Digital & Data/AI Strategy and Transformation
summary
Argues that scale requires concrete choices: cloud hosting with multi‑cloud and GPU‑as‑a‑service for compute, explicit use of vector database (Pinecone) for embeddings, chunking plus RAG pipelines, and measurable KPIs for each use case. Emphasizes four design principles — flexibility, scalability, security, monitoring — and calls out latency tradeoffs for model serving, the need to evaluate LLM capabilities per use case, and MLOps + governance to tame hallucination and operational risk.
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chapters (10)
- 0:00Introduction to Scaling AI with Large Models
- 0:34Key Design Principles for Generative AI
- 1:49Architecting AI-Driven Ecosystems
- 2:12Data Management and Engineering
- 2:39Modern Software Platforms and Digital Channels
- 3:30Deep Dive into Infrastructure and Data Management
- 7:01Operational Challenges and Strategies
- 7:50Key Elements of Generative AI Tech Stack
- 10:13Scaling Generative AI Use Cases
- 11:28Conclusion and Final Thoughts
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