Data Engineering for AI/ML
Build reliable data pipelines, feature stores, and data contracts that power production ML systems.
ML-Ready Data Infrastructure
Machine learning is only as good as the data that powers it. We build data infrastructure that's reliable, scalable, and purpose-built for ML workloads—from feature engineering to model training to production inference.
What We Build
- ETL/ELT Pipelines — Extract, transform, and load data from diverse sources into ML-ready formats
- Feature Engineering — Create reusable feature transformations with consistent logic across training and serving
- Feature Stores — Centralized repositories for feature definitions, computation, and serving
- Data Quality Monitoring — Detect schema drift, missing values, outliers, and distribution shifts
- Data Contracts — Define expectations and SLAs between data producers and consumers
- Real-Time Pipelines — Stream processing for low-latency feature computation and inference
Common Challenges We Solve
Training-Serving Skew
Ensure feature computation is identical in training and production
Data Quality Issues
Catch bad data before it reaches models or downstream systems
Feature Reusability
Build features once, use across multiple models and teams
Scale & Performance
Process large volumes efficiently for both batch and real-time workloads
Technology Stack
- Orchestration: Airflow, Dagster, Prefect, dbt Cloud
- Data Warehouses: Snowflake, BigQuery, Redshift, Databricks
- Stream Processing: Kafka, Kinesis, Flink, Spark Streaming
- Feature Stores: Feast, Tecton, AWS Feature Store, Databricks Feature Store
- Data Quality: Great Expectations, dbt tests, Monte Carlo, Soda
- Transformation: dbt, Spark, Pandas, Polars
Approach
We follow modern data engineering best practices:
- Treat data pipelines as code with version control and CI/CD
- Implement comprehensive data testing and validation
- Design for observability with lineage tracking and monitoring
- Optimize for cost, performance, and reliability
- Document assumptions, transformations, and dependencies
Typical Engagement
Data engineering projects often run 4-12 weeks depending on scope. We can work alongside your team, embed as contractors, or deliver turnkey solutions. Common deliverables include pipeline code, feature definitions, monitoring dashboards, and operational runbooks.
Ready to Accelerate?
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