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Leaderboard6 min read

Amazon ML Challenge 2026: Final Leaderboard Explained

A comprehensive breakdown of the final recorded leaderboard standings, score distributions, top performing teams, and competitive tiering.

Amazon ML Challenge Explorer Editorial Team
Published: September 28, 2026

Overview of the Final Standings

The Amazon ML Challenge 2026 brought together over 10,900 student engineering teams and 36,000 participants across India. At the conclusion of the evaluation window, 4,334 teams recorded validated benchmark submissions on the public evaluation leaderboard.

The competition evaluated automated product feature extraction and multimodal machine learning models under strict F1-score evaluation protocols.

Key Benchmark Figures

  • Total Registered Teams: 10,942
  • Submitting Teams on Final Board: 4,334
  • Participating Institutions: 2,663 colleges & universities
  • Peak Top-Rank F1 Score: 0.985388
  • Median Competitive F1 Score: 0.684120
  • Cross-Institutional Teams: 3,993

The Top Tier: How the Podium Was Won

In the upper decile of the leaderboard (Top 100), the scoring margins were extraordinarily tight. Teams separated by mere thousandths of a decimal point in Micro F1 score often experienced rank swings of dozens of positions between successive submission checkpoints.

Key factors that differentiated top-tier submissions included: 1. Multimodal Ensembling: Combining text classification embeddings with computer vision feature extractors. 2. Post-Processing Calibration: Strict threshold optimization on precision-recall trade-offs to maximize the balanced macro/micro F1 metric. 3. Data Cleaning Pipelines: Resilient text normalizers capable of resolving noisy catalog descriptions and shorthand abbreviations.

Score Distribution Highlights

Unlike traditional Kaggle-style competitions where scores quickly plateau into uniform bands, the Amazon ML Challenge 2026 leaderboard exhibited distinct tiers:

  • Elite Tier (Score > 0.9000): Top ~2.5% of submitting teams.
  • Advanced Tier (0.7500 – 0.8999): Top ~22% of teams showing strong feature modeling.
  • Mid-Tier (0.5000 – 0.7499): The largest cluster (~48% of participants).
  • Baseline Tier (< 0.5000): Teams using preliminary heuristic or baseline model configurations.

Explore the complete final rankings on our Final Recorded Leaderboard and inspect individual team progressions via Movers & Climbers.

#Leaderboard#Final Standings#F1 Score#Competition Overview
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