AWS guidance documentation change
Summary
Added new 'CMS Integration: Quick Start' section with steps for connecting tire prediction models to CMS telemetry pipelines, including data generation, model training, deployment, and verification
Security assessment
The change adds operational guidance for CMS integration but contains no evidence of addressing security vulnerabilities. While IAM roles and SSM parameters are mentioned, these are standard AWS features rather than new security documentation. No security advisories, vulnerability fixes, or security-specific configurations are introduced.
Diff
diff --git a/guidance/latest/automotive-data-platform-on-aws/pm-getting-started.md b/guidance/latest/automotive-data-platform-on-aws/pm-getting-started.md index f217505a3..702daf015 100644 --- a//guidance/latest/automotive-data-platform-on-aws/pm-getting-started.md +++ b//guidance/latest/automotive-data-platform-on-aws/pm-getting-started.md @@ -3 +3 @@ -[Documentation](/index.html)[Guidance for an Automotive Data Platform on AWS](https://docs.aws.amazon.com/solutions/automotive-data-platform-on-aws/)[Implementation Guide](guidance-overview.html) +[Documentation](/index.html)[Guidance for an Automotive Data Platform on AWS](https://docs.aws.amazon.com/solutions/automotive-data-platform-on-aws/)[Implementation Guide](sign-up-for-aws.html) @@ -5 +5 @@ -How the Predictive Maintenance Model WorksImplementation StepsConfigure Environment VariablesBootstrap CDK (First-Time Only)Deploy Infrastructure StacksManual Step: Configure Redshift DatashareTrigger Initial ETL RunTrigger Initial ML TrainingTest Inference PipelineConfigure Alert Notifications +How the Predictive Maintenance Model WorksImplementation StepsConfigure Environment VariablesBootstrap CDK (First-Time Only)Deploy Infrastructure StacksManual Step: Configure Redshift DatashareTrigger Initial ETL RunTrigger Initial ML TrainingTest Inference PipelineConfigure Alert NotificationsCMS Integration: Quick Start @@ -399,0 +400,111 @@ If using S3 unload instead of datashare: +## CMS Integration: Quick Start + +For customers using the [Connected Mobility Guidance](https://docs.aws.amazon.com/guidance/latest/connected-mobility-on-aws/developer-guide.html), follow these steps to connect the tire prediction model to your CMS telemetry pipeline. + +### Step 1: Generate training data + +The solution includes a synthetic data generator that creates realistic tire telemetry with injected anomalies: + + + cd guidance-for-predictive-maintenance + python3 scripts/generate_training_data.py + +This creates 721,024 records across 50 vehicles over 6 months, including: + + * Normal driving patterns with seasonal temperature effects + + * Slow leaks (8% of vehicle-tires, 0.3–1.2 PSI/day loss) + + * Punctures (4%, sudden pressure drop) + + * Valve failures (3%, intermittent pressure loss) + + * Overinflation events (2%) + + + + +Output: `data/training/tire_telemetry_full.parquet` (17.5 MB) + +### Step 2: Train the model + +**Option A: Command line** + + + python3 scripts/train_model.py \ + --region us-east-2 \ + --role-arn arn:aws:iam::ACCOUNT:role/cms-sagemaker-execution-role \ + --bucket cms-tire-prediction-ACCOUNT-REGION \ + --deploy + +**Option B: SageMaker notebook** + +Open `notebooks/train_tire_model.ipynb` in SageMaker Studio or a local Jupyter environment. The notebook provides: + + * Data exploration and visualization (pressure distributions, slow leak examples) + + * Feature preparation and normalization + + * Model training with progress monitoring + + * Evaluation with precision/recall/F1 metrics + + * Anomaly score distribution visualization + + * Endpoint deployment and SSM configuration + + + + +Both options train a SageMaker Random Cut Forest model (~3 minutes), deploy a real-time endpoint (~5 minutes), and save configuration to SSM Parameter Store. + +### Step 3: Deploy CMS integration + +Deploy the CDK stack to create the prediction Lambdas and EventBridge schedule: + + + cd source/infrastructure + DEPLOYMENT_STAGE=prod cdk deploy tire-predictive-maintenance-stack + +This creates: + + * `cms-{stage}-daily-tire-check` Lambda — runs daily, detects slow leak trends + + * `cms-{stage}-blowout-risk` Lambda — real-time highway blowout risk assessment + + * EventBridge schedule (daily at 10 AM UTC) + + * IAM roles with least-privilege permissions + + * S3 bucket for training artifacts + + + + +### Step 4: Verify end-to-end + +Start a simulation in the CMS Fleet Manager UI with the "Tire pressure below safe threshold" maintenance event selected. Within 2 minutes: + + 1. The simulator gradually drops tire pressure from 32 PSI toward 20 PSI + + 2. The Flink MaintenanceProcessor detects `maintenance.tire_pressure` when pressure crosses 28 PSI + + 3. A maintenance alert appears on the vehicle detail page with a $35 estimated repair cost + + 4. The daily tire check Lambda (when run) detects the pressure trend and writes a `prediction.tire_slow_leak` warning + + + + +For highway blowout risk testing, select "Highway blowout risk" which creates a composite condition: tire pressure drops below 30 PSI while vehicle speed exceeds 60 mph. The SageMaker endpoint evaluates the multi-signal risk pattern and writes a `prediction.blowout_risk` alert. + +### SSM Parameters + +After training, the following parameters are available: + +Parameter | Description +---|--- +`/tire-prediction/{stage}/normalization-stats` | Feature normalization (mean/std per feature) +`/tire-prediction/{stage}/anomaly-threshold` | Anomaly score threshold for blowout risk detection +`/tire-prediction/{stage}/endpoint-name` | SageMaker endpoint name for real-time inference +