// Generated by orrery. Edit the diagram spec and re-render instead of editing this file. import { type SVGProps, useId } from "react"; export default function MLPlatformOnAWS(props: SVGProps) { const uid = useId().replace(/[^A-Za-z0-9_-]/g, ""); return ( ML platform on AWS ML platform on AWS. 11 components in 3 groups. Connections: Raw data lake to Feature pipeline; Feature pipeline to Feature Store; Feature Store to Train; Train to Evaluate; Evaluate to Model Registry; Model Registry to Batch transform; Model Registry to Real-time endpoint; Batch transform to Predictions; Real-time endpoint to Drift alarm (captured traffic); Training pipeline to Retraining rule (StartExecution); Retraining rule to Drift alarm. ML PLATFORM ON AWS Amazon SageMaker AI and AWS Step Functions train and serve models; drift alarms retrain them CAPTURED TRAFFIC CAPTURED TRAFFIC STARTEXECUTION STARTEXECUTION AWS CLOUD US-EAST-1 TRAINING PIPELINE RAW DATA LAKE Amazon Simple Storage Service FEATURE PIPELINE SageMaker AI Processing FEATURE STORE Offline store TRAIN SageMaker AI training job EVALUATE Compare to baseline MODEL REGISTRY Approved versions BATCH TRANSFORM Nightly scoring PREDICTIONS S3 bucket REAL-TIME ENDPOINT Online serving DRIFT ALARM Model Monitor and CloudWatch RETRAINING RULE Amazon EventBridge PIPELINE FLOW RETRAINING LOOP MONITORING DATA ); }