Enterprise ML feature engineering and model management for IBM Netezza. Keep your entire machine learning pipeline in-database — no data movement, no external ML platform, no petabyte-scale ETL to manage.
The Approach
Most enterprise ML deployments start with a problem that has nothing to do with the model: getting the data out of the warehouse and into an external ML platform. For organisations running IBM Netezza with petabyte-scale data, that movement is slow, expensive, and creates a separate data governance problem.
Feature Factory takes a different approach. It uses Netezza's INZA (In-database Analytics) capability to run feature engineering, model training, and scoring directly inside the database — using Netezza's own MPP engine. Your data never moves. Your governance controls remain in place. Your ML pipeline runs at warehouse scale without a separate platform.
The philosophy is pragmatic: classical machine learning on well-governed data, applied to the core business decisions that drive revenue, manage risk, and improve operations. Feature Factory is not an AI hype vehicle — it is an engineering tool for getting reliable ML into production on the data you already have.
Eight Engines
Define and compute ML features using Netezza SQL pushdown. Manage feature sets with full versioning and a feature store. Features are computed in-database using Netezza's MPP engine — no extraction required.
Train, version, deploy, and run predictions using INZA in-database analytics. AutoML recommends algorithms (Decision Trees, K-Means, GLM). Experiment tracking and full model history are maintained automatically.
Automated table and column profiling with quality scoring across completeness, distribution, and pattern validation. Quality rule definitions execute on schedule and report against configurable thresholds.
Business domain hierarchy, data stewardship, business glossary, and lineage tracking. Impact analysis shows downstream effects of changes. Full audit trail for every operation on governed data assets.
Monitors feature and data drift over time using PSI (Population Stability Index) metrics. Configurable alert thresholds notify you when data distributions shift enough to affect model reliability.
GDPR, CCPA, and EU AI Act compliance built in. Handles DSAR management, consent tracking, automated erasure workflows, ROPA, and DPIA documentation — with email mailbox polling for incoming requests.
Spatial feature engineering using Netezza Spatial ESRI. Distance calculations, region lookups, spatial aggregations, and UK postcode geocoding with LSOA/MSOA lookups for demographic analysis.
Enriches data with UK demographic profiles (LSOAs: population, age, income, OAC classification). Supports multi-country lookups including world cities and country centroids. Demographic feature templates accelerate common enrichment patterns.
Regulatory Compliance
Feature Factory includes built-in compliance automation for GDPR, CCPA, and the EU AI Act — the regulatory frameworks most relevant to organisations using ML on customer and behavioural data.
These are not add-ons. Compliance functionality is built into the platform from the ground up, covering the operational workflows (DSAR, erasure, consent) and the documentation requirements (ROPA, DPIA, AI system documentation) that regulated industries need.
Licensing
Feature Factory is licensed per Netezza instance. The Community tier is free, with a delayed release schedule. Professional and Enterprise tiers provide current releases and additional support.
For developers, non-profit organisations, academic use, and teams evaluating the platform before a commercial deployment.
For small-to-medium organisations running Feature Factory in production on a single Netezza instance.
For organisations requiring immediate access to current releases, priority support, and direct engineering access.
Get in touch to discuss whether Feature Factory is a fit for your environment, or visit the Feature Factory site to learn more.