GCP credit →https://goo.gle/handson-ep2-lab1
Codelab & source code →https://goo.gle/scholar
ML in BigQuery → https://goo.gle/3O5squw
Did you know you can call a Gemini model directly from a SQL query in BigQuery?
In this hands-on codelab, Ayo and Annie do exactly that, and use it to solve a real problem: converting messy, unstructured text into clean, structured data at scale.
This is Episode 1 of our multi-part series where we build a fully functional, data-aware AI agent on Google Cloud.
🛠️ *What we cover:*
* Loading raw text files from Cloud Storage as BigQuery external tables
* Using BQML.GENERATE_TEXT to send prompts to Gemini inside SQL
* Parsing and structuring LLM output using JSON functions in BigQuery
* Building a clean, queryable dataset ready for downstream AI pipelines This pattern is incredibly powerful for any team sitting on a mountain of unstructured documents, and wanting to make them queryable without a heavy ETL pipeline.
Chapters:
0:00 – Intro
1:44 – Claim GCP credit
2:40 – Data project overview
4:31 – Project set up
15:00 – ELT extraction loading transform intro
18:09 – Loading data
26:24 – BigQuery external table
33:52 [BQML] ML Generate In BigQuery
Watch more Hand on AI → https://goo.gle/HowToWithGemini
🔔 Subscribe to Google Cloud Tech → https://goo.gle/GoogleCloudTech
#Gemini #GoogleCloud
Speakers: Ayo Adedeji, Annie Wang
Products Mentioned: Gemini, BigQuery
