Skip to content

Revise README for SageMaker to Snowflake migration #234

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open
wants to merge 1 commit into
base: main
Choose a base branch
from
Open

Revise README for SageMaker to Snowflake migration #234

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
46 changes: 17 additions & 29 deletions samples/Sagemaker-to-Snowflake/README.md
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -1,40 +1,28 @@
# SageMaker ➜ Snowflake Migration Examples
# SageMaker to Snowflake ML Migration Playbook Examples

This repository provides simple examples to help you migrate machine learning workloads from **AWS SageMaker** to **Snowflake ML**.
This repository is part of a broader **migration playbook** designed to help customers move their **machine learning workloads closer to where their data already resides — in Snowflake**.
By eliminating unnecessary data movement and leveraging Snowflake ML’s native capabilities, customers can accelerate model development, simplify deployment, and improve governance.

### Included Examples

* **XGBoost Classifier**
* **PyTorch Classifier**
* **Image Classification**
* **XGBoost Classifier**
- Training and inference in SageMaker vs. Snowflake ML.
- Demonstrates how Snowflake ML integrates directly with Snowpark DataFrames.

* **PyTorch**
- Compares SageMaker’s distributed training to Snowflake’s built-in support.

* **Image Classification**
- Shows how data can be staged, transformed, and consumed natively in Snowflake.

### Why Migrate?
## Why Snowflake ML?

* Eliminate data movement between platforms
* Use Snowflake’s built‑in governance and security
* Deploy models directly as SQL functions
- **Data stays in Snowflake**: No need to move data out to train, evaluate, or serve models.
- **Seamless integration with Pandas/Snowpark**: Work with Snowflake data as familiar **Pandas DataFrames** or **Snowpark DataFrames**.
- **Unified platform**: Model development, registry, and deployment happen within the same governed environment as your data.
- **Cost & latency benefits**: Avoid data egress and reduce pipeline complexity.

### Quick Start

1. Clone the repo:

```bash
git clone https://github.com/Snowflake-Labs/sf-samples.git
cd sf-samples/samples/ml-sagemaker-to-snowflake
```

2. Install requirements:

```bash
pip install -r requirements.txt
```

3. Run an example (e.g., XGBoost):

```bash
cd xgboost_classifier
python train.py
```

### Repo Structure

Expand Down