Service
ML Engineering
A model in a notebook is not a product. We build the pipelines, infrastructure, and MLOps practices that turn experiments into reliable, monitored, production-grade machine learning — and we re-platform legacy models onto modern stacks.
What we do
- Training & inference pipelines
- Feature engineering and feature stores
- Model migration & re-platforming (e.g. legacy code to Python / Databricks)
- Model serving — batch and real-time
- MLOps: experiment tracking, model registry, CI/CD
- Monitoring, drift detection, and retraining
What you get
- Reproducible training pipelines with tracked experiments
- A model registry and promotion workflow
- Deployed models with monitoring and alerting
- Documentation and MLOps practices your team can own
Tech we use
DatabricksMLflowPythonPySparkscikit-learnPyTorchDelta LakeFeature StoreDocker
FAQ
Can you modernize models written in another language?
Yes. We migrate and re-engineer models from legacy languages and runtimes into Python on modern platforms such as Databricks, validating numerical parity along the way.
We have models but no MLOps. Where do you start?
We start by making training reproducible and deployment repeatable, then layer in monitoring and retraining — incremental steps that deliver value quickly.
Have a data, ML, or AI challenge?
Book a 30-minute call. We'll tell you straight whether and how we can help.
Book a meeting