From Model to Service
Turn a model into a service you can explain and operate. Trace requests, evaluate answers and plan deployment. Then run guided CPU and serving labs, measure client latency and defend queue and cancellation decisions before specializing in GPU serving or fleet operations.
A model that answers once is the beginning. A service also needs a reachable endpoint, permission to read the right files, evidence that its answers are useful, and a way to recover when a release fails.
Follow one small documentation service through five modules. Establish its network, identity and artifact boundaries, measure useful answers, budget retrieval, then plan rollout and capacity. That design work produces a deployment memo.
Two guided labs turn that plan into measurement practice. First, run a small model, remove repeated prompt work and check answer usefulness. Then measure client latency, queueing and cancellation through a local API. Practise explaining what you observed, which calculation supports your decision and what evidence would change it.
The design exercises use explicitly hypothetical inputs. The labs use a pinned small model and real CPU measurements, with runnable code, six diagrams, a quality screen, lifecycle tests and worked decision reviews. You can complete them without renting a GPU. These local experiments do not certify a production deployment's performance. Three lessons are free without an account, two require a free account, and ten require Premium. The serving module starts with a complete free clock-and-queue primer; its runnable experiments and worked review require Premium.
After this course, Foundations of AI Infrastructure explains the device and serving arithmetic in depth. Continue into Inference Engineering, Training at Scale or Fleet and Reliability according to your work.
When you finish, you can
- Trace a request through name resolution, authorization and model execution
- Separate compute, persistent artifacts, metadata and workload credentials
- Read evaluation outcomes and preserve reproducible release evidence
- Budget embedding, retrieval and reranking work independently of generation
- Plan deployment probes, rollback, queue limits and operational measurements
- Defend a small inference service with a capacity worksheet and failure plan
- Run a controlled CPU benchmark, check answer quality and defend a measured improvement
- Measure client latency under a burst, compare admission policies and verify cancellation
BEFORE YOU START · Read the free Start here introduction first. No previous ML deployment is required. This course teaches systems decisions and worked exercises, not programming syntax or a cloud certification syllabus.
Syllabus
Service boundaries
Trace the request, assign workload identity and publish a reproducible model release. Produce a boundary map and an artifact manifest.
Quality and retrieval
Count useful outcomes, budget document retrieval and preserve permissions during corpus changes. Build an evaluation sheet and a stage-capacity plan.
Operate and improve
Promote a tested release, diagnose queues and bound capacity costs. Finish with an evidence-backed service proposal and a rollback plan.
Guided lab: measure a real model
Run a small model on your CPU, isolate repeated prompt work, test answer usefulness and defend a measured recommendation. Three Premium lessons include runnable experiments, diagnostic checks and a worked review.
Serving lab: from a model loop to an API
Understand the client clock in a free primer, then run two Premium workshops: stream a real local model, measure a concurrent burst, bound admission and verify cancellation.