Teradata/jupyter-demos
Mar 2025 – May 2025
3 Months active
Languages Used
PythonJSONCSV
Technical Skills
Data EngineeringModelOpsData AnalysisData VisualizationDeep LearningJupyter Notebooks
Forbidden_Devil
PROFILE
Forbidden_devil
Pratik Somwanshi contributed to the Teradata/jupyter-demos repository by developing features that enhanced data access, visualization, and machine learning workflows. He implemented developer-focused data retrieval guidance in Jupyter Notebooks, using Python and JSON to streamline onboarding and reproducibility for ModelOps. Pratik refactored notebook compatibility with the latest teradatamlwidgets package, improved environment support for deep learning with torch, and fixed a data join bug to ensure accurate population visualizations. He also delivered a banking customer churn analysis use case, integrating CSV data ingestion and dependency management for ML pipelines. His work demonstrated depth in data engineering and practical problem-solving.
Overall Statistics
Feature vs Bugs
75%Features
Repository Contributions
6Total
Bugs
1
Commits
6
Features
3
Lines of code
65,618
Activity Months3
Your Network
33 people
Work History
May 2025
2 Commits • 1 Features
May 1, 2025May 2025 monthly highlights for Teradata/jupyter-demos: Delivered Banking Customer Churn Analysis use case with data ingestion and ML pipeline setup, enabling sentiment/topic modeling from complaints CSV and integration of pre-built dependencies (engine-8.0.0.jar, tokenizer-0.0.1-BETA.jar).
2 Commits • 1 Features
May 1, 2025May 2025 monthly highlights for Teradata/jupyter-demos: Delivered Banking Customer Churn Analysis use case with data ingestion and ML pipeline setup, enabling sentiment/topic modeling from complaints CSV and integration of pre-built dependencies (engine-8.0.0.jar, tokenizer-0.0.1-BETA.jar).
May 2025
April 2025
3 Commits • 1 Features
Apr 1, 2025April 2025 monthly summary for Teradata/jupyter-demos focused on delivering updated notebook compatibility, enabling ML-oriented environment improvements, and ensuring accurate visualizations. Key work delivered includes a Plot Notebook compatibility update aligned with the latest teradatamlwidgets package and an environment upgrade via a torch cell for language model use cases. A critical bug fix was implemented in the Geom Plot data join logic to correctly map population data to state shapes, improving visualization reliability and decision support.
April 2025
3 Commits • 1 Features
Apr 1, 2025April 2025 monthly summary for Teradata/jupyter-demos focused on delivering updated notebook compatibility, enabling ML-oriented environment improvements, and ensuring accurate visualizations. Key work delivered includes a Plot Notebook compatibility update aligned with the latest teradatamlwidgets package and an environment upgrade via a torch cell for language model use cases. A critical bug fix was implemented in the Geom Plot data join logic to correctly map population data to state shapes, improving visualization reliability and decision support.
March 2025
1 Commits • 1 Features
Mar 1, 2025March 2025: Delivered developer-focused data access guidance in the 15_ModelOps_Feature_Engineering notebook within Teradata/jupyter-demos, enabling easy retrieval of demo data from cloud storage via foreign tables or local downloads, with clear trade-offs, markdown explanations, and a ready-to-run data retrieval cell. This work improves onboarding, reproducibility, and demo reliability for ModelOps workflows, aligning with the team's goals of accessible data for rapid experimentation.
1 Commits • 1 Features
Mar 1, 2025March 2025: Delivered developer-focused data access guidance in the 15_ModelOps_Feature_Engineering notebook within Teradata/jupyter-demos, enabling easy retrieval of demo data from cloud storage via foreign tables or local downloads, with clear trade-offs, markdown explanations, and a ready-to-run data retrieval cell. This work improves onboarding, reproducibility, and demo reliability for ModelOps workflows, aligning with the team's goals of accessible data for rapid experimentation.
March 2025
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Quality Metrics
Correctness96.8%
Maintainability96.8%
Architecture93.4%
Performance93.4%
AI Usage20.0%
Skills & Technologies
Programming Languages
CSVJSONPython
Technical Skills
Data AnalysisData EngineeringData VisualizationDeep LearningDependency ManagementJupyter NotebooksMachine LearningModelOpsPythonPython Development
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