Teradata/jupyter-demos
Nov 2024 – Oct 2025
10 Months active
Languages Used
Jupyter NotebookPythonSQLMarkdownHTMLJavaScriptCSSR
Technical Skills
AutoMLData AnalysisData EngineeringData ScienceData VisualizationDependency Management
Shilpa Nalkande
PROFILE
Shilpa Nalkande
Shilpa Nalkande developed a comprehensive suite of analytics and machine learning notebooks for the Teradata/jupyter-demos repository, focusing on end-to-end workflows for use cases such as fraud detection, hospital readmission, and customer churn. She engineered reusable pipelines and integrated AutoML, ModelOps, and feature store capabilities, enabling scalable model deployment and reproducible experimentation. Her work emphasized maintainability through code refactoring, documentation updates, and dashboard integration, while also addressing data handling and model evaluation challenges. Using Python, SQL, and Jupyter Notebooks, Shilpa delivered solutions that improved onboarding, accelerated analytics iteration, and strengthened the reliability and governance of in-database machine learning workflows.
Overall Statistics
Feature vs Bugs
91%Features
Repository Contributions
136Total
Bugs
5
Commits
136
Features
48
Lines of code
168,590
Activity Months10
Your Network
35 people
Work History
October 2025
4 Commits • 2 Features
Oct 1, 2025Month 2025-10 focused on delivering enhanced analytics notebooks and correcting data handling issues to improve reliability and business value across analytics use cases in Teradata/jupyter-demos.
4 Commits • 2 Features
Oct 1, 2025Month 2025-10 focused on delivering enhanced analytics notebooks and correcting data handling issues to improve reliability and business value across analytics use cases in Teradata/jupyter-demos.
October 2025
July 2025
4 Commits • 2 Features
Jul 1, 2025July 2025 monthly summary for Teradata/jupyter-demos: Delivered two customer-facing notebook enhancements for the Hospital Readmission AutoML workflow and improved notebook navigation with dashboard integration. The work enhanced clarity, actionable analytics, and usability, enabling faster, more reliable decision support for clinicians and data scientists.
July 2025
4 Commits • 2 Features
Jul 1, 2025July 2025 monthly summary for Teradata/jupyter-demos: Delivered two customer-facing notebook enhancements for the Hospital Readmission AutoML workflow and improved notebook navigation with dashboard integration. The work enhanced clarity, actionable analytics, and usability, enabling faster, more reliable decision support for clinicians and data scientists.
June 2025
6 Commits • 3 Features
Jun 1, 2025June 2025 — Delivered three key features in the Teradata/jupyter-demos repo, strengthening demo readiness, reproducibility, and end-to-end analytics workflows. Focused on cross-notebook refactor, demo artifacts, and an AutoML-based hospital readmission use case. No major bugs reported this month; changes emphasize maintainability, clearer analytics integration, and end-to-end ML guidance.
6 Commits • 3 Features
Jun 1, 2025June 2025 — Delivered three key features in the Teradata/jupyter-demos repo, strengthening demo readiness, reproducibility, and end-to-end analytics workflows. Focused on cross-notebook refactor, demo artifacts, and an AutoML-based hospital readmission use case. No major bugs reported this month; changes emphasize maintainability, clearer analytics integration, and end-to-end ML guidance.
June 2025
May 2025
8 Commits • 2 Features
May 1, 2025May 2025 monthly summary focused on delivering AI-driven CX capabilities and product discovery enhancements in Teradata/jupyter-demos, with a strong emphasis on documentation, onboarding, and cross-use-case navigation. No major bugs reported; primary work centered on feature completion, code/docs quality, and system integration readiness.
May 2025
8 Commits • 2 Features
May 1, 2025May 2025 monthly summary focused on delivering AI-driven CX capabilities and product discovery enhancements in Teradata/jupyter-demos, with a strong emphasis on documentation, onboarding, and cross-use-case navigation. No major bugs reported; primary work centered on feature completion, code/docs quality, and system integration readiness.
April 2025
8 Commits • 2 Features
Apr 1, 2025April 2025: Delivered the end-to-end Fraud Detection Demo for Teradata/jupyter-demos, featuring ClearScape Analytics and ModelOps. Implemented in-database SQL training for XGBoost and Decision Forest, demonstrated traditional and AutoML approaches, and integrated model lifecycle management with a feature store. Completed updates to demo notebooks and visual assets, and performed targeted maintenance to keep demos current with JavaScript library changes and Telco churn notebook documentation. Addressed version-related issues in AutoML demos and aligned review processes (titles, comments) to improve clarity. Overall, these efforts strengthen the value proposition of in-database ML, governance through ModelOps, and the developer experience.
