MLOps simplified the baseline processes making it easy to build models at scale today. But there has little or no focus on ML acceptance. Any AI/ML model can fail, models are not explainable by design, models can carry the risk of usage during production and model auditing is very complex. Deploying AI for mission-critical use cases requires additional layers like explainability, monitoring, auditability, data privacy and risk mitigation to ensure the AI solution is acceptable to all stakeholders.
Agenda:
- Introducing ML Observability
- Using ML Observability for model monitoring, model explainability and auditing.
- Designing the policy layers to manage model usage risk in ML Observability.