Databricks AI Functions complete guide (with Lakeflow Jobs pipeline setup) скачать в хорошем качестве

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.