aws-samples/amazon-nova-samples
Nov 2025 – Nov 2025
1 Month active
Languages Used
JSONPython
Technical Skills
AWS SageMakerData EngineeringMachine LearningNatural Language Processingclean code practicesrepository management
Zeek Granston
PROFILE
Zeek Granston
During November 2025, Zeekg developed end-to-end text-to-SQL customization workflows for the aws-samples/amazon-nova-samples repository, enabling customers to tailor Amazon Nova models using AWS SageMaker and Bedrock. Zeekg created comprehensive Jupyter notebooks that guided users through data preparation, fine-tuning, model training, and deployment, supported by Python-based utility functions for data handling. To improve repository quality and onboarding, Zeekg also removed unnecessary configuration files and the .vscode folder, streamlining contributor experience. This work demonstrated depth in data engineering and natural language processing, providing practical, reusable solutions for NL2SQL tasks that accelerate adoption across diverse schemas and data sources.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
2
Lines of code
5,140
Activity Months1
Your Network
1563 peopleShared Repositories
77
Work History
November 2025
2 Commits • 2 Features
Nov 1, 2025In 2025-11, focused on enabling customers to customize Amazon Nova for text-to-SQL generation and improving repository quality. Delivered end-to-end customization examples for Bedrock and SageMaker, backed by a detailed Jupyter notebook, data preparation steps, fine-tuning, training, deployment guidance, and utility helpers. Cleaned up the repository by removing the .vscode folder and irrelevant configuration to improve onboarding and contributor experience. These efforts provide a practical reference to accelerate time-to-value for NL2SQL workflows and demonstrate proficiency with AWS ML platforms and Python tooling, delivering clear business value.
2 Commits • 2 Features
Nov 1, 2025In 2025-11, focused on enabling customers to customize Amazon Nova for text-to-SQL generation and improving repository quality. Delivered end-to-end customization examples for Bedrock and SageMaker, backed by a detailed Jupyter notebook, data preparation steps, fine-tuning, training, deployment guidance, and utility helpers. Cleaned up the repository by removing the .vscode folder and irrelevant configuration to improve onboarding and contributor experience. These efforts provide a practical reference to accelerate time-to-value for NL2SQL workflows and demonstrate proficiency with AWS ML platforms and Python tooling, delivering clear business value.
November 2025
Activity
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Quality Metrics
Correctness100.0%
Maintainability90.0%
Architecture100.0%
Performance90.0%
AI Usage50.0%
Skills & Technologies
Programming Languages
JSONPython
Technical Skills
AWS SageMakerData EngineeringMachine LearningNatural Language Processingclean code practicesrepository management
Repositories Contributed To
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