Taming the MLOps Zoo: Orchestrating and Monitoring Models with Airflow

Thanks to AI, your data scientists can build models faster than ever. The new bottleneck? Their attention. When your team maintains a zoo of ML models (dbt/SQL scoring models, Python ML on Kubernetes, and point-and-click product UI models) every new species adds feeding schedules, health checks, and habitat needs. The real question becomes: which animals need the zookeeper right now?

At Pendo, we orchestrate 10+ ML models through Airflow, each with its own dbt Cloud feature prep, Kubernetes scoring pods, and downstream monitoring. This talk covers how we keep the zoo running: DAG dependencies across heterogeneous model types, conditional execution for models that only score on certain schedules, and model-specific sub-pipelines that keep each species healthy. Then we’ll demo DS ModelGuard, an agentic monitoring system we built internally that does the morning rounds, tracking API health, output volume, likelihood drift, and feature-level input drift, so your data scientists know which enclosure to check first.

You’ll leave knowing how to wire up a diverse model zoo in Airflow and how to build attention-routing so your team stops checking every cage and starts prioritizing.