Data & MLOps
We establish the data infrastructure and ML lifecycle tooling that keeps your AI models accurate, observable, and improving over time.
What's included
- Data pipeline design and implementation
- Feature store setup and management
- Model registry and versioning
- Automated retraining pipelines
- Data quality monitoring and alerting
- Experiment tracking and reproducibility
Our approach
Data Landscape Audit
We map your current data sources, pipelines, and quality issues — identifying gaps that will block model reliability in production.
Platform Design
We design an MLOps platform sized to your team: the right tooling for your scale, not the most complex stack on the market.
Pipeline Implementation
We build data pipelines, feature engineering jobs, experiment tracking, and the model registry — with testing and observability built in from the start.
Handoff & Enablement
We document everything, run training sessions with your data engineers, and provide a 30-day support window as your team takes ownership.
What you get
- Data pipeline codebase (tested, documented)
- Feature store with initial feature set
- MLflow or W&B experiment tracking setup
- Automated retraining trigger configuration
- Data quality monitoring dashboard
- Team training and documentation
Technologies we use
- Apache Airflow
- dbt
- Feast
- MLflow
- Weights & Biases
- DVC
- Great Expectations
- Prefect
- Snowflake
- BigQuery
Related services
Ready to get started?
Tell us about your project and we'll come back with a concrete plan within one business day.
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