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Monitoring ML Models in Production: A Complete Guide
A model that passed validation can fail quietly in production. This guide sets out what to monitor at each layer, how to cope with labels that arrive late and how to design alerts people act on, using a year of real sensor data in which the loudest alert was the least useful one.
GCP Logging and Monitoring with Cloud Operations
Google Cloud collects logs and metrics for you. The work is deciding what to keep, what to measure and when to alert. This guide explains log buckets, sinks, audit logs, log-based metrics and alerting policies, and uses a day of pipeline runs to show why an alert on errors misses silent failures.
Monitoring and Alerts in Azure Data Factory
Azure Data Factory shows every run in its Monitor hub, keeps that history for 45 days, and tells nobody when something fails unless you set it up. This guide covers the built-in views, sending run data to Log Analytics, the metrics worth alerting on, queries for failures and slow runs, and notification options.
Prometheus and Grafana for ML Monitoring
Prometheus and Grafana are the usual open-source pair for watching services. This guide instruments a real model API, scrapes it with Prometheus 3.15, unit-tests a drift alert, provisions a Grafana 13 dashboard from files and replays two real weeks of requests to see what the setup catches and what it misses.
AWS CloudWatch for Monitoring Data Pipelines
Monitoring a data pipeline means knowing when it failed, when it did not run, and when it ran but produced bad data. This guide shows how Amazon CloudWatch covers all three: the metrics worth publishing, alarm settings including missing data, logs, EventBridge events from Glue, and a worked example in boto3.