Lilly Tech Systems
ML Pipeline Observability
Gain end-to-end visibility into your machine learning pipelines. Learn to implement distributed tracing across pipeline stages, structured logging for ML workflows, custom metrics for training and inference, data quality monitoring, and observability best practices that help you debug failures and optimize performance.
What You'll Learn
Complete observability coverage for machine learning pipelines.
Distributed Tracing
Trace requests through data ingestion, feature engineering, training, and serving stages with OpenTelemetry.
Structured Logging
Implement structured logging for ML pipelines with context propagation, correlation IDs, and log aggregation.
ML Metrics
Define and collect custom metrics for pipeline throughput, stage latency, resource utilization, and model performance.
Data Quality
Monitor data quality throughout pipelines: schema validation, statistical tests, drift detection, and lineage tracking.
Course Lessons
Follow the lessons in order for comprehensive ML pipeline observability.
1. Introduction
Why ML pipelines need specialized observability, common failure modes, and the observability maturity model.
2. Tracing
Implement distributed tracing with OpenTelemetry across ML pipeline stages for end-to-end request visibility.
3. Logging
Structured logging for ML: training logs, experiment metadata, error classification, and log aggregation with Loki.
4. Metrics
Custom Prometheus metrics for ML pipelines: throughput, latency, data volumes, and model quality indicators.
5. Data Quality
Automated data quality monitoring: schema validation, statistical tests, distribution drift, and data lineage.
6. Best Practices
Production patterns: observability-driven debugging, SLO definition, incident response, and continuous improvement.
Prerequisites
What you need before starting this course.
- Understanding of ML pipeline concepts (data ingestion, feature engineering, training, serving)
- Basic knowledge of observability tools (Prometheus, Grafana, or similar)
- Familiarity with Python and ML frameworks
- Experience with containerized applications and Kubernetes