Published on June 12, 2024
Updated on July 4, 2024
2 min read
The convergence of MLOps and OpsOps
Enterprises are merging machine learning lifecycle management with operational automation. The objective: let systems predict anomalies, trigger mitigations, and learn from outcomes with minimal human intervention.
Core building blocks
Model factory: ML pipelines (Azure ML, Databricks, or MLflow) that retrain anomaly detectors and forecasting models.
Digital twin: Azure Digital Twins or Siemens/AVEVA twins simulate impact before live changes.
Closed-loop automation: Azure Automation, Logic Apps, or ServiceNow Flow to execute remediations.
Feedback loop blueprint
Telemetry streams from IoT, infrastructure, and business KPIs.
Models score risk levels; when thresholds are exceeded, events are published to Event Grid.
Automation platform runs playbooks, with human approvals for high-risk steps.
Outcomes (success/failure) feed back into training data.
Operating model
Create a cross-functional Autonomous Ops Guild spanning SRE, data science, and product owners.
Maintain a catalog of automation runbooks with maturity scores (manual, assisted, autonomous).
Document guardrails: when to fallback to manual and how to override automation quickly.
Placeholder for a fab tour, control room demo, or pipeline review.
KPIs
Track:
Percentage of incidents auto-resolved.
Model drift leading indicators (feature stability, prediction error).
Operator trust scores gathered via post-incident surveys.
Close with an invite to a discovery workshop on autonomous operations.