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MLOps Academy
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Production Environment

Deployment, monitoring, and operational excellence for production ML systems

Monitoring and Drift
Pro
Track model performance, detect data drift, and monitor system health in production

Learn how to set up comprehensive monitoring systems, detect model drift, data drift, and concept drift. Understand metrics, dashboards, and alerting strategies.

Governance & Risk
Implement governance frameworks, compliance, and risk management practices

Explore governance models, compliance requirements, audit trails, model versioning policies, and risk assessment frameworks for production ML systems.

Cost & SLOs
Optimize costs and define Service Level Objectives for production workloads

Understand cost optimization strategies, resource allocation, SLO definition and tracking, budget management, and performance-cost trade-offs.

Alerting
Design effective alerting systems for production ML infrastructure

Learn alerting best practices, threshold configuration, escalation policies, notification channels, and reducing alert fatigue.

Troubleshooting
Debug and resolve issues in production ML systems

Master troubleshooting techniques, log analysis, debugging strategies, incident response, and post-mortem practices for production issues.

Data Management
Production data pipelines, feature serving, governance, and data quality

Operate production data pipelines, feature stores, and data quality monitoring. Governance, lineage, and SLOs for production data.