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
Developed a foundational migration workflow in the Snowflake-Labs/sf-samples repository, focusing on end-to-end integration between AWS SageMaker and Snowflake. The work involved restructuring the repository to support reproducible migration experiments, including comprehensive documentation and an About_Data.md file describing the dataset. Delivered two Jupyter notebooks that demonstrate customer churn prediction using XGBoost in both local Python and Snowflake ML environments, providing clear, practical examples for onboarding and experimentation. Leveraged skills in Python, SQL, and data migration to streamline the transition process, enabling teams to adopt SageMaker-to-Snowflake workflows with improved clarity, reproducibility, and readiness for future machine learning projects.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
664
Activity Months1
Your Network
378 peopleWork 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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