comet-ml/opik
Jan 2025 – Jan 2025
1 Month active
Languages Used
JavaKotlinSQL
Technical Skills
API DesignAPI DevelopmentBackend DevelopmentBug FixingData ProcessingDatabase Design
Daniel Creão
PROFILE
Daniel Creão
During January 2025, Daniela developed an Online Scoring Automation system for the comet-ml/opik repository, integrating LLM-as-Judge to enable automated scoring flows triggered by trace ingestion. She designed and implemented new RESTful API endpoints and evaluator payload types in Java and SQL, refactoring the provider logic to support scalable, LLM-based scoring after trace receipt. Daniela also addressed a critical bug in automation rule creation by correcting projectId handling, ensuring reliable rule setup. Her work included comprehensive testing and enhancements to the end-to-end trace-to-score workflow, improving maintainability and laying a solid foundation for future data-driven rule evaluation and automation.
Overall Statistics
Feature vs Bugs
50%Features
Repository Contributions
4Total
Bugs
1
Commits
4
Features
1
Lines of code
3,845
Activity Months1
Your Network
86 people
Work History
January 2025
4 Commits • 1 Features
Jan 1, 2025January 2025 monthly summary for comet-ml/opik: Delivered Online Scoring Automation powered by LLM-as-Judge integration, enabling automated scoring flows triggered by trace ingestion, and fixed a critical projectId handling bug in automation rule creation. This work lays groundwork for scalable, data-driven rule evaluation and faster time-to-score, with a provider-refactor to optimize scoring performance.
4 Commits • 1 Features
Jan 1, 2025January 2025 monthly summary for comet-ml/opik: Delivered Online Scoring Automation powered by LLM-as-Judge integration, enabling automated scoring flows triggered by trace ingestion, and fixed a critical projectId handling bug in automation rule creation. This work lays groundwork for scalable, data-driven rule evaluation and faster time-to-score, with a provider-refactor to optimize scoring performance.
January 2025
Activity
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Quality Metrics
Correctness82.6%
Maintainability82.6%
Architecture82.6%
Performance70.0%
AI Usage50.0%
Skills & Technologies
Programming Languages
JavaKotlinSQL
Technical Skills
API DesignAPI DevelopmentBackend DevelopmentBug FixingData ProcessingDatabase DesignEvent-Driven ArchitectureJavaLLM IntegrationRESTful APIsRefactoringSQLTesting
Repositories Contributed To
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