Data science & machine learning
MLOps platform
Feature store, model registry, serving and monitoring, so a model is deployable by the team that built it.
8–14 weeksTypical duration
The problem this solves
Your data scientists build models and your engineers rebuild them for production, and the two versions disagree.
What you receive
Artefacts you can hold, and that you can accept or refuse — never a list of activities.
- A feature store with the same definitions used in training and in serving
- A model registry with lineage from data version to deployed artefact
- A serving path your data scientists can deploy to without an engineering ticket
- Drift and performance monitoring on the deployed models
Also in data science & machine learning
Decision modelling & forecasting
Demand, risk or capacity models built against the decision they inform rather than against a leaderboard metric.
Experimentation & causal inference
The design of experiments and the analysis that separates a real effect from a seasonal one.
Model risk & validation
Independent validation, documentation and challenge, in the form a regulator or an audit committee expects.