Observability for Machine Learning: what is it and what are the benefits

Observability is crucial in both DevOps and machine learning contexts, where it involves monitoring workloads by accessing logs, application traces, and resource usage metrics. In machine learning, observability is vital for inspecting and debugging training pipelines, which interact with numerous third-party services and can incur substantial costs. Sematic, an open-source Continuous Machine Learning platform, enhances pipeline observability by surfacing logs, exceptions, and failures directly in the user interface, and by integrating with Grafana for resource usage monitoring. This allows for efficient detection and resolution of inefficiencies, such as idle GPU usage due to I/O bottlenecks, and helps manage overall costs by providing granular visibility into cloud expenditures. Additionally, Sematic supports the monitoring of inference servers to ensure they meet performance expectations and provides insights into model performance over time, potentially indicating when retraining is necessary. By abstracting infrastructure concerns, Sematic enables machine learning teams to quickly resolve issues and iterate on their models, facilitating a smoother development cycle.