Internship Diary Entry: April 22, 2026
Role: AI Engineer — SynerSense Project: AnanaCare Visualization Layer (Embedding Explorer Integration) Hours Worked: 8
Work Summary
Today’s work focused on building a standalone embedding visualization system by integrating the TensorFlow Embedding Projector into the AnanaCare stack.
The main goal was to create a clean, production-ready interface for exploring high-dimensional embeddings (regions, diseases, conditions, demographics) without relying on experimental or fragmented setups.
I started by cloning the official projector repository and extracting only the required assets into a local public/ directory. Instead of maintaining multiple configs, I designed a single unified configuration file (projector_config.json) that loads all four embedding categories. This simplifies both maintenance and extensibility.
On the backend side, I implemented a lightweight FastAPI server (main.py) to serve static assets and automatically generate required TSV files if they are missing. This ensures the visualization system is self-healing and reduces manual setup.
On the frontend, I updated index.html to point to the new unified config instead of the default demo configuration. This connected the UI directly to your project’s real data.
A key refinement was in metadata handling. Previously, metadata included full file paths, which made the system less portable. I updated the pipeline (to_standalone.py) so that metadata now stores only image_id, making it cleaner and environment-independent.
Finally, I validated the full pipeline locally. The UI successfully loaded at http://localhost:8002, all four embedding spaces were visible, and interactions like switching between tensors and using dimensionality reduction techniques (UMAP, t-SNE, PCA) worked as expected.
Key Technical Achievements
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Embedding Visualization Integration: Successfully embedded the TensorFlow Embedding Projector into the project with a custom configuration.
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Unified Configuration System: Created a single
projector_config.jsonto manage multiple embedding spaces cleanly. -
Backend Automation: Built a FastAPI server that serves static assets and auto-generates TSV files when needed.
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Metadata Standardization: Simplified metadata to use only
image_id, improving portability and consistency. -
End-to-End Validation: Verified all embedding categories and dimensionality reduction modes in a live local environment.
Learnings & Insights
- Less config = more reliability: Consolidating multiple configs into one significantly reduces complexity.
- Portability matters early: Using
image_idinstead of file paths prevents environment-specific bugs later. - Visualization is a debugging tool: Seeing embeddings interactively provides insights that raw metrics cannot.
Challenges & Observations
- TSV regeneration initially stalled due to running the script from the wrong working directory. This highlights how fragile data pipelines can be when path assumptions are implicit.
- The projector works well, but scaling to very large embedding sets may require optimization (lazy loading or sampling).
Next Steps
- Regenerate TSV files from the correct working directory to align with the updated metadata format.
- Validate large-scale performance (thousands of embeddings).
- Optionally integrate this visualization into the main dashboard instead of running it as a standalone tool.
- Add filtering/search capabilities for better exploration of embeddings.
Today’s work adds a powerful visual analysis layer to the system, turning raw embeddings into something interpretable and actionable, which is a key step toward making the platform more usable for real-world ML workflows.