AI Data Infrastructure Foundation. @ai-data-foundation

Most AI initiatives stall on data, not models. This package sets up the pipelines, versioning, feature flow, and storage patterns that make ML and AI work repeatable. The finish state is an ML-ready data foundation running in your cloud: governed ingestion, versioned datasets, and a documented path from raw data to training and inference.

The build uses proven tooling fitted to your stack: orchestration with Airflow or Prefect, transformations in dbt, and lakehouse or warehouse storage on Databricks, Snowflake, or your cloud's native services. Where the goal is GenAI, the same foundation covers vector storage and retrieval patterns so RAG workloads stay fresh instead of going stale.

Governed ingestion pipelines

Scheduled, monitored pipelines that move data from your source systems into versioned, query-ready storage.

Dataset and model versioning

Version control for datasets, features, and model artefacts, so every experiment and training run is reproducible.

Feature flow design

A documented path from raw data to features, training sets, and inference inputs, with clear ownership at each step.

Storage and access patterns

Lakehouse or warehouse layout, access controls, and cost-aware retention rules matched to your workloads.

1. Scope: map source systems, target AI use cases, and the current data estate. Agree the foundation architecture and acceptance criteria.
2. Build: stand up pipelines, versioning, and storage in your cloud accounts, with tests and monitoring on every flow.
3. Handover: walk your team through the architecture, hand over runbooks and documentation, and agree the first ML workload to run on it.

The AI Data Infrastructure Foundation is a menu item on the Scrums.com delivery catalog, available at every plan tier. Add it to your plan backlog and your delivery team schedules it like any other item — scoped, tracked, and reported through the SEOP. See Delivery Plan Tiers.

Does this package include building an ML model?

No. It delivers the data foundation that models depend on. The Forecasting & Predictive Analytics Pilot or GenAI Pilot Build menu items are the natural next step once the foundation is live.

What do we need before the sprint starts?

Access to your cloud accounts and source systems, plus a named owner for data decisions. No existing pipeline tooling is required; the sprint can start from a blank estate.

Who operates the foundation after handover?

Your team owns it. Everything is codified and documented, so it runs without us. If you want ongoing operation of pipelines and models, the Model Ops Retainer covers that as a managed service.