Snowflake-Labs/sf-samples
Sep 2025 – Sep 2025
1 Month active
Languages Used
Jupyter NotebookMarkdownPythonSQL
Technical Skills
AWS SageMakerData AnalysisData MigrationDocumentationMachine LearningPython
Ranjeeta Pegu
PROFILE
Ranjeeta Pegu
Ranjeeta Pegu developed a foundational migration workflow for the Snowflake-Labs/sf-samples repository, focusing on seamless integration between AWS SageMaker and Snowflake environments. She restructured the repository to support end-to-end data migration, providing comprehensive documentation and an About_Data.md to clarify dataset context. Ranjeeta authored two Jupyter notebooks that demonstrate customer churn prediction using XGBoost, implemented in both local Python and Snowflake ML environments. Her work emphasized reproducibility and ease of onboarding, enabling teams to experiment with migration scenarios efficiently. By leveraging Python, SQL, and Snowflake, she established a robust framework for accelerating SageMaker-to-Snowflake adoption and experimentation.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
664
Activity Months1
Your Network
329 people
Work History
September 2025
1 Commits • 1 Features
Sep 1, 2025September 2025 monthly summary for Snowflake-Labs/sf-samples focused on enabling end-to-end SageMaker-to-Snowflake migration workflows. Delivered a foundational repository restructure with a detailed migration-readme, an About_Data.md dataset description, and two Jupyter notebooks demonstrating customer churn prediction in local Python and Snowflake ML environments. The effort enhances reproducibility, onboarding, and readiness for migration experiments, positioning the team to accelerate adoption of SageMaker-to-Snowflake workflows.
1 Commits • 1 Features
Sep 1, 2025September 2025 monthly summary for Snowflake-Labs/sf-samples focused on enabling end-to-end SageMaker-to-Snowflake migration workflows. Delivered a foundational repository restructure with a detailed migration-readme, an About_Data.md dataset description, and two Jupyter notebooks demonstrating customer churn prediction in local Python and Snowflake ML environments. The effort enhances reproducibility, onboarding, and readiness for migration experiments, positioning the team to accelerate adoption of SageMaker-to-Snowflake workflows.
September 2025
Activity
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture80.0%
Performance60.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Jupyter NotebookMarkdownPythonSQL
Technical Skills
AWS SageMakerData AnalysisData MigrationDocumentationMachine LearningPythonSnowflakeXGBoost
Repositories Contributed To
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