The modern machine learning (ML) pipeline relies heavily on big data, with applications such as Mobileye's self-driving car efforts processing over 200 petabytes of data or tens of billions of inferences per day. Kafka is a widely used pub/sub framework that powers event-driven pipelines, offering benefits like asynchronous processing, scalability, and reliability. To monitor ML models, Kafka messages can be ingested into the Arize platform using a simple consumer that consumes micro-batches of events, deserializes them, batches them together, and publishes them to Arize for real-time observation. Arize is built to scale, providing easy ways to ingest data, including Kafka event streams, and unlocking ML performance tracing once ground truths are received, which enables improving model performance by understanding the why and how of models.