MLOps. ML that stays useful.
Model lifecycle operations. Drift monitoring, retraining pipelines, feature stores, and cost and latency instrumentation. ML that stays useful after the launch demo.
What is included.
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Model monitoring and drift
Input distributions, output distributions, and concept drift. Alerts wired into on-call. Owners named before signals fire.
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Retraining pipelines
Scheduled or trigger-based. Validated against the current production baseline. Promotion gates before anything ships.
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Feature store operations
Feature freshness, versioning, and cost tracking. Offline-online parity checks.
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Cost and latency tracking
Per-endpoint and per-model dashboards. Budget alerts. Cost regression caught before it ships.
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Governance and audit
Lineage from data to training to deployment. Approval trails for regulated domains.
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Experiment tracking
Experiment registry, hyperparameter logs, and reproducibility support. What you need when someone asks where a number came from.
What we run on.
- MLflow
- Weights & Biases
- Evidently
- Databricks ML
- SageMaker
- Vertex AI
- Feast
- Tecton
- Arize
- Fiddler
- LangSmith
How it works.
- Quarterly
- SLA-backed
- Monthly
- Always open
Questions we get.
- Do you cover LLM systems too?
- Yes. Prompt regression, eval harnesses, cost and latency, and content safety monitoring. The shape is different from classical ML but the discipline is the same.
- Who owns the models?
- You do. We operate, monitor, and retrain. We document decisions but the business owner stays accountable for the model.
- What about data quality?
- Close loop with /managed-services/data-platform-operations/ if that is also covered. Otherwise we coordinate with your team.
- Can you pick up a model you did not build?
- Yes. 2-week onboarding audit first. Output is a runbook and a monitoring baseline.
- How do you handle model deprecation?
- Scheduled reviews. If a model is not earning its keep, we retire it. The cheapest model is the one you do not run.