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, this developer delivered an embedding-based advertisement profile exploration feature within the impresso/impresso-datalab-notebooks repository. They integrated the Impresso API to retrieve content embeddings and consolidated Jupyter Notebook workflows, enabling scalable analysis and retrieval of similar advertisement items. Using Python, Pandas, and data visualization techniques, they implemented end-to-end embedding retrieval and enhanced data presentation for ad-profile insights. The work progressed through multiple commits, evolving from an initial draft to expanded search scenarios and a final update. This feature established a reusable, notebook-driven search path, supporting faster, more relevant ad-profile discovery across teams without addressing major bug fixes.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
5Total
Bugs
0
Commits
5
Features
1
Lines of code
1,177
Activity Months1
Your Network
137 peopleShared Repositories
12
Andrianos MichailMember
maslionokMember
Cao VyMember
Emanuela BorosMember
Daniele GuidoMember
Gleb GlebMember
Gleb GlebMember
Gleb GlebMember
Gleb GlebMember
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
Loading activity data...
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
Overview of all repositories you've contributed to across your timeline