01
Boundaries & metrics
Define automation scope, data access, guardrails, and measurable success criteria.
MLOps
Model deployment and monitoring.
02
Agents, tools & eval
Design agent graphs, tool interfaces, evaluation harness, and failure modes.
03
Production wiring
Connect to your stack with observability, rate limits, and human-in-the-loop paths.
04
Monitor & hand off
Ship runbooks, refine prompts and models, and transfer ownership to your team.
Deploying models is the easy part — keeping them healthy isn't.
Without monitoring, retraining triggers, and deployment automation, production models degrade quietly while downstream systems inherit bad predictions. Manual rollouts turn every update into a risk event.
MLOps pipelines for deploy, monitor, and retrain.
CI/CD for models, automated evaluation gates, observability dashboards, and retraining workflows — so ML behaves like engineered software. Lineage, promotion, and rollback are first-class.
Platform toolchain
Operate cadence — tools that keep models alive.
- MLflow
- Kubeflow
- Airflow
- Prometheus
- Grafana
- Docker
- Kubernetes
- S3
- GitHub Actions
- Models ship like software, not one-off notebooks
- Drift caught before customers feel it
- Clear ownership of promote vs. rollback decisions
Related · AI & Data
Continue in this practice.
Ready to start
Scope a MLOps engagement with senior engineers.
Fixed-scope, dedicated squad, or staff augmentation — we'll map the right model in a short scoping call.