MLOpsEXAMPLE SCENARIO
Automated model delivery from registry to production
This is an illustrative scenario describing how we typically approach this kind of engagement. It does not describe a specific client, and no results are claimed.
Challenge
A frequent situation: models are trained in notebooks and handed over manually. Deployments are slow, training is hard to reproduce, and no one notices when a model degrades.
Architecture
- Training pipelines in Argo Workflows
- MLflow for experiments and model registry with approval stages
- CI/CD that deploys approved models to KServe with canary rollout
- Monitoring for latency, errors and data drift
Approach
- Pick one representative model and automate its full lifecycle
- Define promotion rules and approvals
- Generalize the pipeline into a reusable template
- Onboard further models
Technology
MLflow · Argo Workflows · KServe
Implementation
- Containerize training with pinned dependencies
- Log parameters, data versions and metrics for every run
- Trigger deployment from registry stage changes
- Alert on drift and feed results into retraining decisions
Intended outcome
The intended outcome is a repeatable path from experiment to production, where every deployed model can be traced back to its data and code.
Lessons learned
- Automate one model completely before building a platform
- Reproducibility depends on versioning data, not just code
- Monitoring must exist before the first automated deployment