Services
Data & Feature Management
Reliable ML starts with reliable data. We implement feature stores so training and serving use the same features, trace every model back to the data that built it, put quality gates on your pipelines, and govern the datasets you train, validate and serve on.
We make your ML data dependable: a feature store for consistent features, lineage from source to model, quality checks on every pipeline and governed datasets — so any result can be reproduced and audited.
What's included
- Feature store implementation
- Data lineage and model traceability
- Data-quality controls and validation standards
- Governed training, validation and inference datasets
How it works
A clear path from the first conversation to a system your team runs.
- Step 1:
Map data and features
We trace where each feature comes from today and where training and serving disagree.
- Step 2:
Design the feature store and lineage
One definition per feature, versioned datasets and lineage linking sources, features and models.
- Step 3:
Automate quality gates
Schema, freshness and range checks at every stage stop bad data before it reaches a model.
- Step 4:
Govern and document
Validation standards, owners and documentation, so datasets stay trustworthy and auditable.
Technologies we work with
Chosen for your environment: we work in your cloud and with the tools you already use.
MLOps platforms
- PyTorch
- TensorFlow
- Vertex AI
- MLflow
- Kubeflow
- Prometheus
- Grafana
Languages
- Python
- SQL
- FastAPI
Data engineering
- BigQuery
- Snowflake
- PostgreSQL
- dbt
- Airflow
- Spark
- Databricks
- Delta Lake
- Power BI
In practice
RetailA Unified Data and Feature Foundation for an Australian Retailer
Business data was scattered across spreadsheets and operational systems, and every report was built by hand.
80%Less time spent producing reports
Client engagement · anonymised
Read the case studyFrequently asked questions
Do we need a feature store?
If several models share features, or training and serving compute them differently, yes. For a single model a well-tested pipeline can be enough, and we will say so.
How do you trace a prediction back to its data?
Datasets, features and models are versioned and linked by lineage, so any prediction can be traced to the model version, features and source data behind it.
What data-quality checks do you add?
Schema, freshness, completeness and range checks at each pipeline stage. A failure stops bad data before it reaches training or production.
Which platforms do you work with?
BigQuery, Snowflake, Spark and Airflow, plus the feature-store and lineage options in Vertex AI and MLflow, chosen to fit what you already run.
Does this help with audits?
Yes. Versioned datasets, lineage and documented validation standards give auditors and risk teams the evidence they ask for, designed with GDPR and the EU AI Act in mind.