Deployment & MLOps

A trained model is not a deliverable; a verified, versioned, monitored inference service is. Deployment engagements build that distance out — export, serving, and the operational loop that keeps a shipped model trustworthy.

Serving and export

• Server-side: TF Serving deployments with inference-only exported signatures, batching configuration, and load testing against the real latency budget.
• Cross-runtime: ONNX export for ONNX Runtime and TensorRT targets, with pinned opsets and converter versions recorded in artifact metadata.
• Edge: LiteRT (formerly TensorFlow Lite) conversion for mobile and embedded targets, including full-integer quantization with representative-dataset calibration, and benchmarking on the actual device.

Every conversion ships with parity tests — the converted artifact is verified against the source model on a battery of real inputs before anything reaches production, and the check runs in CI on every retrain.

The operational loop

• Versioning: models, data snapshots, and training code tied together so any production model can be traced to what produced it.
• Monitoring: input-drift and prediction-drift signals, task-metric tracking where labels arrive later, and alerting tuned to action rather than noise.
• Retraining: scheduled or triggered pipelines that retrain, re-verify parity and quality gates, and promote — with a human in the loop where the stakes call for one.

We work with your existing infrastructure — cloud ML platforms, Kubernetes, or plain VMs — rather than installing a platform of our own. Engagements are hourly, and often follow a development or modernization project as its production phase.