Federated ML & AI Architecture
As of 2026-08-16
Federated ML/AI runs ML where it makes sense relative to the data — move compute to data (or share live) instead of reflexively copying everything into one place. In the BDC era (zero-copy sharing, M041; SAP↔Databricks, M034), federation is often the better architecture. Core question per use case: move data or move compute? Default to federation; copy only for concrete reasons (feature reuse, point-in-time snapshots). Data residency (GDPR M082, utilities/banking M099/M097) makes federation a requirement — centralising for ML is non-compliant; train/score where data lawfully resides, move only models/aggregates. Layers: governed products (M033) as feature source → feature store → training in Databricks → inference embedded back into SAP. Governance must hold across the seam (M077/M081/M034). Embed the round-trip (outputs reach decisions), don't strand ML in a lab. Honest caveat: federation adds design complexity — name the trade-offs. Cross-stack-architect frontier skill.
What you will learn
- Understand the core concepts behind federated ml & ai architecture
- Apply ML in a typical SAP analytics engagement
- Recognize the 3-5 common mistakes and how to avoid them
- Position this skill in your personal brand and rate conversation
Module overview
Federated ML and AI architecture is the discipline of running machine learning where it makes sense relative to the data — bringing compute to data, or sharing data live, instead of the reflexive "copy everything into one place first." In the BDC era, with SAP business data and Databricks ML side by side under zero-copy sharing, federation is no longer a compromise — it is often the better architecture.
The core question: move the data or move the compute? The legacy ML pattern extracts SAP data into a separate ML platform — a copy, with all the duplication, drift, latency, and governance loss that implies. The federated pattern uses zero-copy sharing (companion module M041 Delta Sharing) so ML runs against SAP data in place, with semantics preserved (companion module M034). The senior architecture decision per use case: does this genuinely need a materialised feature set, or can the model train/score against shared data live? Default to federation; copy only when there's a concrete reason (feature reuse, point-in-time snapshots).
Prerequisites
- Intermediate hands-on experience on SAP analytics projects
- Review core concepts first: C008, C006, C004
Outcomes
- Understand the core concepts behind federated ml & ai architecture
- Apply ML in a typical SAP analytics engagement
- Explain the core architecture and decision points for Federated ML & AI Architecture
- Apply a repeatable implementation pattern in a 15-minute lab format
Full module available to members. The full module adds: the decision framework · the end-to-end scenario walkthrough · the KPI scorecard · the anti-patterns · the code blocks · the knowledge check · the diagrams.