[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"cheat-sheet---en":3,"domain-info---en":3,"topic-info----en":3,"lesson-aws-certified-ai-practitioner-ai-ml-fundamentals-ai-ml-development-lifecycle-mlops-fundamentals-en":4,"prev-aws-certified-ai-practitioner-ai-ml-fundamentals-ai-ml-development-lifecycle-mlops-fundamentals-en":18,"next-aws-certified-ai-practitioner-ai-ml-fundamentals-ai-ml-development-lifecycle-mlops-fundamentals-en":28},null,{"locked":5,"reason":6,"meta":7,"item":3},true,"paywall",{"title":8,"description":9,"isFree":10,"estimatedMinutes":11,"difficulty":12,"learningObjectives":13},"MLOps Fundamentals","What MLOps is and why ML systems need it: turning a one-off model into a repeatable, monitored, retrainable production system, and the core practices of experimentation, automation, monitoring, and retraining.",false,18,"intermediate",[14,15,16,17],"Define MLOps and explain why ML systems need operational discipline beyond traditional software","Describe the core MLOps concepts: experimentation, repeatable processes, scalable systems, and managing technical debt","Explain why continuous monitoring and retraining are central to production ML","Distinguish MLOps from a single manual model deployment",{"locked":5,"reason":6,"meta":19,"item":3},{"title":20,"description":21,"isFree":10,"estimatedMinutes":22,"difficulty":12,"learningObjectives":23},"AWS Services Across the Pipeline","Which AWS and Amazon SageMaker capability handles each stage of the ML pipeline, from data labeling with Ground Truth to drift detection with Model Monitor, so you can match a service to a stage on the exam.",19,[24,25,26,27],"Map each SageMaker capability to the pipeline stage it serves","Identify the service for data labeling, feature storage, automated model building, and drift monitoring","Distinguish SageMaker JumpStart, Autopilot, Data Wrangler, Feature Store, Model Monitor, and Clarify by their jobs","Recognize that SageMaker AI is the umbrella platform, not a single tool",{"locked":5,"reason":6,"meta":29,"item":3},{"title":30,"description":31,"isFree":10,"estimatedMinutes":32,"difficulty":12,"learningObjectives":33},"Model and Business Metrics","How to measure whether a model works: the confusion matrix and the classification metrics built on it (accuracy, precision, recall, F1, AUC), regression metrics (RMSE, MAE), and the business metrics that decide whether it was worth building.",22,[34,35,36,37],"Read a confusion matrix and define true/false positives and negatives","Define accuracy, precision, recall, and F1, and explain why accuracy alone misleads on imbalanced data","Explain the precision-recall trade-off and when each matters more","Distinguish classification metrics, regression metrics (RMSE, MAE), and business metrics (ROI, cost per user)"]