Foundation Models for Big Data and Prompt Tuning
summary
Describes building foundation models at scale using cloud tools like AWS and Amazon Bedrock, advising parallel processing and auto‑scaling to handle terabyte‑level ingestion and cut training time, tagging datasets for data versioning and lineage, encrypting storage and using role‑based IAM for security. Recommends few‑shot setups (≈10 examples per class), iterative refinement with RLHF and active learning, and standardized prompt templates and libraries; claims these practices raise task accuracy, reduce cost, and speed deployment.
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chapters (11)
- 0:00Foundational models introduction
- 2:19Transformer basics
- 3:04Pre-training data challenges
- 6:37Scalability in data management
- 8:25Data quality control
- 10:11Storage scalability strategies
- 11:34Fine-tuning importance
- 13:18Prompt engineering impact
- 16:32Few-shot learning techniques
- 18:58Multi-prompt strategies
- 19:18Future prompt engineering trends
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