Privorum builds machine learning systems for teams that need predictions, classifications, or ranking to run reliably as part of a real product — not a notebook experiment that never leaves a laptop.
We help answer practical questions such as:
- Is this actually a machine learning problem, or would rules and heuristics solve it with less risk?
- What data do we already have, what do we need, and how do we close the gap?
- How do we train, evaluate, and retrain the model without breaking production?
- How do we monitor drift, data quality, and model degradation once it is live?
- Where does the model belong in the system — batch, real-time, or behind an API?
Typical engagement areas
- model design, training, and evaluation for classical ML and deep learning workloads
- feature engineering, labelling strategy, and training data pipelines
- model serving, inference APIs, and integration with backend services
- MLOps: experiment tracking, model registries, deployment, and rollback
- monitoring for drift, accuracy regressions, and data quality
Technology depth
Python, PyTorch, scikit-learn, Hugging Face, Ray, MLflow, Kubernetes, and the cloud infrastructure needed to train and serve models without surprises.
Machine learning is only useful when the model lives inside a system that can be trusted, operated, and iterated on. We design for the full lifecycle, not just the training run.