From model serving to feature engineering, how caching accelerates and secures intelligent systems
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🚀 Why AI/ML Needs More Than Just Speed
In traditional web systems, caching is simple: store a query result, return it quickly next time. In AI/ML, caching goes beyond speed — it becomes part of the intelligence fabric:
- 💰 Cost efficiency: inference is GPU-expensive; recomputation can blow budgets.
- ⚡ Scalability: millions of users requesting recommendations or translations.
- 🔁 Reproducibility: cached intermediates make experiments auditable.
- 🔒 Compliance: sensitive embeddings, features, or predictions must be cached safely.
Caching is now infrastructure for intelligence, not just optimization.
🧩 Expanded Challenges of AI/ML Workloads
Model Size & Distribution
- Foundation models can be 10–100 GB.
- Downloading them at runtime = cold-start hell.
- Edge caching of model weights reduces startup latency.
Inference Costs
- GPU inference = expensive.