monitoring
7 free lessons tagged monitoring across Programming, AI, Business. Each one is a short sequence of focused steps with narration and a five-question quiz at the end — take them in any order, no signup required.
Canary Releases: Deciding With Evidence Instead of Nerve
A canary release sends a slice of real traffic to a new version and asks whether it is healthy. This lesson covers what to measure, why comparing the canary against the current version beats comparing against history, the statistics problem that makes small canaries weak evidence, and how automated promotion and rollback turn a judgement call into a rule.
Metrics, Logs, Traces: Three Signals, Three Cost Models
Observability is not a product you buy but a property your system has: can you explain a behaviour you did not predict? This lesson defines the three telemetry signals, what question each answers, why their costs grow along completely different axes, and why the difference between monitoring and observability is the difference between known and unknown failure modes.
Monitoring, Drift, and When to Retrain
A model that has stopped working returns answers with the same confidence as one that still does. This lesson covers the difference between data drift and concept drift, what to monitor when labels arrive months late, the triggers that should start a retraining run, and the cases where retraining is the wrong response.
Operating It: Security, Upgrades, and Knowing When to Stop
Self-hosting is a permanent operational responsibility rather than a project. This lesson covers the security surface a local model creates, model upgrades and why they are harder than they look, capacity and cost control, what to monitor, and the honest signals that the deployment should be retired.
Running the Audit and Acting on What It Finds
An audit is only useful if it produces a decision. This lesson covers the audit sequence end to end, the three families of mitigation and what each costs, why some findings cannot be fixed at the model layer, documenting a trade-off you can defend, and monitoring for the drift that reopens a closed finding.
Validating a System Whose Behaviour Is Statistical
Validation was built for models that produce a number, not for systems that produce prose. This lesson covers conceptual soundness, outcomes analysis and ongoing monitoring applied to modern AI, what changes for generative systems, the pre-deployment evidence pack, and how to document a position when no framework covers your system.
Governance, Risk, and Continuous Measurement
Responsible AI is a practice, not a slogan. This lesson covers the EU AI Act's four risk tiers and what each requires, model monitoring and drift detection, hallucination rates and human-in-the-loop design, guardrail KPIs, and how to run governance as a measured, auditable discipline rather than a compliance checkbox.