Machine Learning & MLOps
From model training to production deployment — and the systems to keep models performing.
What “shipped to production” actually means
In MLOps, “deployed” is just the start. A production model needs:
- Reproducible training — same data + same code → same weights. DVC, MLflow, or hashed datasets in object storage.
- Versioned inference — every served prediction tied to a model version, so when something goes wrong you can trace back.
- Eval gates — before any model is promoted, it has to beat the current production model on a frozen holdout. This is non-negotiable.
- Online/offline parity — the features used at inference are computed the same way as at training. Feast or a custom feature store closes this gap.
- Monitoring — input drift, prediction drift, latency, error rate, and business KPI all watched. Most teams have one of these; few have all four.
When this fits
You have data-science capacity but no one paid to keep models running reliably in production. Or you’re scaling from one model to ten and the manual deployment dance has stopped scaling.
Questions about Machine Learning & MLOps.
It means: reproducible training (someone else can rerun and get the same numbers), versioned inputs (the training data is captured, not just the model weights), CI-gated deployments (model can't be promoted without passing eval thresholds), monitored inference (you know when latency or accuracy degrades), and a retraining path (when drift happens, the loop closes itself).
For most teams, managed wins on TCO once you account for the platform team you'd need otherwise. We've shipped on all three. The decision usually comes down to your cloud, your data residency requirements, and how much custom orchestration you need.
Adjacent. LLM apps care more about prompt versioning, retrieval quality, and eval datasets than about training pipelines. Traditional ML cares about training reproducibility, feature engineering, and drift. We do both, but treat them as separate workstreams because the tooling differs.
Multiple layers. (1) Input drift: distribution checks on features (KS test, PSI). (2) Output drift: prediction distribution over time. (3) Performance drift: when labels arrive, comparing predicted vs actual. (4) Business KPI drift: when the model's downstream metric trends wrong. We wire alerts on whichever combo gives you signal first.
Ready to talk about Machine Learning & MLOps?
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