Part 10/12:
Two primary approaches exist:
Model-to-data: Deploy models within existing cloud infrastructure, minimizing data transfer.
Data-to-model: Send data outside the organization—riskier in terms of privacy but sometimes necessary.
A scalable architecture requires componentization—caching, retrieval, and knowledge bases—that enables horizontal deployment across use cases, ensuring flexibility and resilience.
3. Cost Management
AI solutions can be costly due to compute and storage needs. Cost optimization strategies include:
Choosing between open-source or proprietary models based on ROI.
Managing tokens and latency.
Leveraging caching to reuse results.
Constantly evaluating the cost-benefit trade-offs to extend experimentation runway and maximize ROI.