Data@Scale on 5/18 with Krishna Gade on Minimize Risks and Accelerate MLOps with Model Performance Monitoring and Explainability
Reports
While monitoring by itself provides real-time issue visibility, it is often insufficient to identify the root cause of issues given the AI system’s complexity. Observability, a means to deduce internal state from its external outputs, is therefore critical to know the ‘why’ for a quick resolution. Explainable AI enables the deployment of high-risk AI solutions while AI Observability increases the success of these AI deployments.
ML is not the easiest technology to deploy. This results in a majority of ML models never making it to production. This paper focuses on the final hurdle to successful AI deployment and the last mile of MLOps - ML monitoring - and reviews key considerations and current approaches.
Explainable AI is the most effective way to ensure artificial intelligence (AI) solutions are transparent, accountable, responsible, fair, and ethical. Explainability enables companies to address regulatory requirements on algorithmic transparency, oversight, and disclosure, and build responsible AI systems.
