Your model works in a notebook.
Make it work in production.

Reproduce, automate, ship, in that order. We first turn the notebook into a versioned training pipeline with pinned data and a registered baseline model, then add CI/CD with evaluation gates, then deploy behind a canary with drift monitoring from day one. For a single model that path typically takes four to eight weeks, and your data scientist keeps working in the tools they know.

Sometimes SageMaker or another managed platform is genuinely the right answer, and we’ll say so in the readiness review. Managed wins for small teams with standard workloads; open tooling like MLflow, Airflow and Kubernetes wins when you need custom serving, multi-cloud portability or tight cost control at scale. Most of our builds are a pragmatic mix, managed where it’s commodity, open where it’s your edge.

Three layers: input drift (are today’s features distributed like the training data?), prediction drift (has the output distribution shifted?), and delayed ground truth (how did predictions actually score once outcomes arrived?). We implement these with Evidently, Prometheus and Grafana, route alerts to named owners, and wire sustained drift to retraining triggers rather than a dashboard nobody opens.

On evidence, not on a calendar. Some models drift in days, others hold for a year, the monitoring data tells you which one you have. We set up scheduled retraining as a safety net, drift-triggered retraining as the primary mechanism, and evaluation gates so a retrained model only ships if it actually beats the incumbent on your benchmark.

Usually, yes, most AI infrastructure we audit runs 3–5x over what the workload needs. The levers are spot instances for training, autoscaling and scale-to-zero for serving, ONNX conversion and quantization for latency, batching, and moving low-traffic models to CPU. Every change is benchmarked against your accuracy and latency budgets before it ships, and cost per prediction goes on a dashboard so it stays fixed.

That’s the exit criterion for every engagement. Handover means your engineers ship a model change end to end, pipeline, eval, deploy, rollback, with us watching, not driving. You get runbooks, architecture docs and a retraining playbook, everything lives in your repos and your AWS account, and 30 days of hypercare backstops the transition. A retainer afterwards is optional, never required.

The unit of change shifts from model weights to prompts, retrieval configuration and orchestration code, but the discipline is the same. LLMOps means versioning prompts like code, running automated eval and regression suites on every change, tracking token cost and latency per feature, and monitoring output quality with judges and user feedback. If you’re shipping on Claude or OpenAI, this is the difference between a demo and a product.

The readiness review is fixed-price. A first model through full rails, pipeline, registry, CI/CD, monitoring, handover, is typically a six-to-ten-week fixed project depending on how much of your data foundation already exists. Platform buildouts for multiple models are scoped after the review. No percentage-of-cloud-spend fees, and you own everything we build.