MLOPS05-BP01 Establish deployment environment metrics
Establish comprehensive monitoring of machine learning operations to gain visibility into your deployed ML environments. By measuring metrics such as memory, CPU/GPU usage, disk utilization, endpoint invocations, and latency, you can provide optimal performance and improve the reliability of your ML systems.
Desired outcome: You gain real-time visibility into your ML deployment environments through systematic collection and analysis of performance metrics. You establish robust monitoring dashboards, alerting systems, and automated responses to potential issues. This enables you to proactively address performance bottlenecks, optimize resource utilization, and maintain reliable ML services while managing costs effectively.
Common anti-patterns:
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Implementing monitoring only after experiencing production issues.
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Focusing only on basic system metrics while ignoring ML-specific performance indicators.
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Creating monitoring dashboards without establishing corresponding alerts.
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Setting static thresholds that don't account for typical usage patterns.
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Collecting metrics without regular review or actionable response plans.
Benefits of establishing this best practice:
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Early detection of performance issues before they impact end users.
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Improved resource optimization and cost efficiency.
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Enhanced ability to scale ML services based on actual usage patterns.
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Faster troubleshooting and reduced mean time to resolution.
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Data-driven capacity planning and infrastructure decisions.
Level of risk exposed if this best practice is not established: High
Implementation guidance
Establishing effective deployment environment metrics requires a systematic approach to monitoring both infrastructure and application-specific ML metrics. You need to focus on both system-level metrics like CPU, memory, and disk usage, as well as ML-specific metrics such as inference latency, throughput, and model accuracy. By using AWS services like Amazon CloudWatch, you can centralize your monitoring approach and gain comprehensive visibility into your ML operations.
Start by identifying the key performance indicators that matter most for your specific ML workloads. For inference endpoints, these typically include latency, throughput, and resource utilization. For batch processing jobs, focus on processing time, error rates, and resource efficiency. With these KPIs established, you can implement automated monitoring and alerting to quickly identify when your systems deviate from expected performance.
Regular review of your metrics can identify trends and potential areas for optimization. For example, you might discover that certain models require more memory than originally allocated, or that specific types of inference requests consistently take longer to process. This information guides your infrastructure decisions and prioritizes optimization efforts.
Implementation steps
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Record performance-related metrics. Use a monitoring and observability service like Amazon CloudWatch to record performance-related metrics. These metrics can include database transactions, slow queries, I/O latency, HTTP request throughput, service latency, and other key data. For SageMaker AI endpoints, collect metrics such as CPUUtilization, MemoryUtilization, DiskUtilization, and model-specific metrics like ModelLatency and InvocationsPerInstance.
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Analyze metrics when events or incidents occur. Use monitoring dashboards and reports to understand and diagnose the impact of an event or incident. Amazon CloudWatch Dashboards provide insight into what portions of the workload are not performing as expected. Correlate metrics, logs, and traces to quickly identify root causes when issues arise.
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Establish key performance indicators (KPIs) to measure workload performance. Identify the KPIs that indicate whether the workload is performing as intended. An API-based ML endpoint might use overall response latency as an indication of overall performance, while a batch processing job might track throughput metrics. For generative AI applications, monitor additional metrics like token usage, cost per request, prompt engineering effectiveness, and use SageMaker AI Training Plans
for cost optimization of large-scale AI training workloads. -
Use monitoring to generate alarm-based notifications. Monitor metrics for the defined KPIs and generate alarms automatically when the measurements are outside expected boundaries. Configure Amazon CloudWatch Alarms to send notifications using Amazon SNS when thresholds are breached, which provides for timely responses to potential issues.
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Review metrics at regular intervals. As routine maintenance, or in response to events or incidents, review what metrics are collected and identify the metrics that were key in addressing issues. Identify additional metrics that can identify, address, or avoid issues. Schedule periodic reviews to keep your monitoring strategy effective as your ML applications evolve.
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Monitor and alarm proactively. Use KPIs, combined with monitoring and alerting systems, to proactively address performance-related issues. Configure CloudWatch to initiate automated actions to remediate issues where possible. Escalate the alarm to those able to respond if an automated response is not possible. Use anomaly detection to predict expected KPI values, and generate alerts and automatically halt or roll back deployments if KPIs are outside of the expected values.
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Use Amazon CloudWatch for comprehensive monitoring. Use Amazon CloudWatch metrics for SageMaker AI endpoints to determine the memory, CPU usage, and disk utilization. Set up CloudWatch Dashboards to visualize the environment metrics and establish CloudWatch alarms to initiate a notification through Amazon SNS (email, SMS, or webHook) to notify on events occurring in the runtime environment. For model performance monitoring, implement Amazon SageMaker AI Model Monitor to detect data drift and quality issues.
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Use Amazon EventBridge for automated workflows. Define an automated workflow using Amazon EventBridge to respond automatically to events. These events can include training job status changes, endpoint status changes, and increasing the compute environment capacity after it crosses a defined threshold (such as CPU or disk utilization). Create event rules that run Lambda functions to scale resources, update configurations, or notify specific teams based on the event type.
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Use AWS Application Cost Profiler for cost allocation. Implement AWS Application Cost Profiler to report the cost per tenant (model or user). This assists you in tracking resource usage at a granular level and enables accurate cost attribution across different ML models, teams, or business units. Use SageMaker AI Training Plans
for compute reservation and better resource planning of high-demand GPU resources. Use these insights to optimize resource allocation and identify opportunities for cost savings. -
Implement SageMaker AI Model Monitor for data drift detection. Configure SageMaker AI Model Monitor to continuously monitor the quality of your machine learning models in production. Set up automated quality checks to detect data drift, model drift, and bias drift in your production workloads. This enables you to maintain high quality standards and take corrective actions when models start to deviate from expected behavior.
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Use AWS X-Ray for distributed tracing. Implement AWS X-Ray to trace requests through your ML application components. This provides visibility into request flows, latency bottlenecks, and error points in distributed systems. X-Ray provides an understanding of the dependencies between components so you can optimize end-to-end performance of your ML applications.
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Implement Amazon SageMaker AI Clarify for bias monitoring. Use SageMaker AI Clarify to detect bias in your deployed models. Set up regular monitoring jobs to track bias metrics and alert when they exceed acceptable thresholds so that your ML systems continue to make fair and unbiased predictions in production.
Resources
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