impresso/impresso-datalab-notebooks
Oct 2025 – Oct 2025
1 Month active
Languages Used
Python
Technical Skills
API integrationJupyter NotebookPandasdata analysisdata visualizationimage processing
Maud Ehrmann
PROFILE
Maud Ehrmann
During October 2025, Maud Ehrmann developed an embedding-based advertisement profile exploration feature in the impresso/impresso-datalab-notebooks repository. She integrated the Impresso API to retrieve content embeddings and consolidated Jupyter Notebook workflows, enabling users to analyze and retrieve similar advertisement profiles efficiently. Using Python, Pandas, and data visualization techniques, Maud implemented end-to-end embedding retrieval and designed search scenarios that streamline the discovery of relevant ad content. Her work focused on building a scalable, reusable search path for teams, improving data presentation, and laying the groundwork for future enhancements. The feature was delivered across five commits, reflecting thoughtful, incremental engineering progress.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
5Total
Bugs
0
Commits
5
Features
1
Lines of code
1,177
Activity Months1
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Work History
October 2025
5 Commits • 1 Features
Oct 1, 2025October 2025 performance: Delivered a feature to explore advertisement profiles via embedding-based search, integrated with the Impresso API to fetch content embeddings, and consolidated notebook-driven workflows to streamline ad-profile analysis and retrieval of similar items. The work progressed across five commits, from initial draft to expanded search scenarios and a final update. No major bugs were fixed this month; focus was on feature delivery and groundwork for scalable discovery. Business value: faster, more relevant ad-profile insights and a reusable embedding-based search path that can scale across notebooks and teams. Technical achievements: end-to-end embedding retrieval, API integration, and improved data presentation in notebooks.
5 Commits • 1 Features
Oct 1, 2025October 2025 performance: Delivered a feature to explore advertisement profiles via embedding-based search, integrated with the Impresso API to fetch content embeddings, and consolidated notebook-driven workflows to streamline ad-profile analysis and retrieval of similar items. The work progressed across five commits, from initial draft to expanded search scenarios and a final update. No major bugs were fixed this month; focus was on feature delivery and groundwork for scalable discovery. Business value: faster, more relevant ad-profile insights and a reusable embedding-based search path that can scale across notebooks and teams. Technical achievements: end-to-end embedding retrieval, API integration, and improved data presentation in notebooks.
October 2025
Activity
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture80.0%
Performance80.0%
AI Usage40.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
API integrationJupyter NotebookPandasdata analysisdata visualizationimage processingmachine learning
Repositories Contributed To
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