IBM/FailureSensorIQ
May 2025 – Oct 2025
3 Months active
Languages Used
JSONPythonMarkdownJupyter Notebook
Technical Skills
API IntegrationBenchmarkingBug FixingCode RefactoringData EngineeringData Structuring
christodoulos.constantinides@ibm.com
PROFILE
Christodoulos.constantinides@ibm.com
Contributed to IBM/FailureSensorIQ by developing a modular benchmarking framework and embedding-based failure prediction pipeline for industrial asset management. Leveraged Python, PyTorch, and Hugging Face Transformers to implement robust data preprocessing, model training scripts, and evaluation notebooks, supporting proactive maintenance and anomaly detection. Enhanced the evaluation pipeline with parallel processing, improved error handling for LLM availability, and integrated Hugging Face datasets with clear licensing and documentation. Refactored code for maintainability, optimized performance, and ensured reproducibility through configuration and dataset organization updates. Addressed reliability by fixing pipeline bugs and upgrading dependencies, resulting in more efficient, extensible, and reliable machine learning workflows.
Overall Statistics
Feature vs Bugs
80%Features
Repository Contributions
17Total
Bugs
2
Commits
17
Features
8
Lines of code
353,689
Activity Months3
Your Network
990 peopleSame Organization
@ibm.com988
Arun VMember
Aayush-PanchalMember
Aayush KumarMember
Abhijit DeokarMember
Abhina SreeMember
Abhishek KumarMember
Abhishek Kr SrivastavMember
Abraham Lara GranadosMember
Adapa ChathuryaMember
Shared Repositories
2
Shuxin LinMember
Shuxin-LinMember
Work History
October 2025
2 Commits • 1 Features
Oct 1, 2025October 2025 monthly summary for IBM/FailureSensorIQ focusing on LLM embeddings for failure prediction. Implemented data preprocessing, training scripts, and evaluation notebooks for an embedding-based failure-detection pipeline; updated dataset organization to support this feature. Two commits advancing embeddings and HF path. No major bug fixes recorded this month.
2 Commits • 1 Features
Oct 1, 2025October 2025 monthly summary for IBM/FailureSensorIQ focusing on LLM embeddings for failure prediction. Implemented data preprocessing, training scripts, and evaluation notebooks for an embedding-based failure-detection pipeline; updated dataset organization to support this feature. Two commits advancing embeddings and HF path. No major bug fixes recorded this month.
October 2025
June 2025
7 Commits • 4 Features
Jun 1, 2025June 2025 for IBM/FailureSensorIQ delivered business-critical improvements: robust LLM availability error handling with failure-mode testing; faster evaluation pipeline via 8x parallelism; configurable dataset size (sample/full) with logging improvements; Hugging Face dataset integration with notebook and CC BY 4.0 license; enhanced documentation on hardware (A100 80GB) and arXiv reference. These updates reduce risk, accelerate evaluation cycles, and improve data access, reproducibility, and compliance.
June 2025
7 Commits • 4 Features
Jun 1, 2025June 2025 for IBM/FailureSensorIQ delivered business-critical improvements: robust LLM availability error handling with failure-mode testing; faster evaluation pipeline via 8x parallelism; configurable dataset size (sample/full) with logging improvements; Hugging Face dataset integration with notebook and CC BY 4.0 license; enhanced documentation on hardware (A100 80GB) and arXiv reference. These updates reduce risk, accelerate evaluation cycles, and improve data access, reproducibility, and compliance.
May 2025
8 Commits • 3 Features
May 1, 2025May 2025 monthly highlights for IBM/FailureSensorIQ focused on delivering a modular benchmarking ecosystem and robust data assets for predictive maintenance, while stabilizing the evaluation pipeline and reinforcing reliability. The work emphasizes business value through better model evaluation, easier extensibility, and more maintainable data pipelines.
8 Commits • 3 Features
May 1, 2025May 2025 monthly highlights for IBM/FailureSensorIQ focused on delivering a modular benchmarking ecosystem and robust data assets for predictive maintenance, while stabilizing the evaluation pipeline and reinforcing reliability. The work emphasizes business value through better model evaluation, easier extensibility, and more maintainable data pipelines.
May 2025
Activity
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Quality Metrics
Correctness79.4%
Maintainability80.6%
Architecture77.0%
Performance75.4%
AI Usage29.4%
Skills & Technologies
Programming Languages
JSONJupyter NotebookMarkdownPython
Technical Skills
API IntegrationBenchmarkingBug FixingCode RefactoringConfiguration ManagementData AnalysisData EngineeringData LoadingData PreprocessingData StructuringDeep LearningDependency ManagementDevOpsDocumentationFile System Operations
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