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
Developed online scoring automation for the comet-ml/opik repository by integrating LLM-as-Judge, enabling automated scoring flows triggered by trace ingestion. Designed and implemented new RESTful API endpoints and evaluator payload types in Java and SQL, supporting scalable, data-driven rule evaluation. Refactored provider logic to optimize LLM-based scoring performance and improved the end-to-end trace-to-score workflow. Addressed a critical bug in automation rule creation by correcting projectId handling, ensuring reliable rule setup. Enhanced maintainability and future scalability through comprehensive testing and code refactoring. The work focused on backend development, API design, and robust data processing to streamline and automate scoring operations.
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
97 peopleSame Organization
@comet.com20
Alex BezpalkoMember
aadereikoMember
amirzatcometMember
Andres CruzMember
andrii.dudarMember
Boris FeldMember
Collin CunninghamMember
Daniel DimenshteinMember
Douglas BlankMember
Shared Repositories
77
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
Overview of all repositories you've contributed to across your timeline