AI/ML: taking models from experiment to production

Shipping ML isn't just about the model—it's about data pipelines, serving infra, monitoring, and retraining. We help teams close the gap between a great notebook and a production system that stays accurate over time.

We focus on: input validation and schema, model versioning and A/B tests, latency and cost of inference, and a feedback loop from production back into training. We also plan for failure: fallbacks, alerts, and clear ownership when the model drifts.

Getting the first model to production is only the beginning. We design for iteration: can you retrain on new data, roll back a version, or run an A/B test without a full redeploy? Those capabilities pay off as your use cases grow.

If you're ready to take an AI/ML project to production, we can help design the pipeline and ops so it stays reliable and measurable.