Production Machine Learning Systems & MLOps
Models that ship, stay healthy, and earn their keep.
The problem
Plenty of models work in a notebook and never make it to production — or they ship once and quietly rot, with no monitoring, no retraining path, and surprise cloud bills nobody can explain.
What you get
A production ML system with reproducible training, automated deployment, monitoring for drift and quality, and clear cost controls — so the model keeps delivering value long after launch.
What's included
- Problem framing, baselining, and model development
- Reproducible training pipelines and experiment tracking
- Deployment as batch or real-time services with versioning
- Monitoring for data drift, model quality, and latency
- Evaluation harnesses and automated retraining triggers
- Cost optimization across compute and inference
Typical stack
PythonPyTorchscikit-learnXGBoostMLflowWeights & BiasesSageMakerVertex AI
Frequently asked questions
What is MLOps?
MLOps is the set of practices that takes machine learning models from experiment to dependable production — covering reproducible training, deployment, monitoring, evaluation, and retraining, much like DevOps does for software.
How do you stop a model from degrading after launch?
By monitoring inputs and predictions for drift and quality regressions, setting alert thresholds, and wiring retraining triggers so the model is refreshed before performance slips below an agreed bar.
Can you deploy models on our existing cloud?
Yes — engagements target your existing AWS, GCP, or on-prem stack rather than forcing a migration, using managed services where they save time and cost and custom infrastructure only where it's warranted.
Ready to get started with ml systems & mlops?
Tell me about your project and I'll come back with ideas, a clear scope, and next steps — usually within 24 hours. Free discovery call, no commitment.