{
  "id": 718630,
  "title": "How I solved the OOM and Kernel Crashes using a Hybrid GNN + Tree Architecture",
  "url": "/competitions/predict-ai-model-runtime/discussion/718630",
  "author_name": "Islam Ashraf",
  "post_date": "2026-07-03T19:51:41.395000",
  "votes": 0,
  "comment_count": 0,
  "views": 0,
  "content": "<p>Hi everyone,\nI wanted to share a clean, production-ready MLOps solution for the Google TPU Graphs dataset. Instead of using heavy GNNs that bottleneck hardware, I decoupled the architecture into a Shallow Frozen GNN Encoder and a downstream HistGradientBoostingRegressor.</p>\n<p>Key Highlights of my pipeline:</p>\n<p>Completely immune to Out-Of-Memory (OOM) errors.</p>\n<p>Custom Hybrid Loss (MSE + Pairwise Margin Ranking) aligned with Kendall's Tau.</p>\n<p>Fully defensive safe_collate pipeline to filter zero-config files.</p>\n<p>It achieved a stable 0.13018 Public Score / 0.11069 Private Score on late submission.</p>\n<p>Check out the complete, documented code here and let me know your thoughts: [<a href=\"url\" target=\"_blank\">https://www.kaggle.com/code/ashrafsalahedlin/mlops-pipeline-hybrid-gnn-histgradientboosting</a>]\nIf you find this MLOps approach insightful, I would highly appreciate your upvote!</p>",
  "messages": [
    {
      "id": 3487469,
      "postDate": "2026-07-03T19:51:41.397Z",
      "content": "<p>Hi everyone,\nI wanted to share a clean, production-ready MLOps solution for the Google TPU Graphs dataset. Instead of using heavy GNNs that bottleneck hardware, I decoupled the architecture into a Shallow Frozen GNN Encoder and a downstream HistGradientBoostingRegressor.</p>\n<p>Key Highlights of my pipeline:</p>\n<p>Completely immune to Out-Of-Memory (OOM) errors.</p>\n<p>Custom Hybrid Loss (MSE + Pairwise Margin Ranking) aligned with Kendall's Tau.</p>\n<p>Fully defensive safe_collate pipeline to filter zero-config files.</p>\n<p>It achieved a stable 0.13018 Public Score / 0.11069 Private Score on late submission.</p>\n<p>Check out the complete, documented code here and let me know your thoughts: [<a href=\"url\" target=\"_blank\">https://www.kaggle.com/code/ashrafsalahedlin/mlops-pipeline-hybrid-gnn-histgradientboosting</a>]\nIf you find this MLOps approach insightful, I would highly appreciate your upvote!</p>",
      "rawMarkdown": "Hi everyone,\nI wanted to share a clean, production-ready MLOps solution for the Google TPU Graphs dataset. Instead of using heavy GNNs that bottleneck hardware, I decoupled the architecture into a Shallow Frozen GNN Encoder and a downstream HistGradientBoostingRegressor.\n\nKey Highlights of my pipeline:\n\nCompletely immune to Out-Of-Memory (OOM) errors.\n\nCustom Hybrid Loss (MSE + Pairwise Margin Ranking) aligned with Kendall's Tau.\n\nFully defensive safe_collate pipeline to filter zero-config files.\n\nIt achieved a stable 0.13018 Public Score / 0.11069 Private Score on late submission.\n\nCheck out the complete, documented code here and let me know your thoughts: [[https://www.kaggle.com/code/ashrafsalahedlin/mlops-pipeline-hybrid-gnn-histgradientboosting](url)]\nIf you find this MLOps approach insightful, I would highly appreciate your upvote!"
    }
  ],
  "comments": [],
  "raw_markdown_by_id": {
    "3487469": "Hi everyone,\nI wanted to share a clean, production-ready MLOps solution for the Google TPU Graphs dataset. Instead of using heavy GNNs that bottleneck hardware, I decoupled the architecture into a Shallow Frozen GNN Encoder and a downstream HistGradientBoostingRegressor.\n\nKey Highlights of my pipeline:\n\nCompletely immune to Out-Of-Memory (OOM) errors.\n\nCustom Hybrid Loss (MSE + Pairwise Margin Ranking) aligned with Kendall's Tau.\n\nFully defensive safe_collate pipeline to filter zero-config files.\n\nIt achieved a stable 0.13018 Public Score / 0.11069 Private Score on late submission.\n\nCheck out the complete, documented code here and let me know your thoughts: [[https://www.kaggle.com/code/ashrafsalahedlin/mlops-pipeline-hybrid-gnn-histgradientboosting](url)]\nIf you find this MLOps approach insightful, I would highly appreciate your upvote!"
  }
}