DataDog/dd-trace-py
Oct 2025 – Oct 2025
1 Month active
Languages Used
Python
Technical Skills
Backend DevelopmentDistributed TracingObservabilityPythonfull stack developmentinstrumentation
Yakov Shapiro
PROFILE
Yakov Shapiro
Yakov Shapiro enhanced observability in the DataDog/dd-trace-py repository by developing new instrumentation for the Ray integration, focusing on tracing object storage operations through ray.put calls. Using Python and leveraging backend development and distributed tracing expertise, Yakov implemented logic to capture detailed telemetry around these operations, enabling end-to-end traceability for machine learning workloads. Additionally, Yakov introduced an AI Observability spans flag, allowing the system to differentiate AI-related spans for accurate billing. This work addressed the need for precise usage tracking and billing differentiation in AI observability, demonstrating depth in both instrumentation and the integration of observability features into complex distributed systems.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
1
Lines of code
223
Activity Months1
Your Network
937 people
Work History
October 2025
2 Commits • 1 Features
Oct 1, 20252025-10 monthly summary for DataDog/dd-trace-py: Ray integration observability enhancements with AI Observability billing differentiation. Implemented instrumentation around ray.put calls to trace object storage operations and introduced an AI Observability spans flag to classify AI-related spans for correct billing. This work strengthens observability, billing correctness, and data-driven decisions for ML workloads.
2 Commits • 1 Features
Oct 1, 20252025-10 monthly summary for DataDog/dd-trace-py: Ray integration observability enhancements with AI Observability billing differentiation. Implemented instrumentation around ray.put calls to trace object storage operations and introduced an AI Observability spans flag to classify AI-related spans for correct billing. This work strengthens observability, billing correctness, and data-driven decisions for ML workloads.
October 2025
Activity
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Quality Metrics
Correctness100.0%
Maintainability90.0%
Architecture100.0%
Performance90.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
Backend DevelopmentDistributed TracingObservabilityPythonfull stack developmentinstrumentationtracing
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