AI Systems Engineering path · Course 1 · Free · Self-paced

AI Systems Engineering Foundations

Design, evaluate, and prepare complete AI systems for production

Design and assess an entire AI workflow, from use case and context to bounded execution, evaluation, and production readiness. One fictional enterprise case connects all eight lessons.

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8 lessons8–10 hoursGraded checksCertificate of Completion

A useful model is one component. A dependable AI system needs a defined workflow, trustworthy context, bounded actions, measurable outcomes, and an operating plan.

What you will produce

A practical engineering dossier for a bounded enterprise AI use case.

System and dependency map

Use-case brief and measurable criteria

Architecture and model tradeoffs

Context and source-authority flow

Tool contracts and execution boundaries

Evaluation plan and test set

Operating card and recovery paths

Evidence-based release recommendation

Eight lessons.
One running case.

Inspect the curriculum before registration. Full lessons open after enrollment.

Part 1 — Frame the system

01From Model to System45–60 min

02Use Case and Success Criteria50–65 min

Part 2 — Design the workflow

03Architecture and Model Choices55–70 min

04Context and Knowledge55–70 min

05Tools and Bounded Execution55–70 min

Part 3 — Prove and operate it

06Evaluation That Follows the Work55–70 min

07Production Operation and Reliability50–65 min

08Evidence-Based Readiness Review55–70 min

Assessment that teaches.

Each lesson has five required, automatically graded questions. Pass with at least 80%. After all eight lessons, a 32-question final assessment also requires 80%. Retries are allowed. Optional practice exercises are ungraded, and no coding capstone is required.

The credential is a Certificate of Completion, not an accredited professional certification.