tool:triton
Triton
Triton in the context of production machine-learning systems.
Lessons
Formal explanations, examples, simulations, and checkpoints.
Serving & inference systems
Choose batch, online, streaming, edge, or hybrid
Place inference where the decision’s deadline, freshness, and failure semantics can actually be met.
Serving & inference systemsPackage model, preprocessing, schema, and runtime
The deployable artifact is the complete raw-input-to-output program, not the weights alone.
Serving & inference systemsFeatures, batching, caching, and backpressure
Fast serving comes from bounded, version-aware work; graceful overload is a feature, not an afterthought.
Serving & inference systemsCapacity, load testing, autoscaling, and tails
Design capacity around the tail and the delay of the scaling loop, not around average throughput.
Serving & inference systemsShadow, canary, rollback, fallback, observability
A release is safe only when exposure is attributable, bounded, observable, and quickly reversible.
Courses & assignments
Dependency-authoritative learning units.
Casebook
Reported facts and course reconstructions.