Autonomous MLOps Pipelines:Architecting Self-Healing, Drift Resistant Models

🎥 Recorded live at the MLOps World | GenAI Summit 2025 — Austin, TX (October 8, 2025) Session Title: Autonomous MLOps Pipelines: Architecting Self-Healing, Drift-Resistant Models at Scale Speaker: Kamal Singh Bisht, Principal Application Engineer, Discover Financial Services Talk Track: MLOps for Smaller Teams Abstract: Machine learning models deployed in production inevitably face performance degradation due to data drift, concept drift, and infrastructure anomalies. Traditional MLOps pipelines can automate deployment and monitoring, but often fail to adapt or recover autonomously leading to downtime and costly manual fixes. In this session, Kamal Singh Bisht presents a practical framework for building autonomous MLOps pipelines that can detect, adapt, and self-heal in real time. Using a combination of observability tools and orchestration frameworks, these systems monitor telemetry data, identify anomalies, and trigger automated corrective actions—including rollback, retraining, or adaptive alert tuning without human intervention. Learn how to transition from reactive model maintenance to proactive, intelligent MLOps pipelines that ensure continuous model health and business continuity. What you’ll learn: • A reference architecture for end-to-end autonomous MLOps pipelines • Strategies to combat data drift, concept drift, and model decay • Real-world design patterns for auto-retraining, rollback, and model health checks • Tools and frameworks to operationalize self-healing ML systems.