Week 19: Exercises & Checklist

Graded exercises

1. Easy: Run notebooks/01-gemini-multimodal-ocr.ipynb to completion. It renders bill-of-lading images locally, extracts fields to structured JSON with Gemini vision, and prints per-field accuracy plus a cost-per-document estimate. Record both numbers in the Week 19 tracker sheet.

2. Standard: Re-run the extraction loop on a different Gemini model rung (for example gemini-2.5-flash-lite vs gemini-2.5-flash). Add a markdown cell with a two-row table of accuracy vs cost per document, and write one line on whether the cheaper model clears your accuracy bar.

3. Stretch: Load the extracted JSON into BigQuery and run a single ML.GENERATE_TEXT call that classifies each row (for example freight_terms consistency or a commodity risk label). Document the table schema and the exact SQL; note where the free-tier boundary is for this data.

4. Portfolio: Commit a vertex-deployment-guide.md (AI Studio vs Vertex, OCR accuracy table, ADK agent, eval score, cost per document). This is the Google column of the three-cloud comparison matrix completed in Week 20.

Hints

1. Easy: Record OVERALL_FIELD_ACCURACY and COST_PER_DOCUMENT_USD; image rendering and accuracy run locally without a key, but the real numbers need GOOGLE_API_KEY.
2. Standard: Change the MODEL constant in the gemini_extract cell and re-run on the same 4 images; compare accuracy vs cost-per-document in the two-row table.
3. Stretch: ML.GENERATE_TEXT needs a remote model registered first (CREATE MODEL … REMOTE WITH CONNECTION); load the extracted JSON, then run one classification query and note the schema.
4. Portfolio: Lead with the AI Studio-vs-Vertex decision, then the accuracy table, the ADK eval score, and cost per document, those four headings are the Google column.

Checklist (mirrors manifest.json + Excel tracker)

• [ ] Mon: Set up Google Cloud and AI Studio; get an API key; check quotas.
• [ ] Tue: Gemini multimodal lab: BoL images → structured JSON; measure accuracy.
• [ ] Wed: Recreate the support agent with ADK/Agent Engine; compare with the Foundry version.
• [ ] Thu: Run a Vertex evaluation on a golden set; log results to BigQuery.
• [ ] Fri: Use case: end-to-end pipeline (images → clean table); publish cost per document.
• [ ] Sat: Take the Week 19 quiz (quiz.md), pass with 8/10; record the score in Notes.
• [ ] Milestone: Update the Excel tracker; commit the Vertex guide.