Monitoring ML systems at runtime
Monitoring pipelines in production is a critical aspect of MLOps to ensure the performance, reliability, and accuracy of deployed ML models. This includes several practices.
The first practice is logging and collecting metrics. This activity includes instrumenting the ML code with logging statements to capture relevant information during model training and inference. Key metrics to monitor are model accuracy, data drift, latency, and throughput. Popular logging and monitoring frameworks include Prometheus, Grafana, and Elasticsearch, Logstash, and Kibana (ELK).
The second one is alerting, which is a setup of alerts based on predefined thresholds for key metrics. This helps in proactively identifying issues or anomalies in the production pipeline. When an alert is triggered, the appropriate team members can be notified to investigate and address the problem promptly.
Data drift detection is the third activity, which includes monitoring the distribution...
