mlcommons/inference
Jan 2026 – Jan 2026
1 Month active
Languages Used
Python
Technical Skills
Data EngineeringDeep LearningMachine LearningModel EvaluationPython ProgrammingPython scripting
Linjian Ma
PROFILE
Linjian Ma
Developed and delivered the DLRMv3 reference model for large-scale recommendation systems in the mlcommons/inference repository, focusing on robust benchmarking and compliance. The work encompassed implementing data handling pipelines, model architecture, and evaluation metrics using Python and deep learning techniques. A compliance test was added to verify model accuracy in performance mode, featuring comprehensive logging and result comparison to ensure reproducibility and auditability. This approach established end-to-end benchmarking and testing coverage, enabling scalable and reliable performance analysis. The contributions emphasized data engineering, model evaluation, and testing, providing a foundation for reproducible, compliant benchmarking of recommendation models at scale.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
1
Lines of code
22,453
Activity Months1
Your Network
25 peopleShared Repositories
25
Work History
January 2026
2 Commits • 1 Features
Jan 1, 20262026-01 Monthly summary: Delivered the DLRMv3 reference model for large-scale recommendation systems in mlcommons/inference, together with a compliance test to verify accuracy in performance mode. Implemented data handling, model architecture, and evaluation metrics, plus logging and result comparisons to ensure reproducible benchmarking. This work strengthens benchmarking reliability, compliance readiness, and end-to-end verification of model behavior at scale. The efforts are underpinned by two key commits that establish the reference implementation and the compliance test, enabling scalable, auditable performance analysis.
2 Commits • 1 Features
Jan 1, 20262026-01 Monthly summary: Delivered the DLRMv3 reference model for large-scale recommendation systems in mlcommons/inference, together with a compliance test to verify accuracy in performance mode. Implemented data handling, model architecture, and evaluation metrics, plus logging and result comparisons to ensure reproducible benchmarking. This work strengthens benchmarking reliability, compliance readiness, and end-to-end verification of model behavior at scale. The efforts are underpinned by two key commits that establish the reference implementation and the compliance test, enabling scalable, auditable performance analysis.
January 2026
Activity
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Quality Metrics
Correctness90.0%
Maintainability80.0%
Architecture90.0%
Performance80.0%
AI Usage50.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
Data EngineeringDeep LearningMachine LearningModel EvaluationPython ProgrammingPython scriptingdata analysismachine learningtesting
Repositories Contributed To
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