This tutorial outlines a practical, repeatable MLOps workflow for taking a model from training to production monitoring.

Step 1: Prepare Data and Features

• Validate data quality and bias risk before training.
• Version datasets and feature definitions.

Step 2: Train and Evaluate

• Use automated training pipelines for reproducibility.
• Evaluate performance and robustness across segments.

Step 3: Package and Register

• Package the model artifact with metadata and documentation.
• Register the model with versioning and approvals.

Step 4: Deploy with CI/CD

• Automate deployment with validation gates.
• Use canary or shadow releases for risk reduction.

Step 5: Monitor and Retrain

• Monitor drift, latency, and prediction quality.
• Trigger retraining on drift or performance thresholds.

CTA

Ready to move from strategy to measurable impact?

• Request a readiness assessment
• Schedule a strategy call

Sources

• Google Cloud MLOps: Continuous Delivery and Automation Pipelines
• NIST AI Risk Management Framework (AI RMF 1.0)