Add Quickstart: Build an AI Matching App with Snowflake ML

site/sfguides/src/build-an-ai-matching-app-with-snowflake-ml/assets/README.md

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+# Build an AI Matching App with Snowflake ML - Assets
+
+This folder contains all the source code for the **Build an AI Matching App with Snowflake ML** quickstart guide.
+
+## Quick Start
+
+### Option A: Download Individual Files
+
+Browse the folders below on GitHub and download the files you need.
+
+### Option B: Sparse Clone (recommended for Git users)
+
+Download only this guide's files (~5 MB instead of the full repository):
+
+```bash
+git clone --no-checkout --depth 1 --filter=blob:none --sparse \
+  https://github.com/Snowflake-Labs/sfquickstarts.git
+cd sfquickstarts
+git sparse-checkout set site/sfguides/src/build-an-ai-matching-app-with-snowflake-ml/assets
+```
+
+## Folder Structure
+
+```
+assets/
+├── README.md
+├── app/
+│   ├── streamlit_app.py          # 6-page Streamlit dashboard
+│   └── environment.yml           # Streamlit package dependencies
+├── notebooks/
+│   ├── creator_brand_match.py    # Core ML notebook (Feature Store → Registry → SPCS → API)
+│   └── environment.yml           # Notebook package dependencies
+├── scripts/
+│   └── generate_synthetic_data.py  # Archetype-driven synthetic data generator
+└── sql/
+    ├── 00_setup.sql              # Database, warehouse, compute pool, tables, Dynamic Table
+    └── 99_teardown.sql           # Clean up all demo objects
+```
+
+## What Each File Does
+
+| File | Purpose |
+|------|---------|
+| `sql/00_setup.sql` | Creates the CC_DEMO database, warehouse, compute pool, raw tables, and a Dynamic Table for engagement features |
+| `scripts/generate_synthetic_data.py` | Generates 500K behavioral events across 10K creators and 1K brands using archetype-driven distributions |
+| `notebooks/creator_brand_match.py` | End-to-end ML workflow: Feature Store, model training, Registry, SPCS deployment, Cortex Search, CDP enrichment |
+| `app/streamlit_app.py` | Interactive 6-page dashboard: Feature Store, Model Registry, Inference, CDP Profiles, Cortex Search, Model Monitoring |
+
+## Prerequisites
+
+- Snowflake account (Enterprise Edition or higher recommended)
+- Python 3.9+ with pip or conda
+- A role with privileges to create databases, warehouses, compute pools, and Streamlit apps
+
+## Cleanup
+
+Run `sql/99_teardown.sql` in a Snowsight SQL worksheet to remove all objects created by this guide.

site/sfguides/src/build-an-ai-matching-app-with-snowflake-ml/assets/app/environment.yml

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+name: creator_match_demo
+channels:
+  - snowflake
+dependencies:
+  - streamlit
+  - snowflake-snowpark-python
+  - pandas