Bridging DevOps and MLOps: Unifying Pipelines with KitOps and GitOps
summary
Claims that KitOps packages models code datasets and metadata into a versionable OCI artifact and that GitOps then gives automated, auditable deployments. Demo uses model kit packaging (model binaries, params, data, manifest), trains a random forest, saves a pkl, exposes a FastAPI endpoint, builds a Docker/OCI image, and syncs with Argo CD for pull‑based reconciliation and instant rollback. Cites Gartner 85% failure figure and 5–10x ML ops depth; demo deployment syncs successfully and shows drift detection and auditability.
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chapters (11)
- 0:00Bridging DevOps and MLOps
- 3:13DevOps breaks ML pipelines
- 7:32MLOps challenges and maturity
- 10:01KitOps for ML portability
- 13:19GitOps for ML deployment
- 16:51Model Kit components
- 21:10Audit and compliance in ML
- 21:32MLOps and GitOps use cases
- 23:41Argo CD prerequisites
- 26:24Live ML pipeline demo
- 29:27New AI roles and closing
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