Monitoring & Observability

Monitoring and observability involve tracking the performance and health of machine learning models and the infrastructure they rely on. This includes gathering metrics, logs, and traces to understand how models are behaving in production, identify potential issues like performance degradation or data drift, and gain insights into the overall system's operation. The goal is to ensure models are accurate, reliable, and deliver value as expected.

Visit the following resources to learn more:

• @article@What’s the Difference Between Observability and Monitoring?
• @article@Observability and Instrumentation: What They Are and Why They Matter
• @video@What is observability?
• @video@Monitoring vs Observability