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Databricks AI Functions complete guide (with Lakeflow Jobs pipeline setup)

In this complete end-to-end tutorial, we explore how to run LLM models directly inside Databricks SQL using built-in AI Functions.

No external API calls.
No Python orchestration.
Just pure SQL + AI.

In this video, you’ll learn:

• How to download a Kaggle sentiment dataset
• Ingest data into Databricks
• Use AI SQL functions like:

ai_analyze_sentiment
ai_classify
ai_fix_grammar
ai_extract
ai_query

• Extract structured output using schema enforcement
• Run SQL inside notebooks using %sql
• Use Spark variables
• Create a production Databricks Job
• Schedule it using cron (every 1 minute demo)

By the end of this video, you’ll understand how to move from experimentation to production-ready AI pipelines using only Databricks SQL.

This is ideal for:
Data Engineers
ML Engineers
Analytics Engineers
Platform Teams

If you’re working in modern data platforms, this changes how you think about GenAI inside the warehouse.

Subscribe for more advanced Databricks, AI Engineering, and production ML content.

Видео Databricks AI Functions complete guide (with Lakeflow Jobs pipeline setup) канала datageekrj
databricks databricks sql databricks ai functions run llm in sql llm in sql genai databricks databricks tutorial databricks jobs ai_query databricks ai_extract sql ai_analyze_sentiment ai_classify sql ai_fix_grammar production llm pipeline data engineering tutorial sentiment analysis databricks unity catalog spark sql generative ai in data engineering llm production demo databricks notebook kaggle sentiment analysis modern data stack ai
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3 марта 2026 г. 17:27:52
01:00:44
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