Overview of the GenAI Application Lifecycle with MLflowThe Five-Phase GenAI LifecycleWhat This Lifecycle SolvesHallmarks of Effective GenAI WorkflowsApplying the Lifecycle to the Unity Airways Use CaseDevelop with MLflowEvidence-Driven Development WorkflowBuilding the Minimum Evaluable ProductPracticing Trace-First DevelopmentVersioning Development ProcessBalancing Quality, Cost, Latency, Safety, and Product FeelThe Disciplined Iteration LoopEvaluate with MLflowEvaluation ObjectivesEvaluation WorkflowBuilding and Maintaining Evaluation DatasetsInterpreting Scores and Traces TogetherHuman-in-the-Loop FeedbackDiagnosing and Resolving IssuesDeploy with MLflowVersioning Applications, Prompts, and ModelsDeployment and Serving ArchitectureReproducible Deployments and Controlled ReleasesEvidence-Based Traffic Promotion and RollbackMonitor with MLflowMonitoring GoalsObservability at Scale with TracingGovernance and Compliance in MonitoringOperational Reliability and Evidence-Based MonitoringImprove with MLflowImprovement ObjectivesWhat Improvement Looks Like in PracticeBridging Offline and Online SignalsThe Improve Loop As a Disciplined PracticeGrounding Improvement in the Unity Airways Use CaseComplete Workflow IntegrationConclusion and Key Takeaways