What is ML Service Level Agreement (SLA)?

Implement comprehensive monitoring, automated testing, version control, incident response procedures, and continuous improvement processes aligned with organizational objectives.

ML SLAs should cover five categories: availability (99.9% uptime for serving endpoints), latency (p50, p95, p99 response times with specific targets like p99 under 200ms), accuracy (model performance above defined thresholds measured on rolling windows), freshness (maximum age of model and feature data), and error handling (graceful degradation requirements and fallback behavior specifications). Include data pipeline SLOs covering processing delay, completeness, and schema validation pass rates. Define measurement methodology, reporting frequency, and remediation timelines for each metric category.

Start with 30 days of baseline measurement to understand actual system performance before committing to targets. Set SLO targets at the 95th percentile of observed performance rather than aspirational goals. Include error budgets (e.g., 0.1% allowed downtime per month) that give engineering flexibility for deployments and maintenance. Define exclusions clearly: scheduled maintenance windows, force majeure events, and client-side errors. For external APIs, tier SLA commitments by pricing plan. Review SLAs quarterly with performance data and adjust based on infrastructure improvements or changing requirements.

ML SLAs should cover five categories: availability (99.9% uptime for serving endpoints), latency (p50, p95, p99 response times with specific targets like p99 under 200ms), accuracy (model performance above defined thresholds measured on rolling windows), freshness (maximum age of model and feature data), and error handling (graceful degradation requirements and fallback behavior specifications). Include data pipeline SLOs covering processing delay, completeness, and schema validation pass rates. Define measurement methodology, reporting frequency, and remediation timelines for each metric category.

Start with 30 days of baseline measurement to understand actual system performance before committing to targets. Set SLO targets at the 95th percentile of observed performance rather than aspirational goals. Include error budgets (e.g., 0.1% allowed downtime per month) that give engineering flexibility for deployments and maintenance. Define exclusions clearly: scheduled maintenance windows, force majeure events, and client-side errors. For external APIs, tier SLA commitments by pricing plan. Review SLAs quarterly with performance data and adjust based on infrastructure improvements or changing requirements.

ML SLAs should cover five categories: availability (99.9% uptime for serving endpoints), latency (p50, p95, p99 response times with specific targets like p99 under 200ms), accuracy (model performance above defined thresholds measured on rolling windows), freshness (maximum age of model and feature data), and error handling (graceful degradation requirements and fallback behavior specifications). Include data pipeline SLOs covering processing delay, completeness, and schema validation pass rates. Define measurement methodology, reporting frequency, and remediation timelines for each metric category.

Start with 30 days of baseline measurement to understand actual system performance before committing to targets. Set SLO targets at the 95th percentile of observed performance rather than aspirational goals. Include error budgets (e.g., 0.1% allowed downtime per month) that give engineering flexibility for deployments and maintenance. Define exclusions clearly: scheduled maintenance windows, force majeure events, and client-side errors. For external APIs, tier SLA commitments by pricing plan. Review SLAs quarterly with performance data and adjust based on infrastructure improvements or changing requirements.