tools and procedures that will also be necessary.
Subscribe to our YouTube
channel to watch this episode!
Learn more about Amber Roberts:
If you enjoyed this episode then please either:
Previous guests include: Andy McMahon of NatWest Group, Jacopo Tagliabue of Coveo, Adam Sroka of Origami, Amber Roberts of Arize AI, Michal Tadeusiak of deepsense.ai, Danny Leybzon of WhyLabs, Kyle Morris of Banana ML, Federico Bianchi of Università Bocconi, Mateusz Opala of Brainly, Kuba Cieslik of tuul.ai, Adam Becker of Telepath.io and Fernando Rejon & Jakub Zavrel of Zeta Alpha Vector.
Check out our three most downloaded episodes:
MLOps Live is handcrafted by our friends over at: fame.so