[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"cheat-sheet---en":3,"domain-info---en":3,"topic-info----en":3,"prev-aws-certified-ai-practitioner-ai-ml-fundamentals-ai-ml-development-lifecycle-aws-services-across-the-pipeline-en":4,"lesson-aws-certified-ai-practitioner-ai-ml-fundamentals-ai-ml-development-lifecycle-aws-services-across-the-pipeline-en":18,"next-aws-certified-ai-practitioner-ai-ml-fundamentals-ai-ml-development-lifecycle-aws-services-across-the-pipeline-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},"Model Sources and Deployment Options","Where the model in your application comes from (pre-trained open source, a managed model, or one you train yourself) and how you serve it in production: managed API versus self-hosted, and real-time, serverless, asynchronous, or batch inference.",false,20,"intermediate",[14,15,16,17],"Compare the three sources of a model: use a pre-trained model, customize one, or train your own","Distinguish a managed API service from a self-hosted API for serving a model","Choose among real-time, serverless, asynchronous, and batch inference by traffic and payload","Match a production scenario to the deployment option its constraints demand",{"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},"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.",18,[34,35,36,37],"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"]