Machine Learning Infrastructure & Edge Inference Deployment
The engineering that turns a model in a notebook into inference running reliably in the real world.
A trained model is a science experiment until it runs in production. The hard part isn't the notebook; it's the infrastructure — the pipelines, serving, monitoring, and the brutal constraints of running inference on a device at the edge instead of a datacenter GPU.
I build that infrastructure: the systems that take a model from research to reliable production, including the optimization needed to run it on edge hardware where memory, power, and latency are tight. The engineering that makes machine learning actually ship.
- ML training and inference infrastructure design
- Model-serving and deployment pipelines
- Edge-inference optimization and deployment
- Model quantization, pruning, and acceleration
- MLOps, versioning, and reproducibility
- Monitoring, drift detection, and retraining pipelines
- Hardware-accelerator and runtime integration
- Latency, throughput, and cost optimization
I treat ML systems as production software with a model inside, applying real engineering discipline to serving, monitoring, and reliability. Edge deployment gets the low-level optimization it demands — quantization and acceleration tuned to the target hardware — so inference meets the real memory, power, and latency budget.
Every engagement runs under a signed NDA and, where testing is involved, a Safe Harbor agreement. Legal and ethical, start to finish.
- A production ML infrastructure and serving pipeline
- Optimized edge-deployable models meeting device constraints
- Monitoring, versioning, and retraining operations
We have a model but can't ship it — can you help?+
Yes. Bridging the gap from a working model to reliable production inference — serving, monitoring, optimization — is exactly the problem this addresses.
Can you run inference on-device at the edge?+
Yes. Edge deployment with quantization, acceleration, and runtime tuning to meet tight memory, power, and latency budgets on real hardware is a core capability.
Hire me for Machine Learning Infrastructure.
Signed NDA and Safe Harbor on every engagement. Projects from five figures to nine. Discreet, relentless, done right.
I only do legal, ethical hacking — under a Safe Harbor contract and NDA.