8 Commits • 2 Features
Apr 1, 2025April 2025: Delivered the end-to-end Fraud Detection Demo for Teradata/jupyter-demos, featuring ClearScape Analytics and ModelOps. Implemented in-database SQL training for XGBoost and Decision Forest, demonstrated traditional and AutoML approaches, and integrated model lifecycle management with a feature store. Completed updates to demo notebooks and visual assets, and performed targeted maintenance to keep demos current with JavaScript library changes and Telco churn notebook documentation. Addressed version-related issues in AutoML demos and aligned review processes (titles, comments) to improve clarity. Overall, these efforts strengthen the value proposition of in-database ML, governance through ModelOps, and the developer experience.
April 2025
March 2025
14 Commits • 3 Features
Mar 1, 2025March 2025 accomplishments for Teradata/jupyter-demos: delivered a consolidated suite of Jupyter Notebook recipes for Teradata Vantage core functions with built-in help and usage guidance to accelerate onboarding and self-service for data scientists; expanded coverage with an Imbalanced Data Handling Notebook and related edits; added additional notebook content covering video Gemini analysis, Telco churn AutoML outputs, and ModelOps links; completed documentation and review improvements across notebooks; progress toward a standardized, reusable data science workflow and faster experimentation.
March 2025
14 Commits • 3 Features
Mar 1, 2025March 2025 accomplishments for Teradata/jupyter-demos: delivered a consolidated suite of Jupyter Notebook recipes for Teradata Vantage core functions with built-in help and usage guidance to accelerate onboarding and self-service for data scientists; expanded coverage with an Imbalanced Data Handling Notebook and related edits; added additional notebook content covering video Gemini analysis, Telco churn AutoML outputs, and ModelOps links; completed documentation and review improvements across notebooks; progress toward a standardized, reusable data science workflow and faster experimentation.
February 2025
61 Commits • 25 Features
Feb 1, 2025February 2025 performance summary for Teradata/jupyter-demos: Delivered a broad enhancement of notebook infrastructure and analytics recipes, advancing end-to-end demonstration capabilities and operational readiness. Notable progress spans control notebook improvements, a Telco end-to-end demo setup, ModelOps integration and ROC metric enhancements, and a substantial expansion of recipe notebooks across DFFT/IDFFT, Convolve, and time-series analytics. UI/UX improvements, help guidance, and repository hygiene also contributed to improved developer experience and maintainability. These efforts collectively enable faster experimentation, repeatable demonstrations, and stronger governance for analytics workflows.
61 Commits • 25 Features
Feb 1, 2025February 2025 performance summary for Teradata/jupyter-demos: Delivered a broad enhancement of notebook infrastructure and analytics recipes, advancing end-to-end demonstration capabilities and operational readiness. Notable progress spans control notebook improvements, a Telco end-to-end demo setup, ModelOps integration and ROC metric enhancements, and a substantial expansion of recipe notebooks across DFFT/IDFFT, Convolve, and time-series analytics. UI/UX improvements, help guidance, and repository hygiene also contributed to improved developer experience and maintainability. These efforts collectively enable faster experimentation, repeatable demonstrations, and stronger governance for analytics workflows.
February 2025
January 2025
21 Commits • 5 Features
Jan 1, 2025Jan 2025 monthly summary for Teradata/jupyter-demos: Delivered a notebook-driven data science studio with scalable templates and recipes, enhanced the feature engineering and training pipeline, added rapid setup notebooks for experimentation, and improved user feedback and onboarding through documentation and UX improvements. This month focused on enabling faster experimentation, reproducibility, and readiness for ModelOps workflows, while maintaining a strong emphasis on performance through reduced pandas usage and SHAP integration.
January 2025
21 Commits • 5 Features
Jan 1, 2025Jan 2025 monthly summary for Teradata/jupyter-demos: Delivered a notebook-driven data science studio with scalable templates and recipes, enhanced the feature engineering and training pipeline, added rapid setup notebooks for experimentation, and improved user feedback and onboarding through documentation and UX improvements. This month focused on enabling faster experimentation, reproducibility, and readiness for ModelOps workflows, while maintaining a strong emphasis on performance through reduced pandas usage and SHAP integration.
December 2024
3 Commits • 2 Features
Dec 1, 2024December 2024 (Teradata/jupyter-demos) - Delivered two new feature notebooks demonstrating end-to-end ML workflows with Teradata Vantage and restored essential deployment artifacts to unblock runtime environments. This work enhances model deployment readiness, accelerates experimentation, and demonstrates practical data science tooling for customers.
3 Commits • 2 Features
Dec 1, 2024December 2024 (Teradata/jupyter-demos) - Delivered two new feature notebooks demonstrating end-to-end ML workflows with Teradata Vantage and restored essential deployment artifacts to unblock runtime environments. This work enhances model deployment readiness, accelerates experimentation, and demonstrates practical data science tooling for customers.
December 2024
November 2024
7 Commits • 2 Features
Nov 1, 20242024-11 Monthly Summary – Teradata/jupyter-demos Key features delivered: - Advertising Sales Prediction Notebook and Evaluation Pipeline: end-to-end notebook using teradataml OpenSourceML with Vantage, including data connection, loading/exploration, train-test split, SGDRegressor with RandomizedSearchCV, deployment, evaluation, and resource cleanup. Includes environment standardization and ROC/AUC evaluation improvements. - Enterprise Feature Store (EFS) integration with AutoML for Customer Churn: end-to-end use case covering data ingestion, feature engineering, model training, and prediction with Teradata Vantage; demonstrates feature reuse across models. Major bugs fixed (stability and quality): - Dependency and environment stabilizations (updated teradatasqlalchemy installation) and ROC evaluation fixes to ensure repeatable, reliable experiments. - Code review-driven improvements and minor fixes to align notebooks with production standards. Overall impact and accomplishments: - Delivered two end-to-end, production-oriented analytics features that enable faster experimentation, reproducible pipelines, and scalable model deployment on Teradata platforms. - Demonstrated business value through improved churn prediction capabilities and data-driven advertising analysis, supporting better customer retention strategies and revenue forecasting. - Established reusable feature pipelines via EFS, enabling cross-model feature reuse and accelerated model iteration. Technologies/skills demonstrated: - Teradata tools: teradataml, teradatasqlalchemy, Teradata Vantage - ML tooling: SGDRegressor, RandomizedSearchCV, AutoML concepts, ROC/AUC evaluation - Data engineering: data ingestion, feature engineering, deployment, evaluation, resource cleanup - Collaboration and code hygiene: review-driven improvements, environment standardization, and dependency management.
November 2024
7 Commits • 2 Features
Nov 1, 20242024-11 Monthly Summary – Teradata/jupyter-demos Key features delivered: - Advertising Sales Prediction Notebook and Evaluation Pipeline: end-to-end notebook using teradataml OpenSourceML with Vantage, including data connection, loading/exploration, train-test split, SGDRegressor with RandomizedSearchCV, deployment, evaluation, and resource cleanup. Includes environment standardization and ROC/AUC evaluation improvements. - Enterprise Feature Store (EFS) integration with AutoML for Customer Churn: end-to-end use case covering data ingestion, feature engineering, model training, and prediction with Teradata Vantage; demonstrates feature reuse across models. Major bugs fixed (stability and quality): - Dependency and environment stabilizations (updated teradatasqlalchemy installation) and ROC evaluation fixes to ensure repeatable, reliable experiments. - Code review-driven improvements and minor fixes to align notebooks with production standards. Overall impact and accomplishments: - Delivered two end-to-end, production-oriented analytics features that enable faster experimentation, reproducible pipelines, and scalable model deployment on Teradata platforms. - Demonstrated business value through improved churn prediction capabilities and data-driven advertising analysis, supporting better customer retention strategies and revenue forecasting. - Established reusable feature pipelines via EFS, enabling cross-model feature reuse and accelerated model iteration. Technologies/skills demonstrated: - Teradata tools: teradataml, teradatasqlalchemy, Teradata Vantage - ML tooling: SGDRegressor, RandomizedSearchCV, AutoML concepts, ROC/AUC evaluation - Data engineering: data ingestion, feature engineering, deployment, evaluation, resource cleanup - Collaboration and code hygiene: review-driven improvements, environment standardization, and dependency management.
Activity
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Quality Metrics
Correctness95.2%
Maintainability94.8%
Architecture93.6%
Performance89.0%
AI Usage23.6%
Skills & Technologies
Programming Languages
CSSHTMLJavaScriptJupyter NotebookMarkdownPythonRSQL
Technical Skills
AIAI/MLAPI IntegrationAnomaly DetectionAutoMLBYOMBYOM (Bring Your Own Model)Business IntelligenceCSSCloud ComputingCloud ServicesCode CleanupContent ManagementContent RefinementContent Update
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