Ingest & unify
Source systems land in a governed lakehouse with shared schemas, lineage, and quality checks—one truth for BI and AI alike.
The problem
Most AI projects stall on plumbing, not on models
-
Lake and warehouse split into two truths
Data science works off the lake, BI works off the warehouse, and reconciliation lives in tickets. Every AI use case starts by rebuilding context the org already has.
-
No feature store, just ad-hoc SQL
Features are re-derived in every notebook, drift quietly between training and serving, and no one owns the canonical definition of "active customer" or "high-risk transaction."
-
Governance bolted on after launch
Privacy, lineage, and access controls arrive when legal asks—usually weeks before go-live. Rework is expensive and audit sign-off becomes the new critical path.
-
Data quality is unknown until the model fails
Schema drift, broken upstream feeds, and silent nulls are discovered when a forecast goes sideways in production. There is no early-warning layer the platform owns.
运作方式
A platform AI teams can actually build on
Define features & serve
A feature store with point-in-time correctness, a semantic layer for business logic, and a serving tier ready for real-time and batch AI workloads.
Govern & operate
Access, lineage, observability, and cost controls live in the platform—not in tickets—so new use cases ship in weeks, not quarters.
Architecture adapts to your existing cloud, data stack, and governance model.
面向 AI 的数据平台，而非研究项目
包含内容
Everything you needso AI workloads ship
Ingestion, features, serving, and governance—delivered on Synapse as one operating layer your data and AI teams share.
Ingestion & transformation
Batch and streaming pipelines with schema enforcement, contracts, and quality gates before data hits the lakehouse.
Feature store
Versioned features with point-in-time correctness, online and offline parity, and ownership tracked per feature.
Semantic layer
Business-defined metrics and entities shared by BI and AI—no more two definitions of "churn" or "revenue."
Model serving
Batch, real-time, and embedded serving with traffic shaping, version routing, and shadow deployments.
Governance & lineage
Column-level lineage, access policies, PII tagging, and audit trail across every pipeline and model.
Observability
Data freshness, quality, drift, and cost dashboards—so platform owners catch issues before users do.
技术提供 Synapse
成果
What changes when the platform is ready, not improvised
Results vary by context, data maturity, and scope. We scope honestly before we promise precisely.
3–5x
faster time-to-production for new AI use cases
Orientative—depends on starting maturity and scope.
50%
less effort spent on data preparation per project
Orientative—based on platform-enabled teams.
Single
governed source of truth shared by BI, analytics, and AI
合作方式
From assessment to production platform—without a multi-year program
Assess
Assess
Week 1–3
Map current data stack, top AI use cases, governance gaps, and the architectural target state.
Design
Design
Week 4–6
Define lakehouse layout, feature store contracts, semantic layer scope, and governance model with security and legal.
Build
Build
Week 7–14
Stand up ingestion, feature store, serving, and observability—prove the platform on a first real use case.
Operate & scale
Operate & scale
Week 15+
Hand over to platform team, onboard additional use cases, and expand catalog of governed features.
Timelines vary by source-system complexity, cloud setup, and governance maturity.
延伸阅读
Data, AI, and what to explore next
开始
Ready to build a platform AI use cases actually ship on?
No commitment. We start with a scoped session to map your stack, use cases, and governance needs.