Health Check

A health check proactively verifies that a service process is running and responsive, independent of user traffic. It acts as a heartbeat signal.

• Mechanism: Typically an HTTP GET /health endpoint that returns a 200 OK status.
• Failure Action: If the probe fails, an orchestrator (like Kubernetes) can restart the container or pod.
• Purpose: Prevents serving traffic from a crashed or frozen process, a core requirement for high availability.

In containerized environments, two distinct types of checks are used:

• Readiness Probe: Determines if the service is ready to accept traffic (e.g., model loaded, dependencies connected). Traffic is only routed after this succeeds.
• Liveness Probe: Determines if the service is still functioning correctly. A failure triggers a restart.

Using both is critical for zero-downtime deployments and graceful startup during model updates.

Health checks are the primary signal for traffic routing decisions in modern infrastructure.

• Load Balancers: Periodically poll backend instances. Unhealthy instances are removed from the pool of healthy hosts.
• Service Meshes (e.g., Istio, Linkerd): Use health status to manage circuit breakers and failover.
• Impact: This automatic traffic shedding prevents user requests from hitting failing model servers, maintaining overall system SLOs.

Beyond basic process checks, a robust health endpoint for a model server should validate ML-specific dependencies.

• Model Loaded: Verify the model artifact is present in memory/GPU.
• Dependency Health: Check connections to feature stores, vector databases, or external APIs.
• Resource Checks: Monitor GPU memory availability or model cache status.
• Warm-up: A comprehensive check can ensure the model is warmed up and ready for low-latency inference.

Health checks are defined by tunable parameters that balance detection speed with system stability.

• Period/Interval: How often the probe is executed (e.g., every 10 seconds).
• Timeout: How long to wait for a response before marking it as a failure.
• Success/Failure Thresholds: The number of consecutive passes or failures required to change the health state.

Proper configuration prevents flapping (rapid state changes) and allows time for transient issues to resolve.

Health checks are a foundational safety mechanism during progressive delivery of new model versions.

• In a canary release, the new version is deployed to a subset of pods. Their health status is monitored before traffic is incrementally increased.
• If the canary's health checks fail, the rollout can be automatically paused or rolled back, preventing a broader outage.
• This provides a fast-feedback loop for deployment safety, complementing performance-based metrics from A/B testing.

The most fundamental check, verifying the model server process is running and ready to accept traffic. This typically involves a simple HTTP GET request to a dedicated /health or /ready endpoint.

• Liveness Probe: Indicates the container or process is alive. Failure triggers a restart.
• Readiness Probe: Confirms the service is fully initialized (e.g., model loaded, dependencies connected). Failure removes the pod from the load balancer pool.
• Implementation: Often returns a 200 OK status with a JSON payload containing a timestamp and service version.

A deeper validation that the core ML artifact is correctly loaded and all necessary system dependencies are available. This prevents serving requests with a corrupted or partially loaded model.

• Checks: Verifies the model file exists on disk, can be deserialized, and its weights are accessible in memory.
• Dependencies: Validates connections to required external services like feature stores, vector databases, or tokenizers.
• Failure Impact: A failed check here means the endpoint should fail its readiness probe, preventing traffic routing until the issue is resolved.

Monitors the performance characteristics of the model under a synthetic or cached load. This is critical for detecting performance degradation that could breach Service Level Objectives (SLOs).

• Synthetic Inference: Runs a pre-defined batch of sample inputs through the model and measures P50, P95, and P99 latency.
• Throughput Verification: Measures the number of successful inferences per second the endpoint can handle.
• Alerting: Triggers alerts if latency exceeds a threshold (e.g., > 200ms P95) or throughput drops, indicating potential hardware issues, memory leaks, or downstream dependency slowdowns.

Validates that the model's predictions remain sensible and that input data hasn't drifted significantly from the training distribution. This check often runs on a scheduled basis (e.g., hourly).

• Prediction Sanity: Runs inference on a set of golden/benchmark inputs and asserts the outputs are within expected bounds or match cached results.
• Data Drift Proxy: Calculates summary statistics (mean, variance) or uses a drift detection model (like Kolmogorov-Smirnov test on embeddings) on recent live request data compared to a baseline.
• Outcome: A failed sanity check may indicate model corruption, while detected drift can trigger alerts for the Automated Retraining System.

Monitors the underlying infrastructure resources to ensure the model server has adequate capacity and is not failing due to hardware exhaustion.

• Key Metrics: Tracks GPU memory utilization, GPU compute load, system RAM, and CPU usage.
• Hardware Errors: Checks for GPU ECC memory errors or other hardware fault indicators.
• Autoscaling Link: High, sustained utilization metrics can trigger autoscaling policies to add more replicas. Conversely, a GPU memory leak will show as steadily climbing usage until an out-of-memory (OOM) crash.

Verifies the full prediction pipeline, including any post-processing, business rules, and integrations with downstream services, is functioning correctly.

• End-to-End Test: Sends a test request that mimics a real user query through the entire application stack, not just the model inference.
• Validates: Post-processing logic (e.g., filtering, ranking), formatting of the final API response, and successful writes to logging or feedback databases.
• Importance: Catches failures in the orchestration layer around the model, which are often the source of outages even when the core model inference is healthy.

A resilience pattern that monitors for failures from a downstream service (like a model endpoint). If failures exceed a threshold, the circuit 'opens' and all subsequent requests fail fast or are routed to a fallback, preventing cascading system failures and allowing the unhealthy service time to recover. This is a proactive complement to reactive health checks.

• Key Mechanism: Tracks error rates or latency; trips after a configurable threshold.
• States: Closed (normal operation), Open (failing fast), Half-Open (testing recovery).
• Use Case: Protects the overall application when a model server becomes unresponsive or starts returning errors.

A measurable target for the reliability or performance of a service, defined by key metrics. For a model endpoint, SLOs are the business agreements that health checks and monitoring defend.

• Common ML SLOs: Prediction latency (e.g., p99 < 100ms), availability (e.g., 99.9%), and error rate (e.g., < 0.1%).
• Relation to Health Checks: A failing health check typically indicates an SLO breach is imminent or occurring. SLOs define what 'healthy' means quantitatively.
• Example: An SLO of 99.95% availability means the service can be unhealthy (downtime) for no more than ~4.38 hours per year.

A shallow, post-deployment validation that checks whether the core functionalities of a newly deployed service are working before it accepts live user traffic. It's a more comprehensive check than a simple liveness probe.

• Scope: Tests critical integration points and basic model functionality.
• Execution: Runs automatically immediately after a new model container is deployed, before the load balancer directs traffic to it.
• Example Tests: Can the endpoint load the model weights? Does it return a valid schema for a simple test input? Is latency within an expected baseline?

A simpler, more robust model or heuristic that is invoked when the primary model fails. Health checks and circuit breakers are the triggers that activate the fallback strategy to maintain system reliability.

• Purpose: Ensures the application remains functional even during primary model outages, severe performance degradation, or unexpected prediction errors.
• Common Fallbacks: A previous model version, a lightweight model (e.g., logistic regression), a rules-based system, or a cached default response.
• Architecture: Implemented using pattern like retry logic (on transient errors) followed by failover to the fallback.

The network-accessible service (typically a REST API or gRPC service) that exposes a trained machine learning model to receive input data and return predictions. The health check probes this specific endpoint.

• Components: Encapsulates the model, a serving runtime (e.g., TensorFlow Serving, TorchServe, custom FastAPI server), and scaling logic.
• Health Check Types:
  • Liveness: Is the container/process running?
  • Readiness: Is the endpoint ready to accept traffic (e.g., model loaded, dependencies connected)?
• Deployment: Often managed as a containerized microservice behind a load balancer.

A networking component that distributes incoming inference traffic across multiple instances of a model endpoint. It uses health checks to perform critical routing decisions.

• Function: Prevents any single model server from being overwhelmed, enabling horizontal scaling and high availability.
• Health-Aware Routing: Periodically sends health check requests to each backend instance. If an instance fails, the load balancer automatically stops sending traffic to it until it passes checks again.
• Integration: Essential for canary releases and gradual rollouts, as it can split traffic based on weight or rules to different model versions.