Data Platform & Analytics · Chapter 8 of 8
Serving the AI workload
AI appears in 92% of these postings. What it actually asks of a data platform is specific.
Almost every data-platform posting now mentions AI, and most of the time it means one of three concrete demands rather than anything exotic.
What AI workloads actually need from you
| Demand | What it means | The trap |
|---|---|---|
| Feature consistency | The same feature computed identically for training and serving | Two implementations that drift — the single most common cause of a model that works offline and not online |
| Point-in-time correctness | Training data must reflect what was known then | Leakage: joining a dimension as it is now, so the model learns the future |
| Documents for retrieval | Chunked, embedded, indexed, permission-filtered | The index becomes a copy of everything with none of the source access control |
| Throughput for training | Feeding accelerators fast enough that they are not idle | Object-store latency turning an expensive GPU fleet into a waiting room |
The two you will be asked about
Point-in-time correctness is the one that separates people who have shipped a model from people who have not. If you join today's customer dimension onto a two-year-old event to build training data, the model sees attributes that did not exist yet, scores brilliantly offline and fails in production. The fix is temporal joins against a dimension that retains history — which is why chapter 2's insistence on slowly-changing dimensions is not academic.
Permission-filtered retrieval is the one that becomes a security incident. Documents that were access-controlled in their source system get embedded into one index that everybody can query, and the permission model is silently gone. The control is to filter retrieval by the asking user's entitlements at query time, and to test it with two accounts of different privilege asking the same question.
What this means for the role
The data platform is now upstream of the AI platform, which raises the stakes on everything in this book: a wrong number in a dashboard is embarrassing, and the same wrong number in a feature store is a model making decisions on it at scale.
For the model side of this, read RAG — what it fixes and what it does not and Evaluation.