DataEngBytes 2023 - SYD-T2-02 - Vinny Vijekumaar & Zak Sheikh

on YouTube ↗21m

" summary

Claims LLM work is a data engineering problem not just data science and shows it end to end on Databricks Data+AI: use AI_GENERATE_TEXT (OpenAI GPT-3.5) to generate JSON mock records with PII, parse into tables with FROM_JSON, wrap and serve a fine‑tuned Hugging Face model (MPT-7B-instruct) as an MLflow python model and call it via AI_QUERY to detect, extract and mask PII. Result is structured PII profiles and masked data in SQL within about an hour, ready for entity resolution or sentiment pipelines.

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" chapters (15)

1. 0:00LLM-driven data engineering
2. 2:06Speaker backgrounds
3. 3:11Company focus for LLM adoption
4. 4:37LLM internal tooling use cases
5. 6:42Enhancing release notes with LLMs
6. 7:23Enriching unstructured data with LLMs
7. 8:50Commercial vs self-hosted LLMs
8. 10:14LLM Ops and model management
9. 10:34Detecting and masking PII with LLMs
10. 14:29Evaluating open-source models
11. 16:13MLflow for SQL models
12. 17:20PII detection with AI functions
13. 18:43Downstream applications of enriched data
14. 19:24Why LLMs for data engineers
15. 20:05LLM trade-offs

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