Health Check

This component verifies the model's serving infrastructure is reachable and responsive. It involves sending a synthetic request to the model's API endpoint and measuring the HTTP status code and response time.

• Key Metrics: P99 latency, error rate (4xx/5xx), and throughput.
• Purpose: Ensures the model service is online and meets Service Level Agreements (SLAs) for inference speed.
• Example: A health check might fail if the average latency exceeds 500ms or if the endpoint returns a 503 Service Unavailable error.

This test validates that the model produces logically correct and well-formed outputs for a set of known inputs. It checks for schema adherence and business logic.

• Schema Check: Ensures the output matches the expected JSON schema (e.g., correct data types, required fields).
• Golden Set Test: Runs inference on a small, static set of golden examples where the expected output is known, checking for exact or semantic match.
• Purpose: Catches silent failures where the model serves predictions but they are nonsensical or malformed, indicating potential model corruption or code bugs.

This component monitors for statistical shifts in the live data flowing through the model compared to its training or a recent reference window. It is a leading indicator of future performance decay.

• Input Drift (Covariate Shift): Detects changes in the distribution of feature values (e.g., average transaction amount shifts).
• Output Drift (Prediction Shift): Detects changes in the distribution of the model's predictions.
• Techniques: Uses statistical tests like Population Stability Index (PSI) or Kolmogorov-Smirnov test.
• Purpose: Alerts teams to changing real-world conditions before they impact key business metrics.

This check monitors the computational resources consumed by the model serving infrastructure to ensure efficiency and predict scaling needs.

• Key Metrics: GPU/CPU utilization, memory consumption, and queries per second (QPS).
• Scaling Triggers: Identifies if resource usage is approaching predefined thresholds, triggering horizontal or vertical scaling.
• Cost Correlation: High resource usage often correlates directly with cloud inference costs. Monitoring this helps with FinOps and capacity planning.
• Purpose: Prevents performance degradation due to resource exhaustion and controls infrastructure costs.

This component verifies that all dependencies required for the model to function are present, accessible, and uncorrupted. It ensures the deployed environment is consistent.

• Artifact Check: Validates the checksum of the serialized model file (e.g., .pt, .onnx, .joblib) against the version in the model registry.
• Dependency Validation: Confirms the correct versions of libraries (e.g., PyTorch, TensorFlow, CUDA drivers) are installed.
• External Service Connectivity: Tests connections to required external services like vector databases or feature stores.
• Purpose: Guarantees the model's execution environment is exactly as intended, preventing "it works on my machine" failures in production.

This advanced check validates that the model's outputs adhere to critical business rules and safety constraints, acting as a final verification layer.

• Guardrail Enforcement: Tests for violations of content filters, toxicity thresholds, or data privacy rules (e.g., ensuring no PII is leaked).
• Business Rule Validation: For a recommendation model, this might check that no out-of-stock items are recommended.
• Link to Policy: This check directly enforces governance policies defined for the model's use case.
• Purpose: Provides assurance that the model operates within safe, compliant, and commercially viable boundaries.

A benchmark metric or model performance level established under controlled conditions, used as a reference point for comparing future model iterations. A health check often compares current latency and correctness metrics against this established baseline to detect degradation.

• Purpose: Provides a quantitative standard for "normal" operation.
• Usage: Critical for interpreting health check results; deviations signal potential issues.

The automated monitoring and identification of changes in the statistical properties of production data (data drift) or in a model's predictive performance over time. While a basic health check confirms the endpoint is functional, drift detection systems analyze prediction distributions to identify deeper model decay.

• Data Drift: Change in input feature distributions.
• Concept Drift: Change in the relationship between inputs and outputs.

A deployment strategy where a new model version is released to a small, controlled subset of production traffic to validate its performance before a full rollout. Health checks are run aggressively on canary endpoints to catch regressions early.

• Process: Route 1-5% of traffic to the new version.
• Validation: Health checks and business metrics are compared between canary and champion environments.

A deployment strategy where a new model processes live traffic in parallel with the current production model, but its predictions are logged and not served to users. Health checks ensure the shadow model's endpoint is available and performing within latency SLAs during this evaluation phase.

• Key Benefit: Zero-risk performance evaluation with real-world data.
• Requirement: The shadow model's infrastructure must pass the same health checks as the primary model.

The infrastructure and software layer responsible for hosting a deployed model and providing a low-latency API for generating predictions (inference). A health check is a direct probe of the serving layer's availability and performance.

• Components: Includes the inference server, API gateway, load balancer, and scaling logic.
• Health Check Target: The serving endpoint (e.g., a /predict route) is the specific URL probed.

A proactive monitoring technique that uses simulated requests and transactions to test application performance and availability. A model health check is a form of synthetic monitoring, using crafted inference requests to test the model's serving stack.

• Synthetic Requests: Pre-defined, representative inference payloads.
• Measures: End-to-end latency, HTTP status codes, and output correctness.