AI: Use Cases & Patterns

Topics covered

• Model selection and evaluation.
• Data collection, labeling and versioning.
• Serving models at scale and monitoring model performance.
• Responsible AI and governance.

Practical guidance

Start with a small, high-value proof-of-concept, track ML metrics, and integrate model monitoring into your SRE/DevOps pipelines.

Reference & further reading

For hands-on operational patterns that complement AI deployments (CI/CD for models, observability, and platform practices), see TechOps Examples.