From Data Warehouse to AI: Building the Foundation for Machine Learning

The Data Foundation Gap

Every organization that wants to leverage AI and machine learning confronts the same challenge: their data is not ready. Models require clean, well-organized, feature-rich data — and most enterprise data environments were designed for reporting, not machine learning.

Bridging this gap does not require replacing your existing data infrastructure. It requires extending it with capabilities specifically designed to support ML workloads.

What ML Needs from Data Infrastructure

Machine learning workloads have different requirements than traditional analytics:

Feature engineering: ML models consume features — derived data points calculated from raw data. A customer's average order value over 30 days, the number of support tickets in the last quarter, or the sentiment score of recent reviews are all features derived from operational data.

Training data management: Models need large, labeled datasets for training. Managing these datasets — versioning, lineage tracking, and quality monitoring — requires dedicated tooling.

Feature consistency: The same feature must be calculated the same way in training and serving. If a feature is computed differently in your training pipeline versus your production inference pipeline, model performance will degrade.

Data freshness: Some ML features need to reflect the most recent data. A fraud detection model that uses yesterday's transaction patterns is less effective than one using the last hour's patterns.

The Feature Store

A feature store is the central infrastructure component that bridges data engineering and machine learning:

Offline store: Stores historical feature values for model training. Built on your existing data warehouse or lakehouse, the offline store provides point-in-time correct features for any historical date.

Online store: Serves the latest feature values for real-time inference. Built on low-latency data stores (Redis, DynamoDB), the online store provides sub-millisecond feature retrieval for production models.

Feature registry: A catalog of all available features with metadata — description, owner, data source, freshness SLA, and usage statistics. This prevents duplicate feature development and enables feature reuse across models.

Feature computation: Pipelines that calculate features from raw data and populate both offline and online stores. These pipelines must be reliable, scalable, and auditable.

Building the Pipeline

From Data Warehouse to Feature Store

If you already have a well-structured data warehouse, you have a significant head start:

1. Identify candidate features: Work with data scientists to identify which warehouse columns and derived metrics are useful as ML features.
2. Formalize feature definitions: Document the exact computation for each feature, including the time window, aggregation method, and handling of missing values.
3. Build feature pipelines: Create transformation pipelines that compute features from warehouse tables and load them into the feature store.
4. Implement point-in-time joins: Ensure that training data accurately reflects what was known at the time of each historical event, avoiding data leakage from future information.

Handling Real-Time Features

Some features need to be computed from streaming data:

• Sliding window aggregations: Count of events in the last N minutes, average value over the last hour
• Session features: Current session duration, pages viewed in this session
• Interaction features: Time since last purchase, recency of last login

Build streaming feature pipelines using the same computation logic as your batch pipelines. Many feature store platforms support both batch and streaming ingestion.

Data Quality for ML

ML models are particularly sensitive to data quality issues:

Training-serving skew: When the data distribution in production differs from training data. Monitor feature distributions in production and alert when they drift significantly.

Label quality: Supervised learning is only as good as its labels. Implement quality checks on labeled data and track labeling consistency.

Feature drift: The statistical properties of features change over time. A feature that was predictive six months ago may no longer be. Monitor feature importance and retrain when drift is detected.

Missing data patterns: ML models handle missing data differently than analytics queries. Understand your missing data patterns and implement consistent imputation strategies.

Organizational Considerations

Building ML data infrastructure requires collaboration between data engineering, data science, and ML engineering teams:

• Data engineers build and maintain feature pipelines and data quality monitoring
• Data scientists define features, train models, and evaluate performance
• ML engineers deploy models to production, build serving infrastructure, and monitor model performance

The feature store serves as the contract between these teams. Data engineers guarantee feature freshness and quality. Data scientists consume features for training. ML engineers serve features for inference.

Getting Started

Do not attempt to build a comprehensive feature store on day one. Start with a single ML use case:

1. Identify the features it needs
2. Build the minimum pipeline to compute and serve those features
3. Document what works and what does not
4. Generalize the infrastructure for additional use cases

The investment in ML data infrastructure pays compound returns as your organization builds more models. Each new model benefits from features and infrastructure built for previous models.