Scalable Data and AI Solutions with Cloud-Native Architecture
summary
Claims a working cloud‑native RAG pipeline that chunks docs, creates embeddings, and stores them in vector database Pinecone while serving containerized front end, prompt template, and LLM inference under Kubernetes with HPA. Uses serverless GPUs or GPU node pools for low‑latency inference, monitors with Grafana/Prometheus, and persists state for vector stores. Cites industry patterns (Netflix scale example and 10–11s deploy cadence) and concrete tools (S3/Delta Lake, Triton, Hugging Face Optimum) as the implementation basis.
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chapters (11)
- 0:00Introduction to Scalable AI and Cloud Native Architecture
- 1:01Key Elements for Building Scalable AI Solutions
- 2:34Understanding Cloud Native Architecture
- 4:20Deep Dive into Microservices and Containers
- 7:47Serverless Computing and Its Benefits
- 9:28Data Management and MLOps
- 11:34Security and Compliance in AI Solutions
- 13:52Optimizing AI Performance
- 14:46Implementing RAG-Based Architecture
- 17:58Future Trends in AI and Cloud Computing
- 18:27Conclusion and Final Thoughts
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