AI World 4 · ML Pipeline · Stage 6

Wire the Pipeline

Five MLOps tickets: a weak model in production, an unapproved version live, an evaluation with no model, a 3-hour step rerun, and a retraining nobody can trace.

SageMaker Pipelines
Condition steps and caching
Model Registry approval

Domain: AI and machine learning
World: AI World 4: ML Pipeline (Associate)
Game type: Evidence desk: Each ticket is the reporter's words: find its cause in the evidence.
Time: About 30 minutes on a first attempt (estimated)
Exam guides: MLA-C02Skill 3.3.1: Implement automated deployment strategies and rollback actions; Skill 3.3.3: Configure training and inference jobs; Skill 3.3.4: Configure automated testing strategies within CI/CD pipelines for traditional ML and AI workloads; Skill 3.3.5: Build and integrate mechanisms to re-train models; Skill 3.3.6: Manage model versions for repeatability and audits (for example, SageMaker Model Registry, MLflow on SageMaker AI)
AIP-C01Skill 5.1.4: Create systematic quality assurance processes to maintain consistent performance standards for FMs (for example, by using continuous evaluation workflows, regression testing for model outputs, automated quality gates for deployments); Skill 5.1.9: Create deployment validation systems to maintain reliability during FM updates (for example, by using synthetic user workflows, AI-specific output validation for hallucination rates and semantic drift, automated quality checks to ensure response consistency)