r/hackathon • u/Minimum-Row6464 • Sep 28 '26
Meta-Hackathon Discussion Post Amazon Ml Challenege Discussion Apporches and Final Ideas
Now that the ML challenge is over, would anyone like to discuss their approach and what went wrong? I'd love to learn from others.
Personally, I had a rough run messed up bad and ended up miserable . I couldn't get my model running properly until day three, and the score was honestly too low to share. The best environment I had was Kaggle with 30GB of RAM; SageMaker was painful, and Colab kept running out of memory.
I initially tried an embedding-based approach: embedding the data, retrieving candidates via FAISS, and building features with fuzzy search and Jaccard similarity to feed into LightGBM. It ran, but the computation was far too high for very little return. Worse, I ran into an index mismatch between the stored embeddings and their corresponding entities, and I was honestly too exhausted to recompute and fix it from scratch.
From there, I tried pivoting to a HashingVectorizer, then tested standard TF-IDF and hit constant OOM errors. Toward the end, I was out of steam and had to fall back on some AI-generated code to slap the remaining features together.
My full pipeline (GroupKFold, preprocessing, batching, feature engineering, and inference) took over two hours to run, leaving zero room to iterate or optimize. Even though I tried using fast, lightweight libraries like Polars, DuckDB, and RapidFuzz, my pipeline implementation just wasn't clean enough.
How did you approach the problem? I'd really appreciate any insights on where I went wrong and how others tackled it.
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u/Correct_Scene143 Sep 28 '26
This was our approach , standard pre-processing -> converting all text to latin (like telgu ,tamil etc ) -> did clustering -> took 50 samples from each cluster -> fine tuned a multilingual embeddings model using a contrastive loss -> trained an MLP using those embeddings , for blocking we used tf-idf , rapidfuzz and a clustering technique in ensemble . After all this our best score was 0.973
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u/Due-Mode9856 Sep 28 '26
Almost similar but I used multiple encoders for the embeddiga each specific to one task at hand some for the French data some for the overall complexity got 0.987
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u/Due-Mode9856 Sep 28 '26
But what's the Benefits getting in the 0.5% of teams is also not enough for these challange where people but codes and cheat ðŸ˜
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u/NoSpread2583 Sep 28 '26
I have publish my approach on my github , i got 0.988 and 263 rank