MLOps & Data Engineering
The data pipelines, infrastructure, and ML lifecycle tooling that make AI reliable in production.
AI is only as good as the data and infrastructure underneath it. We build the pipelines, feature stores, model registries, and monitoring that turn a promising prototype into a system your team can trust and operate at scale.
How we work
Audit your current data infrastructure → Design pipelines and a model lifecycle → Automate training, evaluation, and deployment → Monitor for drift, cost, and performance in production.
Benefits & deliverables
- ✓ Reliable, versioned data pipelines feeding your models
- ✓ CI/CD for ML — automated retraining and rollback
- ✓ Drift and performance monitoring in production
- ✓ Cost visibility across training and inference
Technology we use
Airflow / Dagster
dbt
MLflow
Kubeflow
Snowflake / BigQuery
Docker / Kubernetes
Frequently asked questions
It's the starting point for almost every engagement. Data cleanup and pipeline design typically come before any model work.
Yes — most engagements augment an existing data/engineering team rather than replace it.