From a model to an AI system.

This course is upcoming. A model’s forward pass is only one part of a usable system. The planned course follows a model from a local inference script to a service whose behavior can be measured and maintained.

The planned project

Build one small inference service and improve it in controlled stages. Keep the model, preprocessing, request contract, evaluation data, and deployment version connected so that a prediction can be traced back to its inputs and implementation.

Planned learning sequence

1. The inference contract. Define inputs, outputs, preprocessing, postprocessing, and failure behavior with typed schemas and reproducible examples.
2. A measurable baseline. Separate model loading, preprocessing, forward time, postprocessing, queueing, and network time. Explain latency percentiles and throughput with real request traces.
3. Batching and resource limits. Explore how batch size, concurrency, memory, and timeouts interact; measure rather than assume the best setting.
4. Packaging and deployment. Version model artifacts and application code, reproduce the runtime, and add readiness and health checks.
5. Evaluation before release. Build a task-specific reference set, identify important slices, and compare candidate versions with the deployed baseline.
6. Observability in operation. Connect request metrics, model versions, input changes, and outcome quality without confusing a healthy process with a useful model.
7. Safe change and recovery. Practice staged rollouts, rollback, incident investigation, and keeping evidence of what changed.
8. Managing the system over time. Track ownership, cost, data and artifact versions, evaluation updates, and the reasons for retraining or replacement.

Prerequisites you can build now

Use the foundations course to understand predictions and learning. Use the Transformer course to inspect a concrete model end to end. The future systems lessons will build on that understanding rather than treating the model as an unexplained API.