Turn AI application testing into release criteria, reproducible packaging, and monitored deployment.
Connect application-level evaluation with the practical work of packaging and exposing a model-backed service. Learners define representative tests, examine reproducibility and API boundaries, and create release gates that include security, rollback, and human ownership. These independently written activities complement the videos; they are not a promise that any specific framework or deployment recipe remains current.
Distinguish model-level measures from application-level tests tied to user tasks. Create release criteria that include quality, safety, latency, and failure handling. Plan a minimal deployment review with access controls, monitoring, rollback, and an accountable owner.
Some familiarity with AI applications and basic software concepts is helpful. No deployment is required: learners may complete each assignment as a paper design. Never publish credentials, use production data in an unapproved test, or deploy a consequential system solely on the basis of this course.
Learners with basic familiarity who are ready to build practical, independent skills
People who learn best through examples, guided lessons, and hands-on practice
Professionals, creators, and independent builders looking for a repeatable workflow
Lesson 1 is free
4 lessons · Intermediate · Full course.
This course uses CC BY licensed material attributed to its content provider or uploader. Lesson playback is available only through authenticated access after publication and licensing checks pass.