Applied · notebook to product
ML in production & MLOps
Getting a model out of the notebook and keeping it honest afterwards: export, inference inside a .NET product, pipelines, tracking and monitoring.
- ONNX / ONNX Runtime Production Real-time inference inside a .NET application
- Azure ML · MLflow Applied (2026) Parallel training, champion/challenger selection
- gRPC · protobuf · Python packaging Production One parser shared by C# and Python
- Docker Applied Azure ML environments, teaching material
- FastAPI · Flask Production and prototype Device services in the lab
- LLM APIs, agents, RAG Exploring (2026) Self-directed tools and teaching material
The gap between a good notebook and a good feature is mostly unglamorous: preprocessing that matches training exactly, latency measured where the model actually runs, constants that can't drift, and a record of why each number is what it is. I have learned most of that the hard way, starting with a model that ran in real time and gave the wrong answer.
Where it shows up
- 2026 A fault detector the shop floor can trust A causal streaming detector for cutting-insert faults, taken from a Python study to shipped C#. It caught every labelled fault without a single false alarm.
- 2026 From labelled cuts to C# constants An Azure ML pipeline that trains candidate detectors in parallel, picks a champion against the challengers, and exports the winner straight into production code.
- 2024–26 One parser for two languages A C# gRPC service with a Python client wheel that opens production's binary recording format to notebooks and Azure ML, so analysis and product read data the same way.
- 2024 Deep models inside a .NET product LSTM, autoencoder and VAE models trained in PyTorch, exported to ONNX and run on live sensor streams inside the product, with latency monitored against the budget.
- 2026 Machine learning, from first principles to production A curriculum of about fifty lessons written for colleagues, from autograd built by hand to GPT, LoRA, RAG and MLOps, alongside a Python course I wrote and taught